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Detection of Bad PMU Data using Machine Learning Techniques

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Detection of Bad PMU Data using Machine Learning Techniques

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From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks.

Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

Controlling spatiotemporal nonlinearities in multimode fibers with deep neural networks

Incremental Learning Meets Reduced Precision Networks

The committee machine: computational to statistical gaps in learning a two-layers neural network

Pretraining boosts out-of-domain robustness for pose estimation

Learning embeddings: efficient algorithms and applications

Learning embeddings: efficient algorithms and applications

From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks.

Controlling spatiotemporal nonlinearities in multimode fibers with deep neural networks

Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

The committee machine: computational to statistical gaps in learning a two-layers neural network

Learning embeddings: efficient algorithms and applications

Pretraining boosts out-of-domain robustness for pose estimation

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

Learning embeddings: efficient algorithms and applications

Incremental Learning Meets Reduced Precision Networks