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05/23/2022
Improving language models fine-tuning with representation consistency targets
Fine-tuning contextualized representations learned by pre-trained langua...
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03/27/2022
Pyramid-BERT: Reducing Complexity via Successive Core-set based Token Selection
Transformer-based language models such as BERT have achieved the state-o...
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12/11/2020
TabTransformer: Tabular Data Modeling Using Contextual Embeddings
We propose TabTransformer, a novel deep tabular data modeling architectu...
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05/22/2020
PruneNet: Channel Pruning via Global Importance
Channel pruning is one of the predominant approaches for accelerating de...
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05/09/2020
schuBERT: Optimizing Elements of BERT
Transformers <cit.> have gradually become a key component for many state...
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06/09/2019
Robust conditional GANs under missing or uncertain labels
Matching the performance of conditional Generative Adversarial Networks ...
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05/20/2019
DARC: Differentiable ARchitecture Compression
In many learning situations, resources at inference time are significant...
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12/01/2018
Number of Connected Components in a Graph: Estimation via Counting Patterns
Due to the limited resources and the scale of the graphs in modern datas...
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11/08/2018
Robustness of Conditional GANs to Noisy Labels
We study the problem of learning conditional generators from noisy label...
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12/13/2017
Learning From Noisy Singly-labeled Data
Supervised learning depends on annotated examples, which are taken to be...
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12/12/2017
PacGAN: The power of two samples in generative adversarial networks
Generative adversarial networks (GANs) are innovative techniques for lea...
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03/18/2017
Spectrum Estimation from a Few Entries
Singular values of a data in a matrix form provide insights on the struc...
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08/22/2016
Computational and Statistical Tradeoffs in Learning to Rank
For massive and heterogeneous modern datasets, it is of fundamental inte...
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02/10/2016
Achieving Budget-optimality with Adaptive Schemes in Crowdsourcing
Crowdsourcing platforms provide marketplaces where task requesters can p...
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01/21/2016
