
PruneNet: Channel Pruning via Global Importance
Channel pruning is one of the predominant approaches for accelerating de...
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schuBERT: Optimizing Elements of BERT
Transformers <cit.> have gradually become a key component for many state...
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Robust conditional GANs under missing or uncertain labels
Matching the performance of conditional Generative Adversarial Networks ...
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DARC: Differentiable ARchitecture Compression
In many learning situations, resources at inference time are significant...
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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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Robustness of Conditional GANs to Noisy Labels
We study the problem of learning conditional generators from noisy label...
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Learning From Noisy Singlylabeled Data
Supervised learning depends on annotated examples, which are taken to be...
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PacGAN: The power of two samples in generative adversarial networks
Generative adversarial networks (GANs) are innovative techniques for lea...
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Spectrum Estimation from a Few Entries
Singular values of a data in a matrix form provide insights on the struc...
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Computational and Statistical Tradeoffs in Learning to Rank
For massive and heterogeneous modern datasets, it is of fundamental inte...
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Achieving Budgetoptimality with Adaptive Schemes in Crowdsourcing
Crowdsourcing platforms provide marketplaces where task requesters can p...
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Datadriven Rank Breaking for Efficient Rank Aggregation
Rank aggregation systems collect ordinal preferences from individuals to...
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Ashish Khetan
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