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08/17/2023
How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?
Pruning has emerged as a powerful technique for compressing deep neural ...
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04/06/2023
Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling
Graph neural networks (GNNs) have been widely applied to learning over g...
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02/27/2023
Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations
Recently, both Contrastive Learning (CL) and Mask Image Modeling (MIM) d...
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01/01/2023
Sharper analysis of sparsely activated wide neural networks with trainable biases
This work studies training one-hidden-layer overparameterized ReLU netwo...
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10/26/2022
M^3ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design
Multi-task learning (MTL) encapsulates multiple learned tasks in a singl...
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08/29/2022
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study
Imaging exams, such as chest radiography, will yield a small set of comm...
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11/01/2021
Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling
Contrastive learning approaches have achieved great success in learning ...
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06/06/2021
Self-Damaging Contrastive Learning
The recent breakthrough achieved by contrastive learning accelerates the...
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10/26/2020
Robust Pre-Training by Adversarial Contrastive Learning
Recent work has shown that, when integrated with adversarial training, s...
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10/29/2019
E2-Train: Energy-Efficient Deep Network Training with Data-, Model-, and Algorithm-Level Saving
Convolutional neural networks (CNNs) have been increasingly deployed to ...
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06/01/2019
ArcticNet: A Deep Learning Solution to Classify Arctic Wetlands
Arctic environments are rapidly changing under the warming climate. Of p...
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05/15/2019
