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07/20/2023
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression
This paper proposes a novel communication-efficient split learning (SL) ...
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02/22/2023
Debiased Distillation by Transplanting the Last Layer
Deep models are susceptible to learning spurious correlations, even duri...
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02/21/2023
MaskedKD: Efficient Distillation of Vision Transformers with Masked Images
Knowledge distillation is a popular and effective regularization techniq...
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02/01/2023
Efficient Meta-Learning via Error-based Context Pruning for Implicit Neural Representations
We introduce an efficient optimization-based meta-learning technique for...
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01/23/2023
Modality-Agnostic Variational Compression of Implicit Neural Representations
We introduce a modality-agnostic neural data compression algorithm based...
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12/05/2022
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling
Trying to capture the sample-label relationship, conditional generative ...
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10/13/2022
Scalable Neural Video Representations with Learnable Positional Features
Succinct representation of complex signals using coordinate-based neural...
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10/11/2022
Meta-Learning with Self-Improving Momentum Target
The idea of using a separately trained target model (or teacher) to impr...
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04/05/2022
Zero-shot Blind Image Denoising via Implicit Neural Representations
Recent denoising algorithms based on the "blind-spot" strategy show impr...
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04/05/2022
Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation
The paradigm of worst-group loss minimization has shown its promise in a...
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10/27/2021
Meta-Learning Sparse Implicit Neural Representations
Implicit neural representations are a promising new avenue of representi...
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06/28/2021
Co^2L: Contrastive Continual Learning
Recent breakthroughs in self-supervised learning show that such algorith...
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12/17/2020
MASKER: Masked Keyword Regularization for Reliable Text Classification
Pre-trained language models have achieved state-of-the-art accuracies on...
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10/26/2020
Provable Memorization via Deep Neural Networks using Sub-linear Parameters
It is known that Θ(N) parameters are sufficient for neural networks to m...
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10/15/2020
A Deeper Look at the Layerwise Sparsity of Magnitude-based Pruning
Recent discoveries on neural network pruning reveal that, with a careful...
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07/06/2020
Learning from Failure: Training Debiased Classifier from Biased Classifier
Neural networks often learn to make predictions that overly rely on spur...
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06/16/2020
Minimum Width for Universal Approximation
The universal approximation property of width-bounded networks has been ...
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06/15/2020
Learning Bounds for Risk-sensitive Learning
In risk-sensitive learning, one aims to find a hypothesis that minimizes...
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02/12/2020
Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning
Magnitude-based pruning is one of the simplest methods for pruning neura...
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12/23/2018