no code implementations • 27 Oct 2023 • Nicholas E. Charron, Felix Musil, Andrea Guljas, Yaoyi Chen, Klara Bonneau, Aldo S. Pasos-Trejo, Jacopo Venturin, Daria Gusew, Iryna Zaporozhets, Andreas Krämer, Clark Templeton, Atharva Kelkar, Aleksander E. P. Durumeric, Simon Olsson, Adrià Pérez, Maciej Majewski, Brooke E. Husic, Ankit Patel, Gianni de Fabritiis, Frank Noé, Cecilia Clementi
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost.
no code implementations • 17 Mar 2022 • R. James Cotton, Emoonah McClerklin, Anthony Cimorelli, Ankit Patel, Tasos Karakostas
Human pose estimation from monocular video is a rapidly advancing field that offers great promise to human movement science and rehabilitation.
no code implementations • 29 Sep 2021 • R. James Cotton, Emoonah McClerklin, Anthony Cimorelli, Ankit Patel
Using more than 9000 monocular video from an instrumented gait analysis lab, we evaluated the performance of existing algorithms for measuring kinematics.
no code implementations • 4 Aug 2020 • Justin Sahs, Ryan Pyle, Aneel Damaraju, Josue Ortega Caro, Onur Tavaslioglu, Andy Lu, Ankit Patel
Our implicit regularization results are complementary to recent work arXiv:1906. 07842, done independently, which showed that initialization scale critically controls implicit regularization via a kernel-based argument.
no code implementations • 19 Jun 2020 • Josue Ortega Caro, Yilong Ju, Ryan Pyle, Sourav Dey, Wieland Brendel, Fabio Anselmi, Ankit Patel
Inspired by theoretical work on linear full-width convolutional models, we hypothesize that the local (i. e. bounded-width) convolutional operations commonly used in current neural networks are implicitly biased to learn high frequency features, and that this is one of the root causes of high frequency adversarial examples.
no code implementations • 25 Sep 2019 • Justin Sahs, Aneel Damaraju, Ryan Pyle, Onur Tavaslioglu, Josue Ortega Caro, Hao Yang Lu, Ankit Patel
Despite their popularity and successes, deep neural networks are poorly understood theoretically and treated as 'black box' systems.
no code implementations • 25 Sep 2019 • Alan Lockett, Ankit Patel, Paul Pfaffinger
We also demonstrate for the first time that a network with excitatory and inhibitory neurons and nonnegative synapse strengths can successfully solve computer vision problems.
1 code implementation • 26 May 2019 • Li Yang, Zhaoqi Leng, Guangyuan Yu, Ankit Patel, Wen-Jun Hu, Han Pu
Artificial neural networks have been successfully incorporated into variational Monte Carlo method (VMC) to study quantum many-body systems.
Strongly Correlated Electrons Disordered Systems and Neural Networks
1 code implementation • ICLR 2019 • Weili Nie, Nina Narodytska, Ankit Patel
Generative adversarial networks (GANs) have achieved great success at generating realistic images.
Ranked #3 on Text Generation on EMNLP2017 WMT
no code implementations • 1 Nov 2018 • Tan Nguyen, Nhat Ho, Ankit Patel, Anima Anandkumar, Michael. I. Jordan, Richard G. Baraniuk
This conjugate prior yields a new regularizer based on paths rendered in the generative model for training CNNs-the Rendering Path Normalization (RPN).
1 code implementation • 24 Jun 2018 • Weili Nie, Ankit Patel
Generative adversarial networks (GANs) are notoriously difficult to train and the reasons underlying their (non-)convergence behaviors are still not completely understood.
1 code implementation • ICML 2018 • Weili Nie, Yang Zhang, Ankit Patel
Backpropagation-based visualizations have been proposed to interpret convolutional neural networks (CNNs), however a theory is missing to justify their behaviors: Guided backpropagation (GBP) and deconvolutional network (DeconvNet) generate more human-interpretable but less class-sensitive visualizations than saliency map.
no code implementations • 16 May 2018 • Wanjia Liu, Huaijin Chen, Rishab Goel, Yuzhong Huang, Ashok Veeraraghavan, Ankit Patel
Good temporal representations are crucial for video understanding, and the state-of-the-art video recognition framework is based on two-stream networks.
no code implementations • 25 Dec 2017 • Romain Cosentino, Randall Balestriero, Richard Baraniuk, Ankit Patel
In this work, we derive a generic overcomplete frame thresholding scheme based on risk minimization.
2 code implementations • 6 May 2016 • Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein
With the growing importance of large network models and enormous training datasets, GPUs have become increasingly necessary to train neural networks.