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Found 154 papers, 64 papers with code
RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis Function Softmax
To address this problem, this paper introduces a novel Radial Basis Function (RBF) distances to replace the commonly used inner products in the softmax loss function, such that it can adaptively assign losses to regularize the intra-class and inter-class distances by reshaping the relative differences, and thus creating more representative prototypes of classes to improve optimization.
软件标识符的自然语言规范性研究(Research on the Natural Language Normalness of Software Identifiers)
“软件源代码的理解则是软件协同开发与维护的核心, 而源代码中占半数以上的标识符的理解则在软件理解中起到重要作用, 传统软件工程主要研究通过命名规范限制标识符的命名过程以构造更易理解和交流的标识符。本文则在梳理分析常见编程语言命名规范的基础上, 提出一种全新的标识符可理解性评价标准。具体而言, 本文首先总结梳理了常见主流编程语言中的命名规范并类比自然语言语素概念本文提出基于软件语素的标识符构成过程, 即标识符的构成可被视为软件语素的生成、排列和连接过程。在此基础上, 本文提出一种结合自然语料库的软件标识符规范性评价方法, 用来衡量软件标识符是否易于理解。最后, 本文通过源代码理解数据集和乇乩乴乨乵乢平台中开源项目对规范性指标进行了验证性实验, 结果表明本文提出的规范性分数能够很好衡量软件项目的可理解性。”
Evaluation of Machine Translation Based on Semantic Dependencies and Keywords
To achieve a comprehensive and in-depth evaluation of the semantic correctness of sentences, the experimental results show that the accuracy of the evaluation algorithm has been improved compared with similar methods, and it can more accurately measure the semantic correctness of machine translation.
Neural Semantic Parsing with Extremely Rich Symbolic Meaning Representations
We introduce a neural "taxonomical" semantic parser to utilize this new representation system of predicates, and compare it with a standard neural semantic parser trained on the traditional meaning representation format, employing a novel challenge set and evaluation metric for evaluation.
Residual Connections Harm Self-Supervised Abstract Feature Learning
We demonstrate that adding a weighting factor to decay the strength of identity shortcuts within residual networks substantially improves semantic feature learning in the state-of-the-art self-supervised masked autoencoding (MAE) paradigm.
UniSAR: Modeling User Transition Behaviors between Search and Recommendation
In this paper, we propose a framework named UniSAR that effectively models the different types of fine-grained behavior transitions for providing users a Unified Search And Recommendation service.
Gaining More Insight into Neural Semantic Parsing with Challenging Benchmarks
The Parallel Meaning Bank (PMB) serves as a corpus for semantic processing with a focus on semantic parsing and text generation.
To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes Process
The model, referred to as Neural Hawkes Process-based Open-App Motivation prediction model (NHP-OAM), employs a hierarchical transformer and a novel intensity function to encode multiple factors, and open-app motivation prediction layer to integrate time and user-specific information for predicting users' open-app motivations.
Large Language Models Enhanced Collaborative Filtering
In this paper, drawing inspiration from the in-context learning and chain of thought reasoning in LLMs, we propose the Large Language Models enhanced Collaborative Filtering (LLM-CF) framework, which distils the world knowledge and reasoning capabilities of LLMs into collaborative filtering.
IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History
This poses a new challenge for existing imitation learning approaches that can only utilize data from experienced experts.
An Item is Worth a Prompt: Versatile Image Editing with Disentangled Control
Building on the success of text-to-image diffusion models (DPMs), image editing is an important application to enable human interaction with AI-generated content.
A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications
However, since FL enables a continuous share of knowledge among participants with each communication round while not allowing local data to be accessed by other participants, FTL faces many unique challenges that are not present in TL.
AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks
Through conducting extensive experiments on a large scale of harmful and safe prompts, we validate the effectiveness of the proposed AutoDefense in improving the robustness against jailbreak attacks, while maintaining the performance at normal user request.
On the Decision-Making Abilities in Role-Playing using Large Language Models
Large language models (LLMs) are now increasingly utilized for role-playing tasks, especially in impersonating domain-specific experts, primarily through role-playing prompts.
RFI-DRUnet: Restoring dynamic spectra corrupted by radio frequency interference -- Application to pulsar observations
Conversely, this work proposes to tackle RFI mitigation as a joint detection and restoration that allows parts of the dynamic spectrum affected by RFI to be not only identified but also recovered.
The FinBen: An Holistic Financial Benchmark for Large Language Models
This along with the rapid development of LLMs, highlights the urgent need for a systematic financial evaluation benchmark for LLMs.
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval
Specifically, FairSync resolves the issue by moving it to the dual space, where a central node aggregates historical fairness data into a vector and distributes it to all servers.
Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English
We evaluate our model and existing LLMs using FLARE-ES, the first comprehensive bilingual evaluation benchmark with 21 datasets covering 9 tasks.
Learning Diverse Policies with Soft Self-Generated Guidance
However, existing methods often require these experiences to be successful and may overly exploit them, which can cause the agent to adopt suboptimal behaviors.
UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems
Reinforcement learning (RL) has gained traction for enhancing user long-term experiences in recommender systems by effectively exploring users' interests.
Curator: Efficient Indexing for Multi-Tenant Vector Databases
Vector databases have emerged as key enablers for bridging intelligent applications with unstructured data, providing generic search and management support for embedding vectors extracted from the raw unstructured data.
Leveraging Frequency Domain Learning in 3D Vessel Segmentation
In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models, which can reduce computational expenses while preserving global receptive fields within the network.
O-PRESS: Boosting OCT axial resolution with Prior guidance, Recurrence, and Equivariant Self-Supervision
Optical coherence tomography (OCT) is a noninvasive technology that enables real-time imaging of tissue microanatomies.
Trajectory-Oriented Policy Optimization with Sparse Rewards
The proposed algorithm undergoes evaluation across extensive discrete and continuous control tasks with sparse and misleading rewards.
Policy Optimization with Smooth Guidance Learned from State-Only Demonstrations
The sparsity of reward feedback remains a challenging problem in online deep reinforcement learning (DRL).
Adaptive trajectory-constrained exploration strategy for deep reinforcement learning
Deep reinforcement learning (DRL) faces significant challenges in addressing the hard-exploration problems in tasks with sparse or deceptive rewards and large state spaces.
Deciphering 'What' and 'Where' Visual Pathways from Spectral Clustering of Layer-Distributed Neural Representations
We present an approach for analyzing grouping information contained within a neural network's activations, permitting extraction of spatial layout and semantic segmentation from the behavior of large pre-trained vision models.
LLMs may Dominate Information Access: Neural Retrievers are Biased Towards LLM-Generated Texts
We refer to this category of biases in neural retrieval models towards the LLM-generated text as the \textbf{source bias}.
Integrating View Conditions for Image Synthesis
In the field of image processing, applying intricate semantic modifications within existing images remains an enduring challenge.
Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics
By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information.
Provably Robust Cost-Sensitive Learning via Randomized Smoothing
We focus on learning adversarially robust classifiers under a cost-sensitive scenario, where the potential harm of different classwise adversarial transformations is encoded in a binary cost matrix.
Transferable Availability Poisoning Attacks
We consider availability data poisoning attacks, where an adversary aims to degrade the overall test accuracy of a machine learning model by crafting small perturbations to its training data.
Generating Less Certain Adversarial Examples Improves Robust Generalization
Recent studies have shown that deep neural networks are vulnerable to adversarial examples.
Structural Adversarial Objectives for Self-Supervised Representation Learning
Within the framework of generative adversarial networks (GANs), we propose objectives that task the discriminator for self-supervised representation learning via additional structural modeling responsibilities.
PINF: Continuous Normalizing Flows for Physics-Constrained Deep Learning
The normalization constraint on probability density poses a significant challenge for solving the Fokker-Planck equation.
Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning
To obtain efficiency and effectiveness, this paper introduces a generative retrieval framework, namely SEATER, which learns SEmAntic Tree-structured item identifiERs via contrastive learning.
HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation
In real-world streaming recommender systems, user preferences often dynamically change over time (e. g., a user may have different preferences during weekdays and weekends).
LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops
RFL means that recommender system can only receive feedback on exposed items from users and update recommender models incrementally based on this feedback.
SciMRC: Multi-perspective Scientific Machine Reading Comprehension
However, the dataset has ignored the fact that different readers may have different levels of understanding of the text, and only includes single-perspective question-answer pairs, leading to a lack of consideration of different perspectives.
Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery
In this paper, we propose a method to construct a boundary that discriminates safe and unsafe states.
Discourse Representation Structure Parsing for Chinese
Previous work has predominantly focused on monolingual English semantic parsing.
KuaiSAR: A Unified Search And Recommendation Dataset
We believe this dataset will serve as a catalyst for innovative research and bridge the gap between academia and industry in understanding the S&R services in practical, real-world applications.
PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
This paper introduces PIXIU, a comprehensive framework including the first financial LLM based on fine-tuning LLaMA with instruction data, the first instruction data with 136K data samples to support the fine-tuning, and an evaluation benchmark with 5 tasks and 9 datasets.
Controllable Multi-Objective Re-ranking with Policy Hypernetworks
In this paper, we propose a framework called controllable multi-objective re-ranking (CMR) which incorporates a hypernetwork to generate parameters for a re-ranking model according to different preference weights.
When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation
In our paper, we propose a Search-Enhanced framework for the Sequential Recommendation (SESRec) that leverages users' search interests for recommendation, by disentangling similar and dissimilar representations within S&R behaviors.
Robust Image Ordinal Regression with Controllable Image Generation
Image ordinal regression has been mainly studied along the line of exploiting the order of categories.
Uncovering ChatGPT's Capabilities in Recommender Systems
The debut of ChatGPT has recently attracted the attention of the natural language processing (NLP) community and beyond.
A Lightweight Recurrent Learning Network for Sustainable Compressed Sensing
While the initial reconstruction sub-network has a hierarchical structure to progressively recover the image, reducing the number of parameters, the residual reconstruction sub-network facilitates recurrent residual feature extraction via recurrent learning to perform both feature fusion and deep reconstructions across different scales.
Transition System Representation of Boolean Control Networks
The first kind of representation is state-based, which converts a BCN into a TS with either distinct control or non-distinct control.
CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data
Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data.
Aggregated (Bi-)Simulation of Finite Valued Networks
Then the overall network can be approximated by the quotient systems of each blocks, which is called the aggregated simulation.
Reproduction number of SARS-CoV-2 Omicron variants, China, December 2022-January 2023
China adjusted the zero-COVID strategy in late 2022, triggering an unprecedented Omicron wave.
P-MMF: Provider Max-min Fairness Re-ranking in Recommender System
In this paper, we proposed an online re-ranking model named Provider Max-min Fairness Re-ranking (P-MMF) to tackle the problem.
Semi-Tensor Product of Hypermatrices with Application to Compound Hypermatrices
Some basic properties of the STP of matrices are extended to the STP of hypermatrices.
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension
Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversational interaction scenes.
Analysis of Discrete-Time Switched Linear Systems under Logic Dynamic Switchings
The control properties of discrete-time switched linear systems (SLS) with switching signals generated by logical dynamic systems are studied using the semi-tensor product (STP) approach.
PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds
To alleviate the cost of hyperparameter tuning and iterative pseudo labeling, we develop a population-based data augmentation framework for 3D detection, named AutoPseudoAugment.
Law Article-Enhanced Legal Case Matching: a Causal Learning Approach
We show that the framework is model-agnostic, and a number of legal case matching models can be applied as the underlying models.
ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension
Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text.
MICO: Selective Search with Mutual Information Co-training
In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit the search executed within one group or only a few groups.
Invariant and Dual Invariant Subspaces of $k$-valued Networks
Finally, the relationship between state invariant subspace and dual invariant subspace of a network is investigated.
Adversarial Attacks on ASR Systems: An Overview
With the development of hardware and algorithms, ASR(Automatic Speech Recognition) systems evolve a lot.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and Fusion
The key idea in FedKF is to let the server return the global knowledge to be fused with the local knowledge in each training round so that the local model can be regularized towards the global optima.
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices
Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP.
Feature Forgetting in Continual Representation Learning
To study the feature forgetting problem, we create a synthetic dataset to identify and visualize the prevalence of feature forgetting in neural networks.
Cross-Lingual Phrase Retrieval
In this paper, we propose XPR, a cross-lingual phrase retriever that extracts phrase representations from unlabeled example sentences.
Reinforcement Re-ranking with 2D Grid-based Recommendation Panels
Then, it defines \emph{the MDP discrete time steps as the ranks in the initial ranking list, and the actions as the prediction of the user-item preference and the selection of the slots}.
Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition
An extension to the pairwise neural ranking is also developed.
A Model-Agnostic Causal Learning Framework for Recommendation using Search Data
In this paper, we propose a model-agnostic framework named IV4Rec that can effectively decompose the embedding vectors into these two parts, hence enhancing recommendation results.
Combining Reinforcement Learning and Inverse Reinforcement Learning for Asset Allocation Recommendations
We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them.
Hidden Order of Boolean Networks
It is a common belief that the order of a Boolean network is mainly determined by its attractors, including fixed points and cycles.
A Divide-and-Merge Point Cloud Clustering Algorithm for LiDAR Panoptic Segmentation
The proposed algorithm is implemented with C++ and wrapped as a python function.
A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation
To our best knowledge, we are the first to attempt the point cloud panoptic segmentation with clustering algorithms.
Understanding Intrinsic Robustness Using Label Uncertainty
A fundamental question in adversarial machine learning is whether a robust classifier exists for a given task.
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection
These larger detection ranges require more efficient and accurate detection models.
To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels
3D object detection is vital for many robotics applications.
Practical Assessment of Generalization Performance Robustness for Deep Networks via Contrastive Examples
Incorporating with a set of randomized strategies for well-designed data transformations over the training set, ContRE adopts classification errors and Fisher ratios on the generated contrastive examples to assess and analyze the generalization performance of deep models in complement with a testing set.
Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification
Unsupervised object re-identification targets at learning discriminative representations for object retrieval without any annotations.
Optimization Variance: Exploring Generalization Properties of DNNs
Unlike the conventional wisdom in statistical learning theory, the test error of a deep neural network (DNN) often demonstrates double descent: as the model complexity increases, it first follows a classical U-shaped curve and then shows a second descent.
Multi-Grained Knowledge Distillation for Named Entity Recognition
To maximally assimilate knowledge into the student model, we propose a multi-grained distillation scheme, which integrates cross entropy involved in conditional random field (CRF) and fuzzy learning. To validate the effectiveness of our proposal, we conducted a comprehensive evaluation on five NER benchmarks, reporting cross-the-board performance gains relative to competing prior-arts.
DCAP: Deep Cross Attentional Product Network for User Response Prediction
Prior studies in predicting user response leveraged the feature interactions by enhancing feature vectors with products of features to model second-order or high-order cross features, either explicitly or implicitly.
ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition
On the one hand, multiple views across tasks possibly relate to each other under practical situations.
Self-distillation with Batch Knowledge Ensembling Improves ImageNet Classification
In this way, our BAKE framework achieves online knowledge ensembling across multiple samples with only a single network.
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT
This paper presented GTA, a new framework for multivariate time series anomaly detection that involves automatically learning a graph structure, graph convolution, and modeling temporal dependency using a Transformer-based architecture.
Improved Estimation of Concentration Under $\ell_p$-Norm Distance Metrics Using Half Spaces
Mahloujifar et al. presented an empirical way to measure the concentration of a data distribution using samples, and employed it to find lower bounds on intrinsic robustness for several benchmark datasets.
Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond
Then, to understand the interpretation results, we also survey the performance metrics for evaluating interpretation algorithms.
Multi-Task Time Series Forecasting With Shared Attention
Time series forecasting is a key component in many industrial and business decision processes and recurrent neural network (RNN) based models have achieved impressive progress on various time series forecasting tasks.
Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction
Intuitively, the potential crowd flow of the new coming site can be implied by exploring the nearby sites.
Model information as an analysis tool in deep learning
We demonstrate using model information as a general analysis tool to gain insight into problems that arise in deep learning.
Information distance for neural network functions
We provide a practical distance measure in the space of functions parameterized by neural networks.
Guidance Module Network for Video Captioning
In this paper, we present a novel architecture which introduces a guidance module to encourage the encoder-decoder model to generate words related to the past and future words in a caption.
Using Enhanced Gaussian Cross-Entropy in Imitation Learning to Digging the First Diamond in Minecraft
Although state-ofthe-art reinforcement learning (RL) systems has led to breakthroughs in many difficult tasks, the sample inefficiency of standard reinforcement learning methods still precludes their application to more extremely complex tasks.
Self-Supervised Visual Representation Learning from Hierarchical Grouping
We create a framework for bootstrapping visual representation learning from a primitive visual grouping capability.
Semi-supervised Autoencoding Projective Dependency Parsing
We describe two end-to-end autoencoding models for semi-supervised graph-based projective dependency parsing.
Cross-Lingual Document Retrieval with Smooth Learning
Cross-lingual document search is an information retrieval task in which the queries' language differs from the documents' language.
Measuring Information Transfer in Neural Networks
We propose a practical measure of the generalizable information in a neural network model based on prequential coding, which we term Information Transfer ($L_{IT}$).
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient
Deep Q-learning algorithms often suffer from poor gradient estimations with an excessive variance, resulting in unstable training and poor sampling efficiency.
Semi-supervised Parsing with a Variational Autoencoding Parser
We propose an end-to-end variational autoencoding parsing (VAP) model for semi-supervised graph-based projective dependency parsing.
Rethink the Connections among Generalization, Memorization and the Spectral Bias of DNNs
Over-parameterized deep neural networks (DNNs) with sufficient capacity to memorize random noise can achieve excellent generalization performance, challenging the bias-variance trade-off in classical learning theory.
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models
Starting with Gilmer et al. (2018), several works have demonstrated the inevitability of adversarial examples based on different assumptions about the underlying input probability space.
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization
We develop a notion of representation vulnerability that captures the maximum change of mutual information between the input and output distributions, under the worst-case input perturbation.
Label-guided Learning for Text Classification
In this study, we propose a label-guided learning framework LguidedLearn for text representation and classification.
Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellers
An electroencephalogram (EEG) based brain-computer interface (BCI) speller allows a user to input text to a computer by thought.
Tensor Graph Convolutional Networks for Text Classification
A new framework TensorGCN (tensor graph convolutional networks), is presented for this task.
Empirical Studies on the Properties of Linear Regions in Deep Neural Networks
A deep neural network (DNN) with piecewise linear activations can partition the input space into numerous small linear regions, where different linear functions are fitted.
Learn to Segment Retinal Lesions and Beyond
This paper attacks the three challenges in the context of diabetic retinopathy (DR) grading.
Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs
Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs).
ACE -- An Anomaly Contribution Explainer for Cyber-Security Applications
In this paper, we introduce Anomaly Contribution Explainer or ACE, a tool to explain security anomaly detection models in terms of the model features through a regression framework, and its variant, ACE-KL, which highlights the important anomaly contributors.
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces
Learning a good substitute model is critical to the success of these attacks, but it requires a large number of queries to the target model.
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
We propose a novel adaptive empirical Bayesian method for sparse deep learning, where the sparsity is ensured via a class of self-adaptive spike-and-slab priors.
SegSort: Segmentation by Discriminative Sorting of Segments
The proposed SegSort further produces an interpretable result, as each choice of label can be easily understood from the retrieved nearest segments.
Ranked #10 on
Unsupervised Semantic Segmentation
on PASCAL VOC 2012 val
(using extra training data)
Low-cost LIDAR based Vehicle Pose Estimation and Tracking
Based on our previous optimization/criteria-based L-Shape fitting algorithm, we here propose a data-driven and model-based method for robust vehicle segmentation and tracking.
Language-independent Cross-lingual Contextual Representations
Contextual representation models like BERT have achieved state-of-the-art performance on a diverse range of NLP tasks.
Learning Conceptual-Contextual Embeddings for Medical Text
External knowledge is often useful for natural language understanding tasks.
The iMaterialist Fashion Attribute Dataset
The dataset is constructed from over one million fashion images with a label space that includes 8 groups of 228 fine-grained attributes in total.
Sentiment Tagging with Partial Labels using Modular Architectures
Many NLP learning tasks can be decomposed into several distinct sub-tasks, each associated with a partial label.
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
Many recent works have shown that adversarial examples that fool classifiers can be found by minimally perturbing a normal input.
Joint Learning of Neural Networks via Iterative Reweighted Least Squares
We formulate this problem as joint learning of multiple copies of the same network architecture and enforce the network weights to be shared across these networks.
P2SGrad: Refined Gradients for Optimizing Deep Face Models
Cosine-based softmax losses significantly improve the performance of deep face recognition networks.
AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations
Our results show that training deep neural networks with the AdaCos loss is stable and able to achieve high face recognition accuracy.
Ranked #6 on
Face Verification
on MegaFace
Bayesian Deep Learning via Stochastic Gradient MCMC with a Stochastic Approximation Adaptation
We propose a robust Bayesian deep learning algorithm to infer complex posteriors with latent variables.
Normalized Diversification
We introduce the concept of normalized diversity which force the model to preserve the normalized pairwise distance between the sparse samples from a latent parametric distribution and their corresponding high-dimensional outputs.
Conditional Image Generation
Generative Adversarial Network
+2
On the Vulnerability of CNN Classifiers in EEG-Based BCIs
Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering.
DeeperLab: Single-Shot Image Parser
We present a single-shot, bottom-up approach for whole image parsing.
Ranked #32 on
Panoptic Segmentation
on Cityscapes val
Delta Embedding Learning
Evaluation also confirms the tuned word embeddings have better semantic properties.
Tidal surface states as fingerprints of non-Hermitian nodal knot metals
Non-Hermitian nodal knot metals (NKMs) contains intricate complex-valued energy bands gives rise to knotted exceptional loops and new topological surface states.
Mesoscale and Nanoscale Physics Materials Science Other Condensed Matter Mathematical Physics Mathematical Physics Quantum Physics
Cost-Sensitive Robustness against Adversarial Examples
Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations.
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
We propose a primal-dual based framework for analyzing the global optimality of nonconvex low-rank matrix recovery.
Learning One-hidden-layer ReLU Networks via Gradient Descent
We study the problem of learning one-hidden-layer neural networks with Rectified Linear Unit (ReLU) activation function, where the inputs are sampled from standard Gaussian distribution and the outputs are generated from a noisy teacher network.
Multi-UAV Cooperative Trajectory for Servicing Dynamic Demands and Charging Battery
To our best knowledge, this paper is the first to design and analyze cooperative path planning algorithms of a large UAV swarm for optimally servicing many spatial locations, where ground users' demands are released dynamically in the long time horizon.
Networking and Internet Architecture
OMG - Emotion Challenge Solution
This short paper describes our solution to the 2018 IEEE World Congress on Computational Intelligence One-Minute Gradual-Emotional Behavior Challenge, whose goal was to estimate continuous arousal and valence values from short videos.
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
This work proposes an algorithm, called NetAdapt, that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget.
Entanglement-guided architectures of machine learning by quantum tensor network
In this work, we implement simple numerical experiments, related to pattern/images classification, in which we represent the classifiers by many-qubit quantum states written in the matrix product states (MPS).
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow
We revisit the inductive matrix completion problem that aims to recover a rank-$r$ matrix with ambient dimension $d$ given $n$ features as the side prior information.
Medical Exam Question Answering with Large-scale Reading Comprehension
Reading and understanding text is one important component in computer aided diagnosis in clinical medicine, also being a major research problem in the field of NLP.
Range Loss for Deep Face Recognition With Long-Tailed Training Data
Unlike these work, this paper investigated how long-tailed data impact the training of face CNNs and develop a novel loss function, called range loss, to effectively utilize the tailed data in training process.
Semi-supervised Structured Prediction with Neural CRF Autoencoder
In this paper we propose an end-to-end neural CRF autoencoder (NCRF-AE) model for semi-supervised learning of sequential structured prediction problems.
Automatic Spatially-aware Fashion Concept Discovery
This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites.
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery
We propose a generic framework based on a new stochastic variance-reduced gradient descent algorithm for accelerating nonconvex low-rank matrix recovery.
PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings
Our proposed system consists of two subsystems and one regression model for predicting STS scores.
Learning Unified Embedding for Apparel Recognition
To ease the training difficulty, a novel learning scheme is proposed by using the output from specialized models as learning targets so that L2 loss can be used instead of triplet loss.
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption
We consider the robust phase retrieval problem of recovering the unknown signal from the magnitude-only measurements, where the measurements can be contaminated by both sparse arbitrary corruption and bounded random noise.
A Unified Framework for Low-Rank plus Sparse Matrix Recovery
We propose a unified framework to solve general low-rank plus sparse matrix recovery problems based on matrix factorization, which covers a broad family of objective functions satisfying the restricted strong convexity and smoothness conditions.
A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery
We propose a generic framework based on a new stochastic variance-reduced gradient descent algorithm for accelerating nonconvex low-rank matrix recovery.
Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements
And in the noiseless setting, our algorithm is guaranteed to linearly converge to the unknown low-rank matrix and achieves exact recovery with optimal sample complexity.
Range Loss for Deep Face Recognition with Long-tail
Convolutional neural networks have achieved great improvement on face recognition in recent years because of its extraordinary ability in learning discriminative features of people with different identities.
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition
We further show that applying deep residual learning can boost the convergence speed of our novel deep recurret convolutional networks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Composing Music with Grammar Argumented Neural Networks and Note-Level Encoding
Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for artificial intelligence research.
A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation
In the general case with noisy observations, we show that our algorithm is guaranteed to linearly converge to the unknown low-rank matrix up to minimax optimal statistical error, provided an appropriate initial estimator.
Understanding Satirical Articles Using Common-Sense
Automatic satire detection is a subtle text classification task, for machines and at times, even for humans.
Rademacher Complexity of the Restricted Boltzmann Machine
Boltzmann machine, as a fundamental construction block of deep belief network and deep Boltzmann machines, is widely used in deep learning community and great success has been achieved.
