Chintala, Soumith

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv

Parallel Computing and Optimization TechniquesComputational Physics and Python Applications

Cited by 31,473

Unsupervised representation learning with deep convolutional generative adversarial networks

Radford, A., Metz, L., & Chintala, S. (2015) · arXiv

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 12,235

Wasserstein GAN

Arjovsky, M., Chintala, S., & Bottou, L. (2017) · arXiv

Generative Adversarial Networks and Image SynthesisStochastic Gradient Optimization Techniques

Cited by 2,225

PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation

Ansel, J., Yang, E., He, H., Gimelshein, N., Jain, A., Voznesensky, M., Bao, B., Bell, P., Berard, D., Burovski, E., et al. (2024) · Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2

Parallel Computing and Optimization TechniquesLogic, programming, and type systems

Cited by 737

Semantic Segmentation using Adversarial Networks

Luc, P., Couprie, C., Chintala, S., & Verbeek, J. (2016) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 611

Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

Hazelwood, K., Bird, S., Brooks, D., Chintala, S., Diril, U., Dzhulgakov, D., Fawzy, M., Jia, B., Jia, Y., Kalro, A., et al. (2018) · 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA)

Cloud Computing and Resource ManagementParallel Computing and Optimization Techniques

Cited by 590

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

Vasilache, N., Johnson, J., Mathieu, M., Chintala, S., Piantino, S., & LeCun, Y. (2014) · arXiv

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 246

Discovering Causal Signals in Images

Lopez-Paz, D., Nishihara, R., Chintala, S., Scholkopf, B., & Bottou, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Bayesian Modeling and Causal InferenceImage Retrieval and Classification Techniques

Cited by 209

Navigating to objects in the real world

Gervet, T., Chintala, S., Batra, D., Malik, J., & Chaplot, D. S. (2023) · Science Robotics

Reinforcement Learning in RoboticsRobotics and Sensor-Based Localization

Cited by 125

A mathematical motivation for complex-valued convolutional networks

Tygert, M., Bruna, J., Chintala, S., LeCun, Y., Piantino, S., & Szlam, A. (2016) · Neural Computation

Mathematical Analysis and Transform MethodsNeural Networks and Applications

Cited by 88

Holo-Dex: Teaching Dexterity with Immersive Mixed Reality

Arunachalam, S. P., Güzey, I., Chintala, S., & Pinto, L. (2022) · arXiv

Robot Manipulation and LearningTeleoperation and Haptic Systems

Cited by 48

See to touch: Learning tactile dexterity through visual incentives

Guzey, I., Dai, Y., Evans, B., Chintala, S., & Pinto, L. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 26