Search Results for author:
Found 365 papers, 110 papers with code
Toward Knowledge-Enriched Conversational Recommendation Systems
Conversational Recommendation Systems recommend items through language based interactions with users. In order to generate naturalistic conversations and effectively utilize knowledge graphs (KGs) containing background information, we propose a novel Bag-of-Entities loss, which encourages the generated utterances to mention concepts related to the item being recommended, such as the genre or director of a movie.
Rare and Zero-shot Word Sense Disambiguation using Z-Reweighting
However, the imbalanced training dataset leads to poor performance on rare senses and zero-shot senses.
Speeding up Transformer Decoding via an Attention Refinement Network
Extensive experiments on ten WMT machine translation tasks show that the proposed model yields an average of 1. 35x faster (with almost no decrease in BLEU) over the state-of-the-art inference implementation.
Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange
Subsequently, we introduce a context-aware feature learning strategy, which encodes object patterns without relying on their specific context by aggregating object features across various scenes.
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm
Unlike previous work, which relies on a generative model or a pre-collected offline dataset enjoying good coverage of the deployment environment, we tackle robust RL via interactive data collection, where the learner interacts with the training environment only and refines the policy through trial and error.
Towards Better Generalization in Open-Domain Question Answering by Mitigating Context Memorization
In addition, it is still unclear how well an OpenQA model can transfer to completely new knowledge domains.
On the Benefits of Over-parameterization for Out-of-Distribution Generalization
We demonstrate that in this scenario, further increasing the model's parameterization can significantly reduce the OOD loss.
LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
Attempting to complement this deficiency, we investigate layerwise properties of LoRA on fine-tuning tasks and observe an uncommon skewness of weight norms across different layers.
DVMNet: Computing Relative Pose for Unseen Objects Beyond Hypotheses
Determining the relative pose of an object between two images is pivotal to the success of generalizable object pose estimation.
Do CLIPs Always Generalize Better than ImageNet Models?
The performance drops from the common to counter groups quantify the reliance of models on spurious features (i. e., backgrounds) to predict the animals.
Gradient based Feature Attribution in Explainable AI: A Technical Review
Due to the lack of a rigorous definition of explainable AI (XAI), a plethora of research related to explainability, interpretability, and transparency has been developed to explain and analyze the model from various perspectives.
Desigen: A Pipeline for Controllable Design Template Generation
In this paper, we present Desigen, an automatic template creation pipeline which generates background images as well as harmonious layout elements over the background.
Strengthening Multimodal Large Language Model with Bootstrapped Preference Optimization
To mitigate this issue, we propose Bootstrapped Preference Optimization (BPO), which conducts preference learning with datasets containing negative responses bootstrapped from the model itself.
Ranked #29 on
Visual Question Answering
on MM-Vet
Language-Driven Visual Consensus for Zero-Shot Semantic Segmentation
To mitigate these issues, we propose a Language-Driven Visual Consensus (LDVC) approach, fostering improved alignment of semantic and visual information. Specifically, we leverage class embeddings as anchors due to their discrete and abstract nature, steering vision features toward class embeddings.
OMH: Structured Sparsity via Optimally Matched Hierarchy for Unsupervised Semantic Segmentation
Our OMH yields better unsupervised segmentation performance compared to existing USS methods.
Strength Lies in Differences! Towards Effective Non-collaborative Dialogues via Tailored Strategy Planning
We investigate non-collaborative dialogue agents that must engage in tailored strategic planning for diverse users to secure a favorable agreement.
An Improved Analysis of Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling
Along this line, Freund et al. (2022) suggest that the modified Langevin algorithm with prior diffusion is able to converge dimension independently for strongly log-concave target distributions.
Popeye: A Unified Visual-Language Model for Multi-Source Ship Detection from Remote Sensing Imagery
Ship detection needs to identify ship locations from remote sensing (RS) scenes.
Edge Computing Enabled Real-Time Video Analysis via Adaptive Spatial-Temporal Semantic Filtering
This paper proposes a novel edge computing enabled real-time video analysis system for intelligent visual devices.
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards
Additionally, DPA models user preferences as directions (i. e., unit vectors) in the reward space to achieve user-dependent preference control.
EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion
In this paper, we propose to adopt textual entailment to find implicit entailment relations between CSKG nodes, to effectively densify the subgraph connecting nodes within the same conceptual class, which indicates a similar level of plausibility.
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
We also prove a lower bound to show that the additive dependence on $C$ is optimal.
Model-based Reinforcement Learning
reinforcement-learning
+1
A Theoretical Analysis of Nash Learning from Human Feedback under General KL-Regularized Preference
In this work, we provide theoretical insights for a recently proposed learning paradigm, Nash learning from human feedback (NLHF), which considered a general preference model and formulated the alignment process as a game between two competitive LLMs.
The Instinctive Bias: Spurious Images lead to Hallucination in MLLMs
In this paper, we identify a typical class of inputs that baffles MLLMs, which consist of images that are highly relevant but inconsistent with answers, causing MLLMs to suffer from hallucination.
PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs
To facilitate the evaluation of pruned subgraphs, we also propose a graph attention network (GAT) based module to reason with the subgraph data.
EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain
Multi-modal large language models (MLLMs) have demonstrated remarkable success in vision and visual-language tasks within the natural image domain.
General Flow as Foundation Affordance for Scalable Robot Learning
Our predicted flow offers actionable geometric and physics guidance, thus facilitating stable zero-shot skill transfer in real-world scenarios. We deploy our method with a policy based on closed-loop flow prediction.
The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness
Recent empirical and theoretical studies have established the generalization capabilities of large machine learning models that are trained to (approximately or exactly) fit noisy data.
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo
Specifically, DMC follows the reverse SDE of a diffusion process that transforms the target distribution to the standard Gaussian, utilizing a non-parametric score estimation.
MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance
The deployment of multimodal large language models (MLLMs) has brought forth a unique vulnerability: susceptibility to malicious attacks through visual inputs.
AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets
We explore the potential of enhancing LLM performance in astronomy-focused question-answering through targeted, continual pre-training.
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs).
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise
This means SGD with heavy-ball momentum is useful in the large-batch settings such as distributed machine learning or federated learning, where a smaller number of iterations can significantly reduce the number of communication rounds, leading to acceleration in practice.
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint
This includes an iterative version of the Direct Preference Optimization (DPO) algorithm for online settings, and a multi-step rejection sampling strategy for offline scenarios.
An Incremental Unified Framework for Small Defect Inspection
Artificial Intelligence (AI)-driven defect inspection is pivotal in industrial manufacturing.
Improving Medical Report Generation with Adapter Tuning and Knowledge Enhancement in Vision-Language Foundation Models
Medical report generation demands automatic creation of coherent and precise descriptions for medical images.
DiffusionPCR: Diffusion Models for Robust Multi-Step Point Cloud Registration
We propose formulating PCR as a denoising diffusion probabilistic process, mapping noisy transformations to the ground truth.
Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning
In this study, we are interested in imbuing robots with the capability of physically-grounded task planning.
Beyond Pixels: Exploring Human-Readable SVG Generation for Simple Images with Vision Language Models
Our method focuses on producing SVGs that are both accurate and simple, aligning with human readability and understanding.
R-Tuning: Teaching Large Language Models to Refuse Unknown Questions
This approach is formalized by first identifying the knowledge gap between parametric knowledge and the instruction tuning data.
Plum: Prompt Learning using Metaheuristic
Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models.
PerceptionGPT: Effectively Fusing Visual Perception into LLM
In this paper, we present a novel end-to-end framework named PerceptionGPT, which efficiently and effectively equips the VLLMs with visual perception abilities by leveraging the representation power of LLMs' token embedding.
CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer
Reconstructing personalized animatable head avatars has significant implications in the fields of AR/VR.
Mesh Neural Cellular Automata
We propose Mesh Neural Cellular Automata (MeshNCA), a method for directly synthesizing dynamic textures on 3D meshes without requiring any UV maps.
Corruption-Robust Offline Reinforcement Learning with General Function Approximation
Notably, under the assumption of single policy coverage and the knowledge of $\zeta$, our proposed algorithm achieves a suboptimality bound that is worsened by an additive factor of $\mathcal{O}(\zeta (C(\widehat{\mathcal{F}},\mu)n)^{-1})$ due to the corruption.
Towards Robust Offline Reinforcement Learning under Diverse Data Corruption
Offline reinforcement learning (RL) presents a promising approach for learning reinforced policies from offline datasets without the need for costly or unsafe interactions with the environment.
Consistent123: Improve Consistency for One Image to 3D Object Synthesis
Large image diffusion models enable novel view synthesis with high quality and excellent zero-shot capability.
3D-Aware Hypothesis & Verification for Generalizable Relative Object Pose Estimation
Our goal then is to estimate the relative object pose between this reference view and a query image that depicts the object in a different pose.
Spurious Feature Diversification Improves Out-of-distribution Generalization
Contrary to the conventional wisdom that focuses on learning invariant features for better OOD performance, our findings suggest that incorporating a large number of diverse spurious features weakens their individual contributions, leading to improved overall OOD generalization performance.
May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability
With this paper, we investigate if free-form conversations can enhance users' comprehension of static explanations, improve acceptance and trust in the explanation methods, and facilitate human-AI collaboration.
MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning
In this paper, we present the first approach that addresses the inherent ambiguities present in pseudo labels by introducing an Evidential Deep Learning (EDL) based uncertainty estimation framework.
Mitigating the Alignment Tax of RLHF
Building on the analysis and the observation that averaging different layers of the transformer leads to significantly different reward-tax trade-offs, we propose Adaptive Model Averaging (AMA) to adaptively find various combination ratios of model layers.
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge Graphs
To perform effective learning on the large-scale UniKG, two key measures are taken, including (i) the semantic alignment strategy for multi-attribute entities, which projects the feature description of multi-attribute nodes into a common embedding space to facilitate node aggregation in a large receptive field; (ii) proposing a novel plug-and-play anisotropy propagation module (APM) to learn effective multi-hop anisotropy propagation kernels, which extends methods of large-scale homogeneous graphs to heterogeneous graphs.
Integrated Robotics Networks with Co-optimization of Drone Placement and Air-Ground Communications
Terrestrial robots, i. e., unmanned ground vehicles (UGVs), and aerial robots, i. e., unmanned aerial vehicles (UAVs), operate in separate spaces.
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning
Our proposed method, COPS (unCertainty based OPtimal Sub-sampling), is designed to minimize the expected loss of a model trained on subsampled data.
Self-Reference Deep Adaptive Curve Estimation for Low-Light Image Enhancement
In this paper, we propose a 2-stage low-light image enhancement method called Self-Reference Deep Adaptive Curve Estimation (Self-DACE).
Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learning
In this case, salient objects are typically non-camouflaged, and camouflaged objects are usually not salient.
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models
As the number of available models and specialized tasks keeps growing, the job of general finetuning becomes highly nontrivial.
Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning
Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in the same domains, which are usually infeasible for realistic applications.
Structure-Sensitive Graph Dictionary Embedding for Graph Classification
Instead of a plain use of a base graph dictionary, we propose the variational graph dictionary adaptation (VGDA) to generate a personalized dictionary (named adapted graph dictionary) for catering to each input graph.
A Universal Semantic-Geometric Representation for Robotic Manipulation
To this end, we present $\textbf{Semantic-Geometric Representation} (\textbf{SGR})$, a universal perception module for robotics that leverages the rich semantic information of large-scale pre-trained 2D models and inherits the merits of 3D spatial reasoning.
Customizing General-Purpose Foundation Models for Medical Report Generation
In this work, we propose customizing off-the-shelf general-purpose large-scale pre-trained models, i. e., foundation models (FMs), in computer vision and natural language processing with a specific focus on medical report generation.
Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models Memories
Pre-trained language models (PLMs) demonstrate excellent abilities to understand texts in the generic domain while struggling in a specific domain.
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
In this paper, we study out-of-distribution (OOD) generalization of offline GCRL both theoretically and empirically to identify factors that are important.
InNeRF360: Text-Guided 3D-Consistent Object Inpainting on 360-degree Neural Radiance Fields
We propose InNeRF360, an automatic system that accurately removes text-specified objects from 360-degree Neural Radiance Fields (NeRF).
DetGPT: Detect What You Need via Reasoning
Overall, our proposed paradigm and DetGPT demonstrate the potential for more sophisticated and intuitive interactions between humans and machines.
Effective Bilevel Optimization via Minimax Reformulation
To address this issue, we propose a reformulation of bilevel optimization as a minimax problem, effectively decoupling the outer-inner dependency.
Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition
Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision.
Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases
In particular, updating an intelligent diagnosis system with training data of new diseases would cause catastrophic forgetting of old disease knowledge.
Hierarchical Interactive Reconstruction Network For Video Compressive Sensing
Deep network-based image and video Compressive Sensing(CS) has attracted increasing attentions in recent years.
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
Utilizing a reward model and a sufficient number of samples, our approach selects the high-quality samples, discarding those that exhibit undesired behavior, and subsequently enhancing the model by fine-tuning on these filtered samples.
Crowd Counting with Sparse Annotation
This paper presents a new annotation method called Sparse Annotation (SA) for crowd counting, which reduces human labeling efforts by sparsely labeling individuals in an image.
ConvBLS: An Effective and Efficient Incremental Convolutional Broad Learning System for Image Classification
Third, the TSMS feature fusion layer is proposed to extract more effective multi-scale features through the integration of CF layers and CE layers.
De-coupling and De-positioning Dense Self-supervised Learning
Dense Self-Supervised Learning (SSL) methods address the limitations of using image-level feature representations when handling images with multiple objects.
Spatiotemporal Self-supervised Learning for Point Clouds in the Wild
In this paper, we introduce an SSL strategy that leverages positive pairs in both the spatial and temporal domain.
NEMTO: Neural Environment Matting for Novel View and Relighting Synthesis of Transparent Objects
We propose NEMTO, the first end-to-end neural rendering pipeline to model 3D transparent objects with complex geometry and unknown indices of refraction.
Environment Invariant Linear Least Squares
To the best of our knowledge, this paper is the first to realize statistically efficient invariance learning in the general linear model.
PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium
To circumvent this difficulty, we examine the problem of identifying a mixed Nash equilibrium, where strategies are randomized and characterized by probability distributions over continuous domains. To this end, we propose PArticle-based Primal-dual ALgorithm (PAPAL) tailored for a weakly entropy-regularized min-max optimization over probability distributions.
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data
However, most CoT studies rely on carefully designed human-annotated rational chains to prompt LLMs, posing challenges for real-world applications where labeled data is available without rational chains.
Active Prompting with Chain-of-Thought for Large Language Models
For this purpose, we propose a solution to the key problem of determining which questions are the most important and helpful ones to annotate from a pool of task-specific queries.
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency
We propose a variance-adaptive algorithm for linear mixture MDPs, which achieves a problem-dependent horizon-free regret bound that can gracefully reduce to a nearly constant regret for deterministic MDPs.
Hashtag-Guided Low-Resource Tweet Classification
Social media classification tasks (e. g., tweet sentiment analysis, tweet stance detection) are challenging because social media posts are typically short, informal, and ambiguous.
On the Convergence of Federated Averaging with Cyclic Client Participation
Federated Averaging (FedAvg) and its variants are the most popular optimization algorithms in federated learning (FL).
History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System
HAHT maintains a long-term memory of history conversations and utilizes history information to understand current conversation context and generate well-informed and context-relevant responses.
ADAPT: Action-aware Driving Caption Transformer
To bridge the gap, we propose an end-to-end transformer-based architecture, ADAPT (Action-aware Driving cAPtion Transformer), which provides user-friendly natural language narrations and reasoning for each decision making step of autonomous vehicular control and action.
Learning in POMDPs is Sample-Efficient with Hindsight Observability
POMDPs capture a broad class of decision making problems, but hardness results suggest that learning is intractable even in simple settings due to the inherent partial observability.
Model Agnostic Sample Reweighting for Out-of-Distribution Learning
The overfitting issue is addressed by considering a bilevel formulation to search for the sample reweighting, in which the generalization complexity depends on the search space of sample weights instead of the model size.
Probabilistic Bilevel Coreset Selection
The goal of coreset selection in supervised learning is to produce a weighted subset of data, so that training only on the subset achieves similar performance as training on the entire dataset.
TempSAL -- Uncovering Temporal Information for Deep Saliency Prediction
Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information, such as scene context, semantic relationships, gaze direction, and object dissimilarity.
TempSAL - Uncovering Temporal Information for Deep Saliency Prediction
Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information such as scene context, semantic relationships, gaze direction, and object dissimilarity.
Ranked #3 on
Saliency Prediction
on SALECI
DSI2I: Dense Style for Unpaired Image-to-Image Translation
Unpaired exemplar-based image-to-image (UEI2I) translation aims to translate a source image to a target image domain with the style of a target image exemplar, without ground-truth input-translation pairs.
VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction
The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction.
VO$Q$L: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation
We study time-inhomogeneous episodic reinforcement learning (RL) under general function approximation and sparse rewards.
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes
In this paper, we consider the contextual bandit with general function approximation and propose a computationally efficient algorithm to achieve a regret of $\tilde{O}(\sqrt{T}+\zeta)$.
ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT
In this paper, we present ExtremeBERT, a toolkit for accelerating and customizing BERT pretraining.
On Robust Observer Design for System Motion on SE(3) Using Onboard Visual Sensors
The key design idea for this observer is to estimate the visible set and identify the mis-identified features from the measurements.
Particle-based Variational Inference with Preconditioned Functional Gradient Flow
With the popularity of Stein variational gradient descent (SVGD), the focus of particle-based VI algorithms has been on the properties of functions in Reproducing Kernel Hilbert Space (RKHS) to approximate the gradient flow.
DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata
Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos.
Normalizing Flow with Variational Latent Representation
The resulting method is significantly more powerful than the standard normalization flow approach for generating data distributions with multiple modes.
FAF: A novel multimodal emotion recognition approach integrating face, body and text
In this paper, we developed a large multimodal emotion dataset, named "HED" dataset, to facilitate the emotion recognition task, and accordingly propose a multimodal emotion recognition method.
Prior-mean-assisted Bayesian optimization application on FRIB Front-End tunning
Bayesian optimization~(BO) is often used for accelerator tuning due to its high sample efficiency.
GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond
The proposed algorithm modifies the standard posterior sampling algorithm in two aspects: (i) we use an optimistic prior distribution that biases towards hypotheses with higher values and (ii) a loglikelihood function is set to be the empirical loss evaluated on the historical data, where the choice of loss function supports both model-free and model-based learning.
Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving
However, wireless channels between the edge server and the autonomous vehicles are time-varying due to the high-mobility of vehicles.
Increasing Visual Awareness in Multimodal Neural Machine Translation from an Information Theoretic Perspective
Multimodal machine translation (MMT) aims to improve translation quality by equipping the source sentence with its corresponding image.
MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation
Commonsense reasoning tasks such as commonsense knowledge graph completion and commonsense question answering require powerful representation learning.
Multilingual Word Sense Disambiguation with Unified Sense Representation
As a key natural language processing (NLP) task, word sense disambiguation (WSD) evaluates how well NLP models can understand the lexical semantics of words under specific contexts.
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games
Existing studies on provably efficient algorithms for Markov games (MGs) almost exclusively build on the "optimism in the face of uncertainty" (OFU) principle.
Optimal Operation of a Tidal Lagoon as a Flexible Source of Electricity
It was demonstrated that by exploiting the flexibility offered by the tidal lagoon, it can achieve a higher revenue in the day-ahead market, although their total electricity generation is reduced.
Asymptotic Statistical Analysis of $f$-divergence GAN
We show that for parametric generative models that are correctly specified, all $f$-divergence GANs with the same discriminator classes are asymptotically equivalent under suitable regularity conditions.
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs
Answering natural language questions on knowledge graphs (KGQA) remains a great challenge in terms of understanding complex questions via multi-hop reasoning.
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning
Thompson Sampling is one of the most effective methods for contextual bandits and has been generalized to posterior sampling for certain MDP settings.
Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition
To overcome the above shortcoming, we introduce a novel unified two-person graph to represent inter-body and intra-body correlations between joints.
OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms
In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing platforms.
Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain
Currently, under supervised learning, a model pretrained by a large-scale nature scene dataset and then fine-tuned on a few specific task labeling data is the paradigm that has dominated the knowledge transfer learning.
Beyond Uniform Lipschitz Condition in Differentially Private Optimization
Most prior results on differentially private stochastic gradient descent (DP-SGD) are derived under the simplistic assumption of uniform Lipschitzness, i. e., the per-sample gradients are uniformly bounded.
Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity
We propose a general framework to design posterior sampling methods for model-based RL.
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data
Motivated by this observation, we propose a new quantity, average drift at optimum, to measure the effects of data heterogeneity, and explicitly use it to present a new theoretical analysis of FedAvg.
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint
Overparameterized neural networks enjoy great representation power on complex data, and more importantly yield sufficiently smooth output, which is crucial to their generalization and robustness.
An Indoor Environment Sensing and Localization System via mmWave Phased Array
At the end, we show that the reconstructed room layout can be utilized to locate a mobile device according to its AoA spectrum, even with single access point.
Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization
This paper considers the generalization performance of differentially private convex learning.
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game
We also extend our techniques to the two-player zero-sum Markov games (MGs), and establish a new performance lower bound for MGs, which tightens the existing result, and verifies the nearly minimax optimality of the proposed algorithm.
MulT: An End-to-End Multitask Learning Transformer
At the heart of our approach is a shared attention mechanism modeling the dependencies across the tasks.
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions
We show that for both known $C$ and unknown $C$ cases, our algorithm with proper choice of hyperparameter achieves a regret that nearly matches the lower bounds.
Deep Non-rigid Structure-from-Motion: A Sequence-to-Sequence Translation Perspective
In this paper, we propose to model deep NRSfM from a sequence-to-sequence translation perspective, where the input 2D frame sequence is taken as a whole to reconstruct the deforming 3D non-rigid shape sequence.
Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy
In essence, this strategy ignores the fact that two crops may truly contain different image information, e. g., background and small objects, and thus tends to restrain the diversity of the learned representations.
Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling
Provably sample-efficient Reinforcement Learning (RL) with rich observations and function approximation has witnessed tremendous recent progress, particularly when the underlying function approximators are linear.
RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering
Finding accurate correspondences among different views is the Achilles' heel of unsupervised Multi-View Stereo (MVS).
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets
When the dataset does not have uniform coverage over all policy pairs, finding an approximate NE involves challenges in three aspects: (i) distributional shift between the behavior policy and the optimal policy, (ii) function approximation to handle large state space, and (iii) minimax optimization for equilibrium solving.
A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework
This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation.
Fast Rates in Pool-Based Batch Active Learning
We consider a batch active learning scenario where the learner adaptively issues batches of points to a labeling oracle.
Minimax Regret Optimization for Robust Machine Learning under Distribution Shift
We instead propose an alternative method called Minimax Regret Optimization (MRO), and show that under suitable conditions this method achieves uniformly low regret across all test distributions.
Black-box Prompt Learning for Pre-trained Language Models
Particularly, instead of fine-tuning the model in the cloud, we adapt PLMs by prompt learning, which efficiently optimizes only a few parameters of the discrete prompts.
A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition
In this paper, we present a simple multi-task framework for SMER, which incorporates the emotion recognition task with other emotion-related auxiliary tasks derived from the intrinsic structure of the music.
OneDConv: Generalized Convolution For Transform-Invariant Representation
Convolutional Neural Networks (CNNs) have exhibited their great power in a variety of vision tasks.
Time Series Generation with Masked Autoencoder
This paper shows that masked autoencoder with extrapolator (ExtraMAE) is a scalable self-supervised model for time series generation.
IDEA: Interpretable Dynamic Ensemble Architecture for Time Series Prediction
We enhance the accuracy and generalization of univariate time series point prediction by an explainable ensemble on the fly.
Bayesian Invariant Risk Minimization
Generalization under distributional shift is an open challenge for machine learning.
Frequency-Aware Contrastive Learning for Neural Machine Translation
Inspired by the observation that low-frequency words form a more compact embedding space, we tackle this challenge from a representation learning perspective.
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training
This lets us show that the decay in generalization performance of adversarial training is a result of the model's attempt to fit hard adversarial instances.
CLIP Meets Video Captioning: Concept-Aware Representation Learning Does Matter
DCD is an auxiliary task that requires a caption model to learn the correspondence between video content and concepts and the co-occurrence relations between concepts.
Ranked #15 on
Video Captioning
on MSR-VTT
Optimizing Latent Space Directions For GAN-based Local Image Editing
Generative Adversarial Network (GAN) based localized image editing can suffer from ambiguity between semantic attributes.
Efficient Neural Network Training via Forward and Backward Propagation Sparsification
For the latter step, instead of using the chain rule based gradient estimators as in existing methods, we propose a variance reduced policy gradient estimator, which only requires two forward passes without backward propagation, thus achieving completely sparse training.
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization
Afterward, we prove that under ideal conditions, independence-driven importance weighting algorithms could identify the variables in this set.
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums
In this paper, we propose Eigencurve, the first family of learning rate schedules that can achieve minimax optimal convergence rates (up to a constant) for SGD on quadratic objectives when the eigenvalue distribution of the underlying Hessian matrix is skewed.
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint
There has been a surge of works bridging MCMC sampling and optimization, with a specific focus on translating non-asymptotic convergence guarantees for optimization problems into the analysis of Langevin algorithms in MCMC sampling.
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning
In this setting, we show that the standard Thompson Sampling is not aggressive enough in exploring new actions, leading to suboptimality in some pessimistic situations.
HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning
However, it is limited to the case where 1) a good feature is known in advance and 2) this feature is fixed during the training: if otherwise, RLSVI suffers an unbearable computational burden to obtain the posterior samples of the parameter in the $Q$-value function.
Improving Adversarial Defense with Self-supervised Test-time Fine-tuning
Although adversarial training and its variants currently constitute the most effective way to achieve robustness against adversarial attacks, their poor generalization limits their performance on the test samples.
Interest-based Item Representation Framework for Recommendation with Multi-Interests Capsule Network
To make the framework model-agnostic, user Multi Interests Capsule Network is designed as an auxiliary task to jointly learn item-based item representations and interest-based item representations.
EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography
In this paper, we present an anchor-free ellipse detection network, namely EllipseNet, which detects the cardiac and thoracic regions in ellipse and automatically calculates the CTR and cardiac axis for fetal cardiac biometrics in 4-chamber view.
Feature Correlation Aggregation: on the Path to Better Graph Neural Networks
The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbors.
G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation
In this paper, we investigate the knowledge distillation (KD) strategy for object detection and propose an effective framework applicable to both homogeneous and heterogeneous student-teacher pairs.
Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation
This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models.
Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation
In this paper, we aim to adapt a generic pretrained model with a relatively small amount of domain-specific data.
Accelerating Edge Intelligence via Integrated Sensing and Communication
Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time.
A Field Guide to Federated Optimization
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.
Multi-Task Hierarchical Learning Based Network Traffic Analytics
Classifying network traffic is the basis for important network applications.
Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization
This paper considers stochastic first-order algorithms for convex-concave minimax problems of the form $\min_{\bf x}\max_{\bf y}f(\bf x, \bf y)$, where $f$ can be presented by the average of $n$ individual components which are $L$-average smooth.
Multi-Hop Transformer for Document-Level Machine Translation
Document-level neural machine translation (NMT) has proven to be of profound value for its effectiveness on capturing contextual information.
Universal Adder Neural Networks
The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values.
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation
For student morphism, weight inheritance strategy is adopted, allowing the student to flexibly update its architecture while fully utilize the predecessor's weights, which considerably accelerates the search; To facilitate dynamic distillation, an elastic teacher pool is trained via integrated progressive shrinking strategy, from which teacher detectors can be sampled without additional cost in subsequent searches.
TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search
While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks.
KECRS: Towards Knowledge-Enriched Conversational Recommendation System
The chit-chat-based conversational recommendation systems (CRS) provide item recommendations to users through natural language interactions.
ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders
Pre-trained text encoders have drawn sustaining attention in natural language processing (NLP) and shown their capability in obtaining promising results in different tasks.
Effective Sparsification of Neural Networks with Global Sparsity Constraint
Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real-world deployments.
Exploring Geometric Consistency for Monocular 3D Object Detection
In addition, we demonstrate that the augmentation methods are well suited for semi-supervised training and cross-dataset generalization.
Reinforced Attention for Few-Shot Learning and Beyond
Few-shot learning aims to correctly recognize query samples from unseen classes given a limited number of support samples, often by relying on global embeddings of images.
Modeling Object Dissimilarity for Deep Saliency Prediction
Saliency prediction has made great strides over the past two decades, with current techniques modeling low-level information, such as color, intensity and size contrasts, and high-level ones, such as attention and gaze direction for entire objects.
Uncertainty-aware Joint Salient Object and Camouflaged Object Detection
Visual salient object detection (SOD) aims at finding the salient object(s) that attract human attention, while camouflaged object detection (COD) on the contrary intends to discover the camouflaged object(s) that hidden in the surrounding.
Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling
We present a new method for few-shot human motion transfer that achieves realistic human image generation with only a small number of appearance inputs.
Involution: Inverting the Inherence of Convolution for Visual Recognition
Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision.
Ranked #703 on
Image Classification
on ImageNet
Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction
We further extend graph convolution into tensor space and propose a tensor graph convolution network to extract more discriminating features from spatial-temporal graph data.
Ranked #1 on
Traffic Prediction
on SZ-Taxi
Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning
Despite tremendous efforts, it is very challenging to generate a robust model to assist in the accurate quantification assessment of COVID-19 on chest CT images.
Siamese Labels Auxiliary Learning
In general, the main work of this paper include: (1) propose SiLa Learning, which improves the performance of common models without increasing test parameters; (2) compares SiLa with DML and proves that SiLa can improve the generalization of the model; (3) SiLa is applied to Dynamic Neural Networks, and proved that SiLa can be used for various types of network structures.
DeEPCA: Decentralized Exact PCA with Linear Convergence Rate
This leads to a decentralized PCA algorithm called \texttt{DeEPCA}, which has a convergence rate similar to that of the centralized PCA, while achieving the best communication complexity among existing decentralized PCA algorithms.
Nondiscriminatory Treatment: a straightforward framework for multi-human parsing
In inference time, we design a brand-new grouping post-processing method that relates each part instance with one single human instance and groups them together to obtain the final human-level parsing result.
On Secure Degrees of Freedom of the MIMO Interference Channel with Local Output Feedback
This paper studies the problem of sum-secure degrees of freedom (SDoF) of the (M, M, N, N) multiple-input multiple-output (MIMO) interference channel with local output feedback, so as to build an information-theoretic foundation and provide practical transmission schemes for 6G-enabled vehicles-to-vehicles (V2V).
Information Theory Information Theory
Invariant Batch Normalization for Multi-source Domain Generalization
We consider the domain generalization problem, where the test domain differs from the training domain.
Effective Training of Sparse Neural Networks under Global Sparsity Constraint
An appealing feature of ProbMask is that the amounts of weight redundancy can be learned automatically via our constraint and thus we avoid the problem of tuning pruning rates individually for different layers in a network.
TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture Search
While existing NAS methods mostly design architectures on one single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks.
Graph Deformer Network
In this paper, we propose a simple yet effective graph deformer network (GDN) to fulfill anisotropic convolution filtering on graphs, analogous to the standard convolution operation on images.
Wasserstein Coupled Graph Learning for Cross-Modal Retrieval
Then, a Wasserstein coupled dictionary, containing multiple pairs of counterpart graph keys with each key corresponding to one modality, is constructed for further feature learning.
PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction
This paper considers the decentralized composite optimization problem.
Mathematical Models of Overparameterized Neural Networks
Deep learning has received considerable empirical successes in recent years.
Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information
As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks.
Evidence of topological nodal lines and surface states in the centrosymmetric superconductor SnTaS2
Utilizing angle-resolved photoemission spectroscopy and first-principles calculations, here, we demonstrate the existence of topological nodal-line states and drumheadlike surface states in centrosymmetric superconductor SnTaS2, which is a type-II superconductor with a critical transition temperature of about 3 K. The valence bands from Ta 5d orbitals and the conduction bands from Sn 5p orbitals cross each other, forming two nodal lines in the vicinity of the Fermi energy without the inclusion of spin-orbit coupling (SOC), protected by the spatial-inversion symmetry and time-reversal symmetry.
Superconductivity
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts
In particular, we propose a novel joint-training framework to train plain CNN by leveraging the gradients of the ResNet counterpart.
How to Characterize The Landscape of Overparameterized Convolutional Neural Networks
With the help of a new technique called {\it neural network grafting}, we demonstrate that even during the entire training process, feature distributions of differently initialized networks remain similar at each layer.
Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets
To this end, we summarize a tiny formula for downsizing neural architectures through a series of smaller models derived from the EfficientNet-B0 with the FLOPs constraint.
Decentralized Accelerated Proximal Gradient Descent
In this paper, we propose a new method which establishes the optimal computational complexity and a near optimal communication complexity.
VEGA: Towards an End-to-End Configurable AutoML Pipeline
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets
To this end, we summarize a tiny formula for downsizing neural architectures through a series of smaller models derived from the EfficientNet-B0 with the FLOPs constraint.
Ranked #692 on
Image Classification
on ImageNet
Improving Constituency Parsing with Span Attention
Constituency parsing is a fundamental and important task for natural language understanding, where a good representation of contextual information can help this task.
Ranked #1 on
Constituency Parsing
on ATB
DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks
Specifically, we search for a meta graph, which can capture more complex semantic relations than a meta path, to determine how graph neural networks (GNNs) propagate messages along different types of edges.
Weakly Supervised Disentangled Generative Causal Representation Learning
This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method under appropriate supervised information.
CorrAttack: Black-box Adversarial Attack with Structured Search
We show that searching over the structured space can be approximated by a time-varying contextual bandits problem, where the attacker takes feature of the associated arm to make modifications of the input, and receives an immediate reward as the reduction of the loss function.
Interest-Behaviour Multiplicative Network for Resource-limited Recommendation
In this paper, we aim to mine the cue of user preferences in resource-limited recommendation tasks, for which purpose we specifically build a large used car transaction dataset possessing resource-limitation characteristics.
Instance-Aware Graph Convolutional Network for Multi-Label Classification
For label diffusion of instance-awareness in graph convolution, rather than using the statistical label correlation alone, an image-dependent label correlation matrix (LCM), fusing both the statistical LCM and an individual one of each image instance, is constructed for graph inference on labels to inject adaptive information of label-awareness into the learned features of the model.
