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06/09/2023
End-to-End Neural Network Compression via ℓ_1/ℓ_2 Regularized Latency Surrogates
Neural network (NN) compression via techniques such as pruning, quantiza...
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10/04/2022
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks
Deep Neural Networks are known to be brittle to even minor distribution ...
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08/19/2022
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization
We consider the problem of OOD generalization, where the goal is to trai...
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08/15/2021
Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time
In several real world applications, machine learning models are deployed...
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05/21/2021
Rule Augmented Unsupervised Constituency Parsing
Recently, unsupervised parsing of syntactic trees has gained considerabl...
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09/29/2020
What if Neural Networks had SVDs?
Various Neural Networks employ time-consuming matrix operations like mat...
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06/25/2020
