Pratik Chaudhari

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Stefano SoattoProfessor of Computer Science, UCLAVerified email at ucla.edu
Emilio FrazzoliProfessor of Dynamic Systems and ControlVerified email at ethz.ch
Riccardo Zecchinaprofessor, theoretical physics, Bocconi UniversityVerified email at unibocconi.it
Carlo BaldassiBocconi University; INFNVerified email at unibocconi.it
Alex SmolaAmazon Web ServicesVerified email at smola.org
Rasool FakoorAmazon Web ServicesVerified email at mavs.uta.edu
Adam M. ObermanProfessor, Department of Mathematics and Statistics, McGill UniversityVerified email at mcgill.ca
Sertac KaramanAssociate Professor of Aeronautics and Astronautics, Massachusetts Institute of TechnologyVerified email at mit.edu
Anna ChoromanskaNew York UniversityVerified email at cims.nyu.edu
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityVerified email at cs.nyu.edu
Levent SagunFacebook AI ResearchVerified email at fb.com
Jennifer ChayesTechnical Fellow & Managing Director, MicrosoftVerified email at microsoft.com
Christian BorgsProfessor, UC BerkeleyVerified email at christianborgs.com
Avinash RavichandranAWSVerified email at cis.jhu.edu
S osherVerified email at math.ucla.edu
Guneet Singh DhillonAmazon Web ServicesVerified email at utexas.edu
Daniela RusAndrew (1956) and Erna Viterbi Professor of Computer Science, MITVerified email at csail.mit.edu
Jana TumovaKTHVerified email at kth.se
Jonas MuellerAmazon Web ServicesVerified email at csail.mit.edu
David HsuProfessor of Computer Science, National University of SingaporeVerified email at comp.nus.edu.sg

Pratik Chaudhari

University of Pennsylvania

Verified email at seas.upenn.edu - Homepage

Deep Learning Machine Learning Robotics Computer Vision


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Entropy-SGD: biasing gradient descent into wide valleys P Chaudhari, A Choromanska, S Soatto, Y LeCun, C Baldassi, C Borgs, ... ICLR, arXiv:1611.01838, 2017	412	2017
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks P Chaudhari, S Soatto ICLR, arXiv:1710.11029, 2018	186	2018
A Baseline for Few-Shot Image Classification GS Dhillon, P Chaudhari, A Ravichandran, S Soatto ICLR, arXiv:1909.02729, 2020	136	2020
Deep relaxation: partial differential equations for optimizing deep neural networks P Chaudhari, A Oberman, S Osher, S Soatto, G Carlier Research in the Mathematical Sciences 5 (3), 1-30, 2018	109	2018
Incremental sampling-based algorithm for minimum-violation motion planning LIR Castro, P Chaudhari, J Tůmová, S Karaman, E Frazzoli, D Rus CDC, 3217-3224, 2013	71	2013
Meta-Q-Learning R Fakoor, P Chaudhari, S Soatto, AJ Smola ICLR, arXiv:1910.00125, 2020	32	2020
Sampling-based algorithms for continuous-time POMDPs P Chaudhari, S Karaman, D Hsu, E Frazzoli ACC, 4604-4610, 2013	32	2013
Rethinking the Hyperparameters for Fine-tuning H Li, P Chaudhari, H Yang, M Lam, A Ravichandran, R Bhotika, S Soatto ICLR, arXiv:2002.11770, 2020	26	2020
On the energy landscape of deep networks P Chaudhari, S Soatto ICML Workshop on Optimization in Machine Learning, 2016	22*	2016
Parle: parallelizing stochastic gradient descent P Chaudhari, C Baldassi, R Zecchina, S Soatto, A Talwalkar, A Oberman SysML, arXiv:1707.00424, 2018	21	2018
Game theoretic controller synthesis for multi-robot motion planning Part I: Trajectory based algorithms M Zhu, M Otte, P Chaudhari, E Frazzoli ICRA, 1646-1651, 2014	21	2014
Sampling-based algorithms for optimal motion planning using process algebra specifications V Varricchio, P Chaudhari, E Frazzoli ICRA, 5326-5332, 2014	18	2014
P3O: Policy-on Policy-off Policy Optimization R Fakoor, P Chaudhari, AJ Smola UAI, arXiv:1905.01756, 2019	17	2019
Incremental minimum-violation control synthesis for robots interacting with external agents P Chaudhari, T Wongpiromsarny, E Frazzoli ACC, 1761-1768, 2014	10	2014
Sampling-based algorithm for filtering using Markov chain approximations P Chaudhari, S Karaman, E Frazzoli CDC, 5972-5978, 2012	7	2012
Directional adversarial training for cost sensitive deep learning classification applications M Terzi, GA Susto, P Chaudhari Engineering Applications of Artificial Intelligence 91, 103550, 2020	6	2020
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation R Fakoor, J Mueller, N Erickson, P Chaudhari, AJ Smola NeurIPS, arXiv:2006.14284, 2020	6	2020
TraDE: Transformers for density estimation R Fakoor, P Chaudhari, J Mueller, AJ Smola arXiv preprint arXiv:2004.02441, 2020	5	2020
A Free-Energy Principle for Representation Learning Y Gao, P Chaudhari ICML, arXiv:2002.12406, 2020	4	2020
A geometric interpretation of stochastic gradient descent using diffusion metrics R Fioresi, P Chaudhari, S Soatto Entropy 22 (1), 101, 2020	4	2020

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