Search Results for author:
Found 247 papers, 112 papers with code
NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural Networks
In this paper, we propose novel semantic data augmentation methods, Novel Augmentation of New Node Attributes (NaNa), and Molecular Interactions and Geometric Upgrading (MiGu) to incorporate backbone chemical and side-chain biophysical information into protein classification tasks and a co-embedding residual learning framework.
Larimar: Large Language Models with Episodic Memory Control
Efficient and accurate updating of knowledge stored in Large Language Models (LLMs) is one of the most pressing research challenges today.
Model Reprogramming Outperforms Fine-tuning on Out-of-distribution Data in Text-Image Encoders
When evaluating the performance of a pre-trained model transferred to a downstream task, it is imperative to assess not only the in-distribution (ID) accuracy of the downstream model but also its capacity to generalize and identify out-of-distribution (OOD) samples.
Duwak: Dual Watermarks in Large Language Models
As large language models (LLM) are increasingly used for text generation tasks, it is critical to audit their usages, govern their applications, and mitigate their potential harms.
How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance
Despite algorithmic efforts to improve the minority group accuracy, a theoretical generalization analysis of ERM on individual groups remains elusive.
MENTOR: Multilingual tExt detectioN TOward leaRning by analogy
Text detection is frequently used in vision-based mobile robots when they need to interpret texts in their surroundings to perform a given task.
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations.
Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes
Large Language Models (LLMs) are becoming a prominent generative AI tool, where the user enters a query and the LLM generates an answer.
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models
In the realm of subject-driven text-to-image (T2I) generative models, recent developments like DreamBooth and BLIP-Diffusion have led to impressive results yet encounter limitations due to their intensive fine-tuning demands and substantial parameter requirements.
A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity
In this study, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu.
Training Nonlinear Transformers for Efficient In-Context Learning: A Theoretical Learning and Generalization Analysis
Despite the empirical success, the mechanics of how to train a Transformer to achieve ICL and the corresponding ICL capacity is mostly elusive due to the technical challenges of analyzing the nonconvex training problems resulting from the nonlinear self-attention and nonlinear activation in Transformers.
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark
In the evolving landscape of natural language processing (NLP), fine-tuning pre-trained Large Language Models (LLMs) with first-order (FO) optimizers like SGD and Adam has become standard.
It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition
Recent studies have successfully shown that large language models (LLMs) can be successfully used for generative error correction (GER) on top of the automatic speech recognition (ASR) output.
Audio-Visual Speech Recognition
Automatic Speech Recognition
+3
From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers
Building upon this insight, in this work, we propose a novel density loss that encourages higher activation sparsity (equivalently, lower activation density) in the pre-trained models.
DDI-CoCo: A Dataset For Understanding The Effect Of Color Contrast In Machine-Assisted Skin Disease Detection
We take another angle to investigate color contrast's impact, beyond skin tones, on malignancy detection in skin disease datasets: We hypothesize that in addition to skin tones, the color difference between the lesion area and skin also plays a role in malignancy detection performance of dermatology AI models.
Large Language Models are Efficient Learners of Noise-Robust Speech Recognition
To this end, we propose to extract a language-space noise embedding from the N-best list to represent the noise conditions of source speech, which can promote the denoising process in GER.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective
Dataset distillation offers a potential means to enhance data efficiency in deep learning.
Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift
Diffusion models (DM) have become state-of-the-art generative models because of their capability to generate high-quality images from noises without adversarial training.
Conditional Modeling Based Automatic Video Summarization
The aim of video summarization is to shorten videos automatically while retaining the key information necessary to convey the overall story.
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $ε$-Greedy Exploration
This paper provides the first theoretical convergence and sample complexity analysis of the practical setting of DQNs with $\epsilon$-greedy policy.
Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
While efforts have been made to mitigate such problems, either by implementing a safety filter at the evaluation stage or by fine-tuning models to eliminate undesirable concepts or styles, the effectiveness of these safety measures in dealing with a wide range of prompts remains largely unexplored.
AutoVP: An Automated Visual Prompting Framework and Benchmark
To bridge this gap, we propose AutoVP, an end-to-end expandable framework for automating VP design choices, along with 12 downstream image-classification tasks that can serve as a holistic VP-performance benchmark.
Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
Optimizing large language models (LLMs) for downstream use cases often involves the customization of pre-trained LLMs through further fine-tuning.
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities.
HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models
We make our results publicly accessible for reproducible pipelines with released pre-trained models, thus providing a new evaluation paradigm for ASR error correction with LLMs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You Where
While image data starts to enjoy the simple-but-effective self-supervised learning scheme built upon masking and self-reconstruction objective thanks to the introduction of tokenization procedure and vision transformer backbone, convolutional neural networks as another important and widely-adopted architecture for image data, though having contrastive-learning techniques to drive the self-supervised learning, still face the difficulty of leveraging such straightforward and general masking operation to benefit their learning process significantly.
Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table Transformers
For machine learning with tabular data, Table Transformer (TabTransformer) is a state-of-the-art neural network model, while Differential Privacy (DP) is an essential component to ensure data privacy.
Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts
In this work, we propose Prompting4Debugging (P4D) as a debugging and red-teaming tool that automatically finds problematic prompts for diffusion models to test the reliability of a deployed safety mechanism.
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
To accomplish this challenging task, first, a spectral sensitivity map is introduced to characterize the generalization weaknesses of models in the frequency domain.
Robust Mixture-of-Expert Training for Convolutional Neural Networks
Since the lack of robustness has become one of the main hurdles for CNNs, in this paper we ask: How to adversarially robustify a CNN-based MoE model?
Lost In Translation: Generating Adversarial Examples Robust to Round-Trip Translation
Language Models today provide a high accuracy across a large number of downstream tasks.
Causal Video Summarizer for Video Exploration
Multi-modal video summarization has a video input and a text-based query input.
NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes
To address this challenge, we introduce NeuralFuse, a novel add-on module that addresses the accuracy-energy tradeoff in low-voltage regimes by learning input transformations to generate error-resistant data representations.
VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models
This paper presents a unified backdoor attack framework (VillanDiffusion) to expand the current scope of backdoor analysis for DMs.
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks
In deep learning, mixture-of-experts (MoE) activates one or few experts (sub-networks) on a per-sample or per-token basis, resulting in significant computation reduction.
Diagnostic Spatio-temporal Transformer with Faithful Encoding
This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex spatio-temporal (ST) dependency.
Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
However, supervised CL is prone to collapsing representations of subclasses within a class by not capturing all their features, and unsupervised CL may suppress harder class-relevant features by focusing on learning easy class-irrelevant features; both significantly compromise representation quality.
Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits
Variational quantum circuit (VQC) is a promising approach for implementing quantum neural networks on noisy intermediate-scale quantum (NISQ) devices.
Causalainer: Causal Explainer for Automatic Video Summarization
In this work, a Causal Explainer, dubbed Causalainer, is proposed to address this issue.
Virus2Vec: Viral Sequence Classification Using Machine Learning
Understanding the host-specificity of different families of viruses sheds light on the origin of, e. g., SARS-CoV-2, rabies, and other such zoonotic pathogens in humans.
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models
Formally, GREAT Score carries the physical meaning of a global statistic capturing a mean certified attack-proof perturbation level over all samples drawn from a generative model.
Overload: Latency Attacks on Object Detection for Edge Devices
Our method is based on a newly formulated optimization problem and a novel technique, called spatial attention, to increase the inference time of object detection.
Exploring the Benefits of Visual Prompting in Differential Privacy
Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation to downstream tasks by engineering a well-trained frozen source model.
Convex Bounds on the Softmax Function with Applications to Robustness Verification
The softmax function is a ubiquitous component at the output of neural networks and increasingly in intermediate layers as well.
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks
Using our framework, we present the first leaderboard, MultiRobustBench, for benchmarking multiattack evaluation which captures performance across attack types and attack strengths.
A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity
Based on a data model characterizing both label-relevant and label-irrelevant tokens, this paper provides the first theoretical analysis of training a shallow ViT, i. e., one self-attention layer followed by a two-layer perceptron, for a classification task.
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and storage costs.
Certified Interpretability Robustness for Class Activation Mapping
Interpreting machine learning models is challenging but crucial for ensuring the safety of deep networks in autonomous driving systems.
AI Maintenance: A Robustness Perspective
With the advancements in machine learning (ML) methods and compute resources, artificial intelligence (AI) empowered systems are becoming a prevailing technology.
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning
To this end, we reprogram an off-the-shelf pre-trained English language transformer and benchmark it on a set of protein physicochemical prediction tasks (secondary structure, stability, homology, stability) as well as on a biomedically relevant set of protein function prediction tasks (antimicrobial, toxicity, antibody affinity).
Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization
In this paper, we design and analyze stochastic inexact augmented Lagrangian methods (Stoc-iALM) to solve problems involving a nonconvex composite (i. e. smooth+nonsmooth) objective and nonconvex smooth functional constraints.
On Human Visual Contrast Sensitivity and Machine Vision Robustness: A Comparative Study
It is well established in neuroscience that color vision plays an essential part in the human visual perception system.
Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image Classification
In this paper, we provide 20, 000 non-trivial human annotations on popular datasets as a first step to bridge gap to studying how natural semantic spurious features affect image classification, as prior works often study datasets mixing low-level features due to limitations in accessing realistic datasets.
How to Backdoor Diffusion Models?
To gain a better understanding of the limitations and potential risks, this paper presents the first study on the robustness of diffusion models against backdoor attacks.
When Neural Networks Fail to Generalize? A Model Sensitivity Perspective
Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions.
NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration
With the advancement of deep learning technology, neural networks have demonstrated their excellent ability to provide accurate predictions in many tasks.
Understanding and Improving Visual Prompting: A Label-Mapping Perspective
As highlighted below, we show that when reprogramming an ImageNet-pretrained ResNet-18 to 13 target tasks, our method outperforms baselines by a substantial margin, e. g., 7. 9% and 6. 7% accuracy improvements in transfer learning to the target Flowers102 and CIFAR100 datasets.
Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise
Recently, quantum classifiers have been found to be vulnerable to adversarial attacks, in which quantum classifiers are deceived by imperceptible noises, leading to misclassification.
Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming
In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR).
Inference and Denoise: Causal Inference-based Neural Speech Enhancement
This study addresses the speech enhancement (SE) task within the causal inference paradigm by modeling the noise presence as an intervention.
An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization
Molecule optimization is an important problem in chemical discovery and has been approached using many techniques, including generative modeling, reinforcement learning, genetic algorithms, and much more.
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
In this work, we theoretically analyze the connection among cross-entropy loss, attack success rate, and clean accuracy in this setting.
Visual Prompting for Adversarial Robustness
In this work, we leverage visual prompting (VP) to improve adversarial robustness of a fixed, pre-trained model at testing time.
Rethinking Normalization Methods in Federated Learning
We also show that layer normalization is a better choice in FL which can mitigate the external covariate shift and improve the performance of the global model.
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data
Given a pretrained model, the representations of data synthesized from the Gaussian mixture are used to compare with our reference to infer the quality.
Reprogramming Pretrained Language Models for Antibody Sequence Infilling
Results on antibody design benchmarks show that our model on low-resourced antibody sequence dataset provides highly diverse CDR sequences, up to more than a two-fold increase of diversity over the baselines, without losing structural integrity and naturalness.
Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration
Neural network calibration is an essential task in deep learning to ensure consistency between the confidence of model prediction and the true correctness likelihood.
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
We then provide two robustness certification criteria: certified prediction and certified attack inefficacy for DPFL on both user and instance levels.
Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning
Empowered by the robust relation net built on SSL, we found that BEYOND outperforms baselines in terms of both detection ability and speed.
Active Sampling of Multiple Sources for Sequential Estimation
Additionally, each process $i\in\{1, \dots, K\}$ has a private parameter $\alpha_i$.
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
To address these challenges, in this paper, we introduce a VFL framework with multiple heads (VIM), which takes the separate contribution of each client into account, and enables an efficient decomposition of the VFL optimization objective to sub-objectives that can be iteratively tackled by the server and the clients on their own.
Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification
The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome -- millions of sequences and counting.
CARBEN: Composite Adversarial Robustness Benchmark
Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e. g., perturbations bounded in Lp ball.
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
Graph convolutional networks (GCNs) have recently achieved great empirical success in learning graph-structured data.
On Certifying and Improving Generalization to Unseen Domains
This highlights that the performance of DG methods on a few benchmark datasets may not be representative of their performance on unseen domains in the wild.
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
Certifiable robustness is a highly desirable property for adopting deep neural networks (DNNs) in safety-critical scenarios, but often demands tedious computations to establish.
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
Spurred by that, we propose distributed adversarial training (DAT), a large-batch adversarial training framework implemented over multiple machines.
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning.
Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression
In this work, we first put forth an end-to-end quantum neural network, TTN-VQC, which consists of a quantum tensor network based on a tensor-train network (TTN) for dimensionality reduction and a VQC for functional regression.
Learning Geometrically Disentangled Representations of Protein Folding Simulations
Massive molecular simulations of drug-target proteins have been used as a tool to understand disease mechanism and develop therapeutics.
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions
More and more investors and machine learning models rely on social media (e. g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements.
Evaluating the Adversarial Robustness for Fourier Neural Operators
In recent years, Machine-Learning (ML)-driven approaches have been widely used in scientific discovery domains.
Treatment Learning Causal Transformer for Noisy Image Classification
In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy by jointly estimating their treatment effects.
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing
This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance.
Towards Creativity Characterization of Generative Models via Group-based Subset Scanning
We propose group-based subset scanning to identify, quantify, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of the generative models.
Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning
In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models and can even learn general task-agnostic representations for efficient finetuning to downstream tasks.
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing
Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
Holistic Adversarial Robustness of Deep Learning Models
Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability.
Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations
We then propose generalized adversarial training (GAT) to extend model robustness from $\ell_{p}$-ball to composite semantic perturbations, such as the combination of Hue, Saturation, Brightness, Contrast, and Rotation.
Auto-Transfer: Learning to Route Transferrable Representations
Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labeled data can be difficult to obtain in many applications.
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited.
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics
To this end, we construct a network mapping $\phi$, converting a neural network $G_A$ to a directed line graph $G_B$ that is defined on those edges in $G_A$.
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
With the integrated framework, we achieve up to 6\% improvement on the standard accuracy and 17\% improvement on the robust accuracy.
Catastrophic Data Leakage in Vertical Federated Learning
We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).
Best Arm Identification in Contaminated Stochastic Bandits
Owing to the adversarial contamination of the rewards, each arm's mean is only partially identifiable.
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations
Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to its implications on a wide range of machine learning tasks such as model compression, generalization gap assessment, and adversarial attacks.
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines
To alleviate this issue, we propose a novel data augmentation scheme, FourierMix, that produces augmentations to improve the spectral coverage of the training data.
Pessimistic Model Selection for Offline Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has demonstrated great potentials in solving sequential decision making problems in many applications.
Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis
Basically, given only a small set of detectors that are learned to recognize some manually annotated attributes (i. e., the seen attributes), we aim to synthesize the detectors of novel attributes in a zero-shot learning manner.
Meta Adversarial Perturbations
A plethora of attack methods have been proposed to generate adversarial examples, among which the iterative methods have been demonstrated the ability to find a strong attack.
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination
Owing to the adversarial contamination of the rewards, each arm's mean is only partially identifiable.
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?
We show that AdvCL is able to enhance cross-task robustness transferability without loss of model accuracy and finetuning efficiency.
CAFE: Catastrophic Data Leakage in Vertical Federated Learning
We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).
How and When Adversarial Robustness Transfers in Knowledge Distillation?
Our comprehensive analysis shows several novel insights that (1) With KDIGA, students can preserve or even exceed the adversarial robustness of the teacher model, even when their models have fundamentally different architectures; (2) KDIGA enables robustness to transfer to pre-trained students, such as KD from an adversarially trained ResNet to a pre-trained ViT, without loss of clean accuracy; and (3) Our derived local linearity bounds for characterizing adversarial robustness in KD are consistent with the empirical results.
Robust Event Classification Using Imperfect Real-world PMU Data
Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e. g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers.
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks
Moreover, when the algorithm for training a pruned neural network is specified as an (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned weights in the hidden layer.
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition
In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system.
QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks
The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks.
Less is More: Dimension Reduction Finds On-Manifold Adversarial Examples in Hard-Label Attacks
It was recently shown in the gradient-level setting that regular adversarial examples leave the data manifold, while their on-manifold counterparts are in fact generalization errors.
Tactics on Refining Decision Boundary for Improving Certification-based Robust Training
In verification-based robust training, existing methods utilize relaxation based methods to bound the worst case performance of neural networks given certain perturbation.
Certified Robustness for Free in Differentially Private Federated Learning
Federated learning (FL) provides an efficient training paradigm to jointly train a global model leveraging data from distributed users.
Benchmarking Machine Learning Robustness in Covid-19 Spike Sequence Classification
The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 viral genome --- millions of sequences and counting.
Auto-Transfer: Learning to Route Transferable Representations
Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labelled data can be difficult to obtain in many applications.
How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited.
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.
Real-World Adversarial Examples involving Makeup Application
The Cycle-GAN is used to generate adversarial makeup, and the architecture of the victimized classifier is VGG 16.
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
Unsupervised domain adaptation (UDA) enables cross-domain learning without target domain labels by transferring knowledge from a labeled source domain whose distribution differs from that of the target.
MAML is a Noisy Contrastive Learner in Classification
Model-agnostic meta-learning (MAML) is one of the most popular and widely adopted meta-learning algorithms, achieving remarkable success in various learning problems.
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering.
Generalizing Adversarial Training to Composite Semantic Perturbations
We then propose generalized adversarial training (GAT) to extend model robustness from $\ell_{p}$ norm to composite semantic perturbations, such as Hue, Saturation, Brightness, Contrast, and Rotation.
On the Effectiveness of Poisoning against Unsupervised Domain Adaptation
However, the limited effect of poisoning is restricted to the setting where training and test data are from the same distribution.
Voice2Series: Reprogramming Acoustic Models for Time Series Classification
Learning to classify time series with limited data is a practical yet challenging problem.
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks
Our method exploits clipping and smoothing on model parameters to control the global model smoothness, which yields a sample-wise robustness certification on backdoors with limited magnitude.
Predicting Deep Neural Network Generalization with Perturbation Response Curves
However, despite these successes, the recent Predicting Generalization in Deep Learning (PGDL) NeurIPS 2020 competition suggests that there is a need for more robust and efficient measures of network generalization.
Simple Transparent Adversarial Examples
We propose two new aspects of adversarial image generation methods and evaluate them on the robustness of Google Cloud Vision API's optical character recognition service and object detection APIs deployed in real-world settings such as sightengine. com, picpurify. com, Google Cloud Vision API, and Microsoft Azure's Computer Vision API.
Vision Transformers are Robust Learners
Transformers, composed of multiple self-attention layers, hold strong promises toward a generic learning primitive applicable to different data modalities, including the recent breakthroughs in computer vision achieving state-of-the-art (SOTA) standard accuracy.
High-Robustness, Low-Transferability Fingerprinting of Neural Networks
This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks, featuring high-robustness to the base model against model pruning as well as low-transferability to unassociated models.
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained.
Gi and Pal Scores: Deep Neural Network Generalization Statistics
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of regression, classification, and control tasks.
Towards creativity characterization of generative models via group-based subset scanning
We propose group-based subset scanning to quantify, detect, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of generative models.
On the Adversarial Robustness of Vision Transformers
Following the success in advancing natural language processing and understanding, transformers are expected to bring revolutionary changes to computer vision.
Don't Forget to Sign the Gradients!
Engineering a top-notch deep learning model is an expensive procedure that involves collecting data, hiring human resources with expertise in machine learning, and providing high computational resources.
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples
It was recently shown in the gradient-level setting that regular adversarial examples leave the data manifold, while their on-manifold counterparts are in fact generalization errors.
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations
Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to its implications on a wide range of machine learning tasks such as model compression, generalization gap assessment, and adversarial attacks.
Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning
In this paper, we propose a framework of generating adversarial examples for unsupervised models and demonstrate novel applications to data augmentation.
Domain Adaptation for Learning Generator from Paired Few-Shot Data
We propose a Paired Few-shot GAN (PFS-GAN) model for learning generators with sufficient source data and a few target data.
Non-Singular Adversarial Robustness of Neural Networks
In this paper, we formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights.
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
Despite the generalization power of the meta-model, it remains elusive that how adversarial robustness can be maintained by MAML in few-shot learning.
Training a Resilient Q-Network against Observational Interference
Deep reinforcement learning (DRL) has demonstrated impressive performance in various gaming simulators and real-world applications.
Meta Federated Learning
Due to its distributed methodology alongside its privacy-preserving features, Federated Learning (FL) is vulnerable to training time adversarial attacks.
Fast Training of Provably Robust Neural Networks by SingleProp
Recent works have developed several methods of defending neural networks against adversarial attacks with certified guarantees.
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks.
Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks
The goal of LSC is to characterize and quantify language variations with respect to word meaning, to measure how distinct two language sources are (that is, people or language models).
Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records
With the successful adoption of machine learning on electronic health records (EHRs), numerous computational models have been deployed to address a variety of clinical problems.
Robust Text CAPTCHAs Using Adversarial Examples
At the second stage, we design and apply a highly transferable adversarial attack for text CAPTCHAs to better obstruct CAPTCHA solvers.
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks
Moreover, as the algorithm for training a sparse neural network is specified as (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned model weights in the hidden layer.
CAFE: Catastrophic Data Leakage in Federated Learning
In this paper, we revisit this defense premise and propose an advanced data leakage attack to efficiently recover batch data from the shared aggregated gradients.
ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Dynamics
Empowered by the disentangled latent space learning, the extrinsic latent embedding is successfully used for classification or property prediction of different drugs bound to a specific protein.
Self-Progressing Robust Training
Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems.
Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework
In this work, we focus on the study of stochastic zeroth-order (ZO) optimization which does not require first-order gradient information and uses only function evaluations.
Reprogramming Language Models for Molecular Representation Learning
Recent advancements in transfer learning have made it a promising approach for domain adaptation via transfer of learned representations.
SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change
Our results show evidence that the number of landmarks used for alignment has a directimpact on the predictive performance of the model.
How Robust are Randomized Smoothing based Defenses to Data Poisoning?
Moreover, our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods such as Gaussian data augmentation\cite{cohen2019certified}, MACER\cite{zhai2020macer}, and SmoothAdv\cite{salman2019provably} that achieve high certified adversarial robustness.
Optimizing Molecules using Efficient Queries from Property Evaluations
Machine learning based methods have shown potential for optimizing existing molecules with more desirable properties, a critical step towards accelerating new chemical discovery.
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
Testing on the Google Speech Commands Dataset, the proposed QCNN encoder attains a competitive accuracy of 95. 12% in a decentralized model, which is better than the previous architectures using centralized RNN models with convolutional features.
Ranked #1 on
Keyword Spotting
on Google Speech Commands
(10-keyword Speech Commands dataset metric)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Higher-Order Certification for Randomized Smoothing
We also provide a framework that generalizes the calculation for certification using higher-order information.
Optimizing Mode Connectivity via Neuron Alignment
Yet, current curve finding algorithms do not consider the influence of symmetry in the loss surface created by model weight permutations.
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases
When the training data are maliciously tampered, the predictions of the acquired deep neural network (DNN) can be manipulated by an adversary known as the Trojan attack (or poisoning backdoor attack).
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources
Current transfer learning methods are mainly based on finetuning a pretrained model with target-domain data.
BIG-bench Machine Learning
Diabetic Retinopathy Detection
+1
Proper Network Interpretability Helps Adversarial Robustness in Classification
Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability (namely, making network interpretation maps visually similar), or interpretability is itself susceptible to adversarial attacks.
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
In this paper, we provide a theoretically-grounded generalizability analysis of GNNs with one hidden layer for both regression and binary classification problems.
A Dynamical Systems Approach for Convergence of the Bayesian EM Algorithm
Out of the recent advances in systems and control (S\&C)-based analysis of optimization algorithms, not enough work has been specifically dedicated to machine learning (ML) algorithms and its applications.
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning
Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many signal processing and machine learning applications.
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e. g., high broad-spectrum potency and low toxicity.
DBA: Distributed Backdoor Attacks against Federated Learning
Compared to standard centralized backdoors, we show that DBA is substantially more persistent and stealthy against FL on diverse datasets such as finance and image data.
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
In this work, we propose to employ mode connectivity in loss landscapes to study the adversarial robustness of deep neural networks, and provide novel methods for improving this robustness.
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement
Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Hidden Cost of Randomized Smoothing
The fragility of modern machine learning models has drawn a considerable amount of attention from both academia and the public.
Defending against Backdoor Attack on Deep Neural Networks
Although deep neural networks (DNNs) have achieved a great success in various computer vision tasks, it is recently found that they are vulnerable to adversarial attacks.
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks
To overcome these limitations, we propose a general framework which leverages the greedy search algorithms and zeroth-order methods to obtain robust GNNs in a generic and an efficient manner.
Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning
Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e. g., autonomous navigation and continuous robot arm control.)
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks
Designing effective defense against adversarial attacks is a crucial topic as deep neural networks have been proliferated rapidly in many security-critical domains such as malware detection and self-driving cars.
Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent
Despite the great achievements of the modern deep neural networks (DNNs), the vulnerability/robustness of state-of-the-art DNNs raises security concerns in many application domains requiring high reliability.
Block Switching: A Stochastic Approach for Deep Learning Security
Recent study of adversarial attacks has revealed the vulnerability of modern deep learning models.
CAT: Customized Adversarial Training for Improved Robustness
Adversarial training has become one of the most effective methods for improving robustness of neural networks.
Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States
Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e. g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
Towards Verifying Robustness of Neural Networks Against Semantic Perturbations
Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task.
Adversarial T-shirt! Evading Person Detectors in A Physical World
To the best of our knowledge, this is the first work that models the effect of deformation for designing physical adversarial examples with respect to-rigid objects such as T-shirts.
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
Moreover, the same classifier yields the lack of a trade-off with respect to ideal distributions while yielding a trade-off when accuracy is measured with respect to the given (possibly biased) dataset.
SPROUT: Self-Progressing Robust Training
Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy and reliable machine learning systems.
Visual Interpretability Alone Helps Adversarial Robustness
Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability, and interpretability is itself susceptible to adversarial attacks.
Optimizing Loss Landscape Connectivity via Neuron Alignment
Empirically, this initialization is critical for efficiently learning a simple, planar, low-loss curve between networks that successfully generalizes.
Efficient Training of Robust and Verifiable Neural Networks
We propose that many common certified defenses can be viewed under a unified framework of regularization.
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness
The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing adversarial robustness against norm-ball bounded input perturbations.
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
We study the most practical problem setup for evaluating adversarial robustness of a machine learning system with limited access: the hard-label black-box attack setting for generating adversarial examples, where limited model queries are allowed and only the decision is provided to a queried data input.
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability.
hpGAT: High-order Proximity Informed Graph Attention Network
Graph neural networks (GNNs) have recently made remarkable breakthroughs in the paradigm of learning with graph-structured data.
Ranked #39 on
Node Classification
on Citeseer
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses
However, one critical drawback of current defenses is that the robustness enhancement is at the cost of noticeable performance degradation on legitimate data, e. g., large drop in test accuracy.
Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization
In this work, we demonstrate the promise of applying reinforcement learning (RL) to optimize NoC runtime performance.
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method
Robust machine learning is currently one of the most prominent topics which could potentially help shaping a future of advanced AI platforms that not only perform well in average cases but also in worst cases or adverse situations.
Variational Quantum Circuits for Deep Reinforcement Learning
To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep $Q$-value function for decision-making and policy-selection reinforcement learning with experience replay and target network.
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
Graph neural networks (GNNs) which apply the deep neural networks to graph data have achieved significant performance for the task of semi-supervised node classification.
Adversarial Attack Generation Empowered by Min-Max Optimization
In this paper, we show how a general framework of min-max optimization over multiple domains can be leveraged to advance the design of different types of adversarial attacks.
Model Agnostic Contrastive Explanations for Structured Data
Recently, a method [7] was proposed to generate contrastive explanations for differentiable models such as deep neural networks, where one has complete access to the model.
Leveraging Latent Features for Local Explanations
As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions of these models has become a hot research topic, both at the global and local level.
Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach
We study the problem of attacking machine learning models in the hard-label black-box setting, where no model information is revealed except that the attacker can make queries to probe the corresponding hard-label decisions.
signSGD via Zeroth-Order Oracle
Our study shows that ZO signSGD requires $\sqrt{d}$ times more iterations than signSGD, leading to a convergence rate of $O(\sqrt{d}/\sqrt{T})$ under mild conditions, where $d$ is the number of optimization variables, and $T$ is the number of iterations.
When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks
To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation.
Toward A Neuro-inspired Creative Decoder
Creativity, a process that generates novel and meaningful ideas, involves increased association between task-positive (control) and task-negative (default) networks in the human brain.
