Devansh Arpit

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 46

Top Topics

Independently And Identically Distributed

Covariate Shift

Subspace Learning

Top Venues

Publications

Itai Feigenbaum, Devansh Arpit, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese
Causal Layering via Conditional Entropy. CoRR (2024)
Itai Feigenbaum, Devansh Arpit, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese, Huan Wang
Causal Layering via Conditional Entropy. CLeaR (2024)
Itai Feigenbaum, Devansh Arpit, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese
Editing Arbitrary Propositions in LLMs without Subject Labels. CoRR (2024)
Weiran Yao, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu, Yihao Feng, Le Xue, Rithesh R. N., Zeyuan Chen, Jianguo Zhang, Devansh Arpit, Ran Xu, Phil Mui, Huan Wang, Caiming Xiong, Silvio Savarese
Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization. ICLR (2024)
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
Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization. CoRR (2023)
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
REX: Rapid Exploration and eXploitation for AI Agents. CoRR (2023)
Itai Feigenbaum, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Devansh Arpit
On the Unlikelihood of D-Separation. CoRR (2023)
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
BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents. CoRR (2023)
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
Merlion: End-to-End Machine Learning for Time Series. J. Mach. Learn. Res. 24 (2023)
Devansh Arpit, Matthew Fernandez, Chenghao Liu, Weiran Yao, Wenzhuo Yang, Paul Josel, Shelby Heinecke, Eric Hu, Huan Wang, Stephen Hoi, Caiming Xiong, Kun Zhang, Juan Carlos Niebles
Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data. CoRR (2023)
Devansh Arpit, Huan Wang, Yingbo Zhou, Caiming Xiong
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization. NeurIPS (2022)
Stanislaw Jastrzebski, Devansh Arpit, Oliver Åstrand, Giancarlo Kerg, Huan Wang, Caiming Xiong, Richard Socher, Kyunghyun Cho, Krzysztof J. Geras
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization. ICML (2021)
Devansh Arpit, Aadyot Bhatnagar, Huan Wang, Caiming Xiong
Momentum Contrastive Autoencoder: Using Contrastive Learning for Latent Space Distribution Matching in WAE. CoRR (2021)
Devansh Arpit, Huan Wang, Yingbo Zhou, Caiming Xiong
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization. CoRR (2021)
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
Merlion: A Machine Learning Library for Time Series. CoRR (2021)
Bram Wallace, Devansh Arpit, Huan Wang, Caiming Xiong
Learning Rich Nearest Neighbor Representations from Self-supervised Ensembles. CoRR (2021)
Stanislaw Jastrzebski, Devansh Arpit, Oliver Åstrand, Giancarlo Kerg, Huan Wang, Caiming Xiong, Richard Socher, Kyunghyun Cho, Krzysztof J. Geras
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization. CoRR (2020)
Stanislaw Jastrzebski, Maciej Szymczak, Stanislav Fort, Devansh Arpit, Jacek Tabor, Kyunghyun Cho, Krzysztof J. Geras
The Break-Even Point on Optimization Trajectories of Deep Neural Networks. CoRR (2020)
Devansh Arpit, Huan Wang, Caiming Xiong, Richard Socher, Yoshua Bengio
Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning. CoRR (2020)
Stanislaw Jastrzebski, Maciej Szymczak, Stanislav Fort, Devansh Arpit, Jacek Tabor, Kyunghyun Cho, Krzysztof J. Geras
The Break-Even Point on Optimization Trajectories of Deep Neural Networks. ICLR (2020)
Devansh Arpit, Víctor Campos, Yoshua Bengio
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets. NeurIPS (2019)
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville
On the Spectral Bias of Neural Networks. ICML (2019)
Devansh Arpit, Caiming Xiong, Richard Socher
Entropy Penalty: Towards Generalization Beyond the IID Assumption. CoRR (2019)
Devansh Arpit, Victor Campos, Yoshua Bengio
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets. CoRR (2019)
Devansh Arpit, Yoshua Bengio
The Benefits of Over-parameterization at Initialization in Deep ReLU Networks. CoRR (2019)
Bhargav Kanuparthi, Devansh Arpit, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio
h-detach: Modifying the LSTM Gradient Towards Better Optimization. ICLR (Poster) (2019)
Chen Xing, Devansh Arpit, Christos Tsirigotis, Yoshua Bengio
A Walk with SGD. CoRR (2018)
Nasim Rahaman, Devansh Arpit, Aristide Baratin, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville
On the Spectral Bias of Deep Neural Networks. CoRR (2018)
Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey
Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio. ICANN (3) (2018)
Konrad Zolna, Devansh Arpit, Dendi Suhubdy, Yoshua Bengio
Fraternal Dropout. ICLR (Poster) (2018)
Stanislaw Jastrzebski, Devansh Arpit, Nicolas Ballas, Vikas Verma, Tong Che, Yoshua Bengio
Residual Connections Encourage Iterative Inference. ICLR (Poster) (2018)
Devansh Arpit, Bhargav Kanuparthi, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio
h-detach: Modifying the LSTM Gradient Towards Better Optimization. CoRR (2018)
Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey
Finding Flatter Minima with SGD. ICLR (Workshop) (2018)
Konrad Zolna, Devansh Arpit, Dendi Suhubdy, Yoshua Bengio
Fraternal Dropout. CoRR (2017)
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
A Closer Look at Memorization in Deep Networks. ICML (2017)
Stanislaw Jastrzebski, Devansh Arpit, Nicolas Ballas, Vikas Verma, Tong Che, Yoshua Bengio
Residual Connections Encourage Iterative Inference. CoRR (2017)
Neeti Narayan, Nishant Sankaran, Devansh Arpit, Karthik Dantu, Srirangaraj Setlur, Venu Govindaraju
Person Re-identification for Improved Multi-person Multi-camera Tracking by Continuous Entity Association. CVPR Workshops (2017)
Samira Shabanian, Devansh Arpit, Adam Trischler, Yoshua Bengio
Variational Bi-LSTMs. CoRR (2017)
David Krueger, Nicolas Ballas, Stanislaw Jastrzebski, Devansh Arpit, Maxinder S. Kanwal, Tegan Maharaj, Emmanuel Bengio, Asja Fischer, Aaron C. Courville
Deep Nets Don't Learn via Memorization. ICLR (Workshop) (2017)
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
A Closer Look at Memorization in Deep Networks. CoRR (2017)
Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey
Three Factors Influencing Minima in SGD. CoRR (2017)
Devansh Arpit, Yingbo Zhou, Hung Q. Ngo, Venu Govindaraju
Why Regularized Auto-Encoders learn Sparse Representation? ICML (2016)
Devansh Arpit, Yingbo Zhou, Bhargava Urala Kota, Venu Govindaraju
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks. ICML (2016)
Devansh Arpit, Chetan Ramaiah, Venu Govindaraju
Subspace learning via low rank projections for dimensionality reduction. BTAS (2016)
Devansh Arpit, Yingbo Zhou, Bhargava Urala Kota, Venu Govindaraju
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks. CoRR (2016)
Devansh Arpit, Hung Q. Ngo, Yingbo Zhou, Nils Napp, Venu Govindaraju
Towards Optimality Conditions for Non-Linear Networks. CoRR (2016)
Devansh Arpit, Yingbo Zhou, Hung Q. Ngo, Venu Govindaraju
Why Regularized Auto-Encoders learn Sparse Representation? CoRR (2015)
Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju
Dimensionality Reduction with Subspace Structure Preservation. NIPS (2014)
Devansh Arpit, Gaurav Srivastava, Venu Govindaraju
Randomized Subspace Learning Algorithms with Subspace Structure Preservation Guarantees. CoRR (2014)
Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju
Dimensionality Reduction with Subspace Structure Preservation. CoRR (2014)
Devansh Arpit, Shuang Wu, Pradeep Natarajan, Rohit Prasad, Premkumar Natarajan
Ridge Regression based classifiers for large scale class imbalanced datasets. WACV (2013)
Devansh Arpit, Gaurav Srivastava, Yun Fu
Locality-constrained Low Rank Coding for face recognition. ICPR (2012)
Devansh Arpit, Anoop M. Namboodiri
Fingerprint feature extraction from gray scale images by ridge tracing. IJCB (2011)