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09/20/2023
Are Large Language Models Really Robust to Word-Level Perturbations?
The swift advancement in the scale and capabilities of Large Language Mo...
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09/19/2023
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks
Graph Neural Networks (GNNs) have become popular in Graph Representation...
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09/18/2023
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data
Federated learning is an emerging distributed machine learning method, e...
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09/11/2023
Temporal Action Localization with Enhanced Instant Discriminability
Temporal action detection (TAD) aims to detect all action boundaries and...
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09/02/2023
RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model
Text-to-image generation (TTI) refers to the usage of models that could ...
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09/01/2023
DARC: Distribution-Aware Re-Coloring Model for Generalizable Nucleus Segmentation
Nucleus segmentation is usually the first step in pathological image ana...
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08/31/2023
Continual Learning From a Stream of APIs
Continual learning (CL) aims to learn new tasks without forgetting previ...
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08/31/2023
CktGNN: Circuit Graph Neural Network for Electronic Design Automation
The electronic design automation of analog circuits has been a longstand...
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08/30/2023
MerA: Merging Pretrained Adapters For Few-Shot Learning
Adapter tuning, which updates only a few parameters, has become a mainst...
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08/29/2023
Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models
Most open-domain dialogue systems suffer from forgetting important infor...
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08/22/2023
ShadowNet for Data-Centric Quantum System Learning
Understanding the dynamics of large quantum systems is hindered by the c...
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08/18/2023
Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent
Stochastic gradient descent (SGD) performed in an asynchronous manner pl...
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08/16/2023
DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning
To address the communication burden issues associated with federated lea...
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08/05/2023
Cross-modal Cross-domain Learning for Unsupervised LiDAR Semantic Segmentation
In recent years, cross-modal domain adaptation has been studied on the p...
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08/03/2023
Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants
How to enable learnability for new classes while keeping the capability ...
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07/30/2023
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup
Adaptive optimization has achieved notable success for distributed learn...
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07/26/2023
PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions
Relying on large-scale training data with pixel-level labels, previous e...
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07/26/2023
Understanding Deep Neural Networks via Linear Separability of Hidden Layers
In this paper, we measure the linear separability of hidden layer output...
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07/22/2023
Patch-Wise Point Cloud Generation: A Divide-and-Conquer Approach
A generative model for high-fidelity point clouds is of great importance...
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07/20/2023
Heterogeneous Federated Learning: State-of-the-art and Research Challenges
Federated learning (FL) has drawn increasing attention owing to its pote...
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06/30/2023
Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer
Deep neural networks often suffer from poor generalization due to comple...
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06/16/2023
Structured Cooperative Learning with Graphical Model Priors
We study how to train personalized models for different tasks on decentr...
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06/09/2023
Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization
Federated learning (FL) is a distributed paradigm that coordinates massi...
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06/08/2023
Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning
Recent works have shown the potential of diffusion models in computer vi...
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06/06/2023
Transition role of entangled data in quantum machine learning
Entanglement serves as the resource to empower quantum computing. Recent...
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06/05/2023
Towards Arbitrarily Expressive GNNs in O(n^2) Space by Rethinking Folklore Weisfeiler-Lehman
Message passing neural networks (MPNNs) have emerged as the most popular...
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06/05/2023
Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training
Dense retrievers have achieved impressive performance, but their demand ...
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06/05/2023
Decentralized SGD and Average-direction SAM are Asymptotically Equivalent
Decentralized stochastic gradient descent (D-SGD) allows collaborative l...
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06/05/2023
MotionTrack: Learning Motion Predictor for Multiple Object Tracking
Significant advancements have been made in multi-object tracking (MOT) w...
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06/02/2023
Collect-and-Distribute Transformer for 3D Point Cloud Analysis
Although remarkable advancements have been made recently in point cloud ...
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06/01/2023
Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image Generation
Text-conditional diffusion models are able to generate high-fidelity ima...
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06/01/2023
Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking
Zero-shot transfer learning for Dialogue State Tracking (DST) helps to h...
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05/28/2023
Learning to Learn from APIs: Black-Box Data-Free Meta-Learning
Data-free meta-learning (DFML) aims to enable efficient learning of new ...
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05/25/2023
Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning
Data augmentation (DA) is a crucial technique for enhancing the sample e...
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05/25/2023
All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation
Pseudo-labels are widely employed in weakly supervised 3D segmentation t...
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05/24/2023
Self-Evolution Learning for Discriminative Language Model Pretraining
Masked language modeling, widely used in discriminative language model (...
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05/24/2023
Revisiting Token Dropping Strategy in Efficient BERT Pretraining
Token dropping is a recently-proposed strategy to speed up the pretraini...
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05/24/2023
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training
Personalized federated learning (PFL) aims to produce the greatest perso...
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05/23/2023
Improving Heterogeneous Model Reuse by Density Estimation
This paper studies multiparty learning, aiming to learn a model using th...
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05/22/2023
Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks
Text classification tasks often encounter few shot scenarios with limite...
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05/22/2023
VanillaNet: the Power of Minimalism in Deep Learning
At the heart of foundation models is the philosophy of "more is differen...
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05/19/2023
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape
In federated learning (FL), a cluster of local clients are chaired under...
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05/16/2023
Prompt-Tuning Decision Transformer with Preference Ranking
Prompt-tuning has emerged as a promising method for adapting pre-trained...
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05/10/2023
MMoT: Mixture-of-Modality-Tokens Transformer for Composed Multimodal Conditional Image Synthesis
Existing multimodal conditional image synthesis (MCIS) methods generate ...
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05/01/2023
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy
To defend the inference attacks and mitigate the sensitive information l...
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04/28/2023
Deep Graph Reprogramming
In this paper, we explore a novel model reusing task tailored for graph ...
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04/27/2023
Compositional 3D Human-Object Neural Animation
Human-object interactions (HOIs) are crucial for human-centric scene und...
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04/23/2023
HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search
Recent neural architecture search (NAS) based approaches have made great...
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04/23/2023
Segment Anything in Non-Euclidean Domains: Challenges and Opportunities
The recent work known as Segment Anything (SA) has made significant stri...
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04/07/2023