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10/15/2021
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
Various pruning approaches have been proposed to reduce the footprint re...
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04/18/2021
Rethinking Network Pruning – under the Pre-train and Fine-tune Paradigm
Transformer-based pre-trained language models have significantly improve...
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04/12/2020
Minimizing FLOPs to Learn Efficient Sparse Representations
Deep representation learning has become one of the most widely adopted a...
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11/23/2018
Representer Point Selection for Explaining Deep Neural Networks
We propose to explain the predictions of a deep neural network, by point...
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10/30/2018
Word Mover's Embedding: From Word2Vec to Document Embedding
While the celebrated Word2Vec technique yields semantically rich represe...
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10/10/2018
Learning Tensor Latent Features
We study the problem of learning latent feature models (LFMs) for tensor...
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09/14/2018
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability
Kernel method has been developed as one of the standard approaches for n...
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06/27/2014