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08/29/2023
Cross-Modal Retrieval Meets Inference:Improving Zero-Shot Classification with Cross-Modal Retrieval
Contrastive language-image pre-training (CLIP) has demonstrated remarkab...
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08/24/2023
FedSoL: Bridging Global Alignment and Local Generality in Federated Learning
Federated Learning (FL) aggregates locally trained models from individua...
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06/19/2023
Enhancing Generalization and Plasticity for Sample Efficient Reinforcement Learning
In Reinforcement Learning (RL), enhancing sample efficiency is crucial, ...
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06/18/2023
Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model
We consider the problem of recovering hidden communities in the Labeled ...
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05/23/2023
Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification
Respiratory sound contains crucial information for the early diagnosis o...
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05/19/2023
Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation
Transformer-based speech self-supervised learning (SSL) models, such as ...
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03/22/2023
Re-thinking Federated Active Learning based on Inter-class Diversity
Although federated learning has made awe-inspiring advances, most studie...
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03/20/2023
Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning
Deep learning in general domains has constantly been extended to domain-...
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03/09/2023
Communication-Efficient Collaborative Heterogeneous Bandits in Networks
The multi-agent multi-armed bandit problem has been studied extensively ...
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03/03/2023
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning
The multi-agent setting is intricate and unpredictable since the behavio...
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02/10/2023
CUDA: Curriculum of Data Augmentation for Long-Tailed Recognition
Class imbalance problems frequently occur in real-world tasks, and conve...
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02/03/2023
Revisiting Intermediate Layer Distillation for Compressing Language Models: An Overfitting Perspective
Knowledge distillation (KD) is a highly promising method for mitigating ...
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12/20/2022
Large Language Models Are Reasoning Teachers
Language models (LMs) have demonstrated remarkable performance on downst...
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12/07/2022
MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy
Cell segmentation is a fundamental task for computational biology analys...
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12/05/2022
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition
The Weather4Cast competition (hosted by NeurIPS 2022) required competito...
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12/01/2022
Denoising after Entropy-based Debiasing A Robust Training Method for Dataset Bias with Noisy Labels
Improperly constructed datasets can result in inaccurate inferences. For...
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10/18/2022
Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks
Translation has played a crucial role in improving the performance on mu...
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08/17/2022
Nearly Optimal Latent State Decoding in Block MDPs
We investigate the problems of model estimation and reward-free learning...
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07/05/2022
The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
In this paper, we propose a novel benchmark called the StarCraft Multi-A...
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06/30/2022
Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction
Precipitation forecasting is an important scientific challenge that has ...
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06/28/2022
Risk Perspective Exploration in Distributional Reinforcement Learning
Distributional reinforcement learning demonstrates state-of-the-art perf...
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06/27/2022
Revisiting Architecture-aware Knowledge Distillation: Smaller Models and Faster Search
Knowledge Distillation (KD) has recently emerged as a popular method for...
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06/18/2022
Demystifying the Base and Novel Performances for Few-shot Class-incremental Learning
Few-shot class-incremental learning (FSCIL) has addressed challenging re...
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06/15/2022
ALASCA: Rethinking Label Smoothing for Deep Learning Under Label Noise
As label noise, one of the most popular distribution shifts, severely de...
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06/03/2022
Supernet Training for Federated Image Classification under System Heterogeneity
Efficient deployment of deep neural networks across many devices and res...
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05/31/2022
Mitigating Dataset Bias by Using Per-sample Gradient
The performance of deep neural networks is strongly influenced by the tr...
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05/30/2022
Adversarial Bandits Robust to S-Switch Regret
We study the adversarial bandit problem under S number of switching best...
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05/13/2022
Revisiting the Updates of a Pre-trained Model for Few-shot Learning
Most of the recent few-shot learning algorithms are based on transfer le...
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05/11/2022
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning
Cross-domain few-shot learning (CD-FSL), where there are few target samp...
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05/03/2022
FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning
Robustness is becoming another important challenge of federated learning...
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04/11/2022
Neural Processes with Stochastic Attention: Paying more attention to the context dataset
Neural processes (NPs) aim to stochastically complete unseen data points...
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04/08/2022
SuperNet in Neural Architecture Search: A Taxonomic Survey
Deep Neural Networks (DNN) have made significant progress in a wide rang...
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02/01/2022
Understanding Cross-Domain Few-Shot Learning: An Experimental Study
Cross-domain few-shot learning has drawn increasing attention for handli...
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01/31/2022
Rotting infinitely many-armed bandits
We consider the infinitely many-armed bandit problem with rotting reward...
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06/29/2021
Self-Contrastive Learning
This paper proposes a novel contrastive learning framework, coined as Se...
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06/06/2021
Preservation of the Global Knowledge by Not-True Self Knowledge Distillation in Federated Learning
In Federated Learning (FL), a strong global model is collaboratively lea...
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05/19/2021
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation
Knowledge distillation (KD), transferring knowledge from a cumbersome te...
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02/23/2021
Winning Ticket in Noisy Image Classification
Modern deep neural networks (DNNs) become frail when the datasets contai...
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12/30/2020
Test Score Algorithms for Budgeted Stochastic Utility Maximization
Motivated by recent developments in designing algorithms based on indivi...
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12/06/2020
TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture
Federated learning has emerged as a new paradigm of collaborative machin...
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10/23/2020
Regret in Online Recommendation Systems
This paper proposes a theoretical analysis of recommendation systems in ...
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10/13/2020
MixCo: Mix-up Contrastive Learning for Visual Representation
Contrastive learning has shown remarkable results in recent self-supervi...
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08/20/2020
Does MAML really want feature reuse only?
Meta-learning, the effort to solve new tasks with only a few samples, ha...
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10/14/2019
Optimal Clustering from Noisy Binary Feedback
We study the problem of recovering clusters from binary user feedback. I...
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07/29/2019
Reinforcement with Fading Memories
We study the effect of imperfect memory on decision making in the contex...
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05/14/2019
Spectral Approximate Inference
Given a graphical model (GM), computing its partition function is the mo...
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10/26/2018
Spectrogram-channels u-net: a source separation model viewing each channel as the spectrogram of each source
Nowadays, the task of sound source separation is an interesting task for...
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03/03/2017
Contextual Multi-armed Bandits under Feature Uncertainty
We study contextual multi-armed bandit problems under linear realizabili...
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10/20/2015
Optimal Cluster Recovery in the Labeled Stochastic Block Model
We consider the problem of community detection or clustering in the labe...
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04/13/2015