Devansh Arpit

According to our database1, Devansh Arpit authored at least 27 papers between 2011 and 2020.

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Bibliography

2020
Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning.
CoRR, 2020

The Break-Even Point on Optimization Trajectories of Deep Neural Networks.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Entropy Penalty: Towards Generalization Beyond the IID Assumption.
CoRR, 2019

The Benefits of Over-parameterization at Initialization in Deep ReLU Networks.
CoRR, 2019

How to Initialize your Network? Robust Initialization for WeightNorm & ResNets.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the Spectral Bias of Neural Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

h-detach: Modifying the LSTM Gradient Towards Better Optimization.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
On the Spectral Bias of Deep Neural Networks.
CoRR, 2018

A Walk with SGD.
CoRR, 2018

Fraternal Dropout.
Proceedings of the 6th International Conference on Learning Representations, 2018

Finding Flatter Minima with SGD.
Proceedings of the 6th International Conference on Learning Representations, 2018

Residual Connections Encourage Iterative Inference.
Proceedings of the 6th International Conference on Learning Representations, 2018

Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

2017
Variational Bi-LSTMs.
CoRR, 2017

Three Factors Influencing Minima in SGD.
CoRR, 2017

A Closer Look at Memorization in Deep Networks.
Proceedings of the 34th International Conference on Machine Learning, 2017

Deep Nets Don't Learn via Memorization.
Proceedings of the 5th International Conference on Learning Representations, 2017

Person Re-identification for Improved Multi-person Multi-camera Tracking by Continuous Entity Association.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017

2016
Towards Optimality Conditions for Non-Linear Networks.
CoRR, 2016

Why Regularized Auto-Encoders learn Sparse Representation?
Proceedings of the 33nd International Conference on Machine Learning, 2016

Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Subspace learning via low rank projections for dimensionality reduction.
Proceedings of the 8th IEEE International Conference on Biometrics Theory, 2016

2014
Randomized Subspace Learning Algorithms with Subspace Structure Preservation Guarantees.
CoRR, 2014

Dimensionality Reduction with Subspace Structure Preservation.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013
Ridge Regression based classifiers for large scale class imbalanced datasets.
Proceedings of the 2013 IEEE Workshop on Applications of Computer Vision, 2013

2012
Locality-constrained Low Rank Coding for face recognition.
Proceedings of the 21st International Conference on Pattern Recognition, 2012

2011
Fingerprint feature extraction from gray scale images by ridge tracing.
Proceedings of the 2011 IEEE International Joint Conference on Biometrics, 2011


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