Devansh Arpit

According to our database, Devansh Arpit authored at least 40 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Causal Layering via Conditional Entropy.

Itai Feigenbaum, Devansh Arpit, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese

CoRR, 2024

Editing Arbitrary Propositions in LLMs without Subject Labels.

Itai Feigenbaum, Devansh Arpit, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese

CoRR, 2024

2023

Merlion: End-to-End Machine Learning for Time Series.

Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu, Tian Lan, Wenzhuo Yang, Rowan Cassius, Doyen Sahoo, Devansh Arpit, Sri Subramanian, Gerald Woo, Amrita Saha, Arun Kumar Jagota, Gokulakrishnan Gopalakrishnan, Manpreet Singh, K. C. Krithika, Sukumar Maddineni, Dae-ki Cho, Bo Zong, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Steven C. H. Hoi, Huan Wang

J. Mach. Learn. Res., 2023

BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents.

Zhiwei Liu, Weiran Yao, Jianguo Zhang, Le Xue, Shelby Heinecke, Rithesh Murthy, Yihao Feng, Zeyuan Chen, Juan Carlos Niebles, Devansh Arpit, Ran Xu, Phil Mui, Huan Wang, Caiming Xiong, Silvio Savarese

CoRR, 2023

Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization.

Weiran Yao, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu, Yihao Feng, Le Xue, Rithesh Murthy, Zeyuan Chen, Jianguo Zhang, Devansh Arpit, Ran Xu, Phil Mui, Huan Wang, Caiming Xiong, Silvio Savarese

CoRR, 2023

REX: Rapid Exploration and eXploitation for AI Agents.

Rithesh Murthy, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu, Le Xue, Weiran Yao, Yihao Feng, Zeyuan Chen, Akash Gokul, Devansh Arpit, Ran Xu, Phil Mui, Huan Wang, Caiming Xiong, Silvio Savarese

CoRR, 2023

On the Unlikelihood of D-Separation.

Itai Feigenbaum, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Devansh Arpit

CoRR, 2023

Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data.

Devansh Arpit, Matthew Fernandez, Chenghao Liu, Weiran Yao, Wenzhuo Yang, Paul Josel, Shelby Heinecke, Eric Hu, Huan Wang, Stephen C. H. Hoi, Caiming Xiong, Kun Zhang, Juan Carlos Niebles

CoRR, 2023

2022

Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization.

Devansh Arpit, Huan Wang, Yingbo Zhou, Caiming Xiong

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Momentum Contrastive Autoencoder: Using Contrastive Learning for Latent Space Distribution Matching in WAE.

Devansh Arpit, Aadyot Bhatnagar, Huan Wang, Caiming Xiong

CoRR, 2021

Learning Rich Nearest Neighbor Representations from Self-supervised Ensembles.

Bram Wallace, Devansh Arpit, Huan Wang, Caiming Xiong

CoRR, 2021

Merlion: A Machine Learning Library for Time Series.

Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu, Tian Lan, Wenzhuo Yang, Rowan Cassius, Doyen Sahoo, Devansh Arpit, Sri Subramanian, Gerald Woo, Amrita Saha, Arun Kumar Jagota, Gokulakrishnan Gopalakrishnan, Manpreet Singh, K. C. Krithika, Sukumar Maddineni, Dae-ki Cho, Bo Zong, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Steven C. H. Hoi, Huan Wang

CoRR, 2021

Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization.

Stanislaw Jastrzebski, Devansh Arpit, Oliver Åstrand, Giancarlo Kerg, Huan Wang, Caiming Xiong, Richard Socher, Kyunghyun Cho, Krzysztof J. Geras

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

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

Devansh Arpit, Huan Wang, Caiming Xiong, Richard Socher, Yoshua Bengio

CoRR, 2020

The Break-Even Point on Optimization Trajectories of Deep Neural Networks.

Stanislaw Jastrzebski, Maciej Szymczak, Stanislav Fort, Devansh Arpit, Jacek Tabor, Kyunghyun Cho, Krzysztof J. Geras

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Entropy Penalty: Towards Generalization Beyond the IID Assumption.

Devansh Arpit, Caiming Xiong, Richard Socher

CoRR, 2019

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

Devansh Arpit, Yoshua Bengio

CoRR, 2019

How to Initialize your Network? Robust Initialization for WeightNorm & ResNets.

Devansh Arpit, Víctor Campos, Yoshua Bengio

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.

Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville

Proceedings of the 36th International Conference on Machine Learning, 2019

h-detach: Modifying the LSTM Gradient Towards Better Optimization.

Bhargav Kanuparthi, Devansh Arpit, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

On the Spectral Bias of Deep Neural Networks.

Nasim Rahaman, Devansh Arpit, Aristide Baratin, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville

CoRR, 2018

A Walk with SGD.

Chen Xing, Devansh Arpit, Christos Tsirigotis, Yoshua Bengio

CoRR, 2018

Fraternal Dropout.

Konrad Zolna, Devansh Arpit, Dendi Suhubdy, Yoshua Bengio

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

Finding Flatter Minima with SGD.

Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey

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

Residual Connections Encourage Iterative Inference.

Stanislaw Jastrzebski, Devansh Arpit, Nicolas Ballas, Vikas Verma, Tong Che, Yoshua Bengio

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.

Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

2017

Variational Bi-LSTMs.

Samira Shabanian, Devansh Arpit, Adam Trischler, Yoshua Bengio

CoRR, 2017

Three Factors Influencing Minima in SGD.

Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey

CoRR, 2017

A Closer Look at Memorization in Deep Networks.

Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, Simon Lacoste-Julien

Proceedings of the 34th International Conference on Machine Learning, 2017

Deep Nets Don't Learn via Memorization.

David Krueger, Nicolas Ballas, Stanislaw Jastrzebski, Devansh Arpit, Maxinder S. Kanwal, Tegan Maharaj, Emmanuel Bengio, Asja Fischer, Aaron C. Courville

Proceedings of the 5th International Conference on Learning Representations, 2017

Person Re-identification for Improved Multi-person Multi-camera Tracking by Continuous Entity Association.

Neeti Narayan, Nishant Sankaran, Devansh Arpit, Karthik Dantu, Srirangaraj Setlur, Venu Govindaraju

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017

2016

Towards Optimality Conditions for Non-Linear Networks.

Devansh Arpit, Hung Q. Ngo, Yingbo Zhou, Nils Napp, Venu Govindaraju

CoRR, 2016

Why Regularized Auto-Encoders learn Sparse Representation?

Devansh Arpit, Yingbo Zhou, Hung Q. Ngo, Venu Govindaraju

Proceedings of the 33nd International Conference on Machine Learning, 2016

Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks.

Devansh Arpit, Yingbo Zhou, Bhargava Urala Kota, Venu Govindaraju

Proceedings of the 33nd International Conference on Machine Learning, 2016

Subspace learning via low rank projections for dimensionality reduction.

Devansh Arpit, Chetan Ramaiah, Venu Govindaraju

Proceedings of the 8th IEEE International Conference on Biometrics Theory, 2016

2014

Randomized Subspace Learning Algorithms with Subspace Structure Preservation Guarantees.

Devansh Arpit, Gaurav Srivastava, Venu Govindaraju

CoRR, 2014

Dimensionality Reduction with Subspace Structure Preservation.

Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju

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.

Devansh Arpit, Shuang Wu, Pradeep Natarajan, Rohit Prasad, Premkumar Natarajan

Proceedings of the 2013 IEEE Workshop on Applications of Computer Vision, 2013

2012

Locality-constrained Low Rank Coding for face recognition.

Devansh Arpit, Gaurav Srivastava, Yun Fu

Proceedings of the 21st International Conference on Pattern Recognition, 2012

2011

Fingerprint feature extraction from gray scale images by ridge tracing.

Devansh Arpit, Anoop M. Namboodiri

Proceedings of the 2011 IEEE International Joint Conference on Biometrics, 2011