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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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09/18/2023
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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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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08/30/2023
MerA: Merging Pretrained Adapters For Few-Shot Learning
Adapter tuning, which updates only a few parameters, has become a mainst...
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08/24/2023
Can Linguistic Knowledge Improve Multimodal Alignment in Vision-Language Pretraining?
The multimedia community has shown a significant interest in perceiving ...
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08/24/2023
Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints
We propose a novel master-slave architecture to solve the top-K combinat...
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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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08/01/2023
LGViT: Dynamic Early Exiting for Accelerating Vision Transformer
Recently, the efficient deployment and acceleration of powerful vision t...
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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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07/25/2023
High-Resolution Volumetric Reconstruction for Clothed Humans
We present a novel method for reconstructing clothed humans from a spars...
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07/17/2023
GBT: Two-stage transformer framework for non-stationary time series forecasting
This paper shows that time series forecasting Transformer (TSFT) suffers...
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07/16/2023
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning
Forgetting refers to the loss or deterioration of previously acquired in...
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07/14/2023
Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy
Data-poisoning based backdoor attacks aim to insert backdoor into models...
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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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06/25/2023
Enhancing Adversarial Training via Reweighting Optimization Trajectory
Despite the fact that adversarial training has become the de facto metho...
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06/19/2023
FDNet: Focal Decomposed Network for Efficient, Robust and Practical Time Series Forecasting
This paper presents FDNet: a Focal Decomposed Network for efficient, rob...
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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
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
Although graph neural networks (GNNs) have achieved impressive achieveme...
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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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05/30/2023
Dynamic Sparsity Is Channel-Level Sparsity Learner
Sparse training has received an upsurging interest in machine learning d...
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05/30/2023
Are Large Kernels Better Teachers than Transformers for ConvNets?
This paper reveals a new appeal of the recently emerged large-kernel Con...
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05/29/2023
Compact Real-time Radiance Fields with Neural Codebook
Reconstructing neural radiance fields with explicit volumetric represent...
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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
Incomplete Multimodal Learning for Complex Brain Disorders Prediction
Recent advancements in the acquisition of various brain data sources hav...
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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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05/24/2023
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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05/19/2023
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
DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation
Predominant techniques on talking head generation largely depend on 2D i...
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05/02/2023
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing
Federated learning (FL) is a collaborative learning paradigm for decentr...
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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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04/24/2023
Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimization
Backdoor defense, which aims to detect or mitigate the effect of malicio...
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04/10/2023
Evaluate Geometry of Radiance Field with Low-frequency Color Prior
Radiance field is an effective representation of 3D scenes, which has be...
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04/07/2023
On Efficient Training of Large-Scale Deep Learning Models: A Literature Review
The field of deep learning has witnessed significant progress, particula...
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04/04/2023
Quantum Imitation Learning
Despite remarkable successes in solving various complex decision-making ...
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03/24/2023
Towards Making the Most of ChatGPT for Machine Translation
ChatGPT shows remarkable capabilities for machine translation (MT). Seve...
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03/23/2023
Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization
Robust generalization aims to tackle the most challenging data distribut...
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03/20/2023
Make Landscape Flatter in Differentially Private Federated Learning
To defend the inference attacks and mitigate the sensitive information l...
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03/20/2023
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning
The goal of data-free meta-learning is to learn useful prior knowledge f...
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03/15/2023
Visual Prompt Based Personalized Federated Learning
As a popular paradigm of distributed learning, personalized federated le...
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03/07/2023
Graph Decision Transformer
Offline reinforcement learning (RL) is a challenging task, whose objecti...
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03/07/2023
SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention
Lung cancer is the leading cause of cancer death worldwide. The best sol...
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03/01/2023
AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks
Sharpness aware minimization (SAM) optimizer has been extensively explor...
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02/24/2023
Subspace based Federated Unlearning
Federated learning (FL) enables multiple clients to train a machine lear...
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02/21/2023
Fusion of Global and Local Knowledge for Personalized Federated Learning
Personalized federated learning, as a variant of federated learning, tra...
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02/21/2023
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy
Federated learning is an emerging distributed machine learning framework...
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02/18/2023
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE
This technical report briefly describes our JDExplore d-team's submissio...
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02/15/2023
FedABC: Targeting Fair Competition in Personalized Federated Learning
Federated learning aims to collaboratively train models without accessin...
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02/08/2023