Pushmeet Kohli
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Pushmeet Kohli is a person.
- See: Verifiable AI, Probabilistic Programming, Machine Learning Systems for Healthcare, Interpretable AI, Explainable AI, Relational Inductive Bias, AlphaFold System, Sorting Algorithm.
References
2024
- (Zambaldi et al., 2024) ⇒ Vinicius Zambaldi, David La, Alexander E. Chu, Harshnira Patani, Amy E. Danson, Tristan O. C. Kwan, Thomas Frerix, Rosalia G. Schneider, David Saxton, Ashok Thillaisundaram, Zachary Wu, Isabel Moraes, Oskar Lange, Eliseo Papa, Gabriella Stanton, Victor Martin, Sukhdeep Singh, Lai H. Wong, Russ Bates, Simon A. Kohl, Josh Abramson, Andrew W. Senior, Yilmaz Alguel, Mary Y. Wu, Irene M. Aspalter, Katie Bentley, David L. V. Bauer, Peter Cherepanov, Demis Hassabis, Pushmeet Kohli, Rob Fergus, and Jue Wang. (2024). “De Novo Design of High-affinity Protein Binders with AlphaProteo.”
- (Romera-Paredes et al., 2024) ⇒ Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M. Pawan Kumar, Emilien Dupont, Francisco J. R. Ruiz, Jordan S. Ellenberg, Pengming Wang, Omar Fawzi, Pushmeet Kohli, and Alhussein Fawzi. (2024). “Mathematical Discoveries from Program Search with Large Language Models.” In: Nature, 625(7995). doi:10.1038/s41586-023-06924-6
2023
- (Mankowitz et al., 2023) ⇒ Daniel J. Mankowitz, Andrea Michi, Anton Zhernov, Marco Gelmi, Marco Selvi, Cosmin Paduraru, Edouard Leurent, Shariq Iqbal, Jean-Baptiste Lespiau, Alex Ahern, Thomas Köppe, Kevin Millikin, Stephen Gaffney, Sophie Elster, Jackson Broshear, Chris Gamble, Kieran Milan, Robert Tung, Minjae Hwang, Taylan Cemgil, Mohammadamin Barekatain, Yujia Li, Amol Mandhane, Thomas Hubert, Julian Schrittwieser, Demis Hassabis, Pushmeet Kohli, Martin Riedmiller, Oriol Vinyals, and David Silver. (2023). “Faster Sorting Algorithms Discovered Using Deep Reinforcement Learning.” In: Nature, 618(7964).
2022
- (Varadi et al., 2022) ⇒ Mihaly Varadi, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, Oana Stroe, Gemma Wood, Agata Laydon, Augustin Žídek, Tim Green, Kathryn Tunyasuvunakool, Stig Petersen, John Jumper, Ellen Clancy, Richard Green, Ankur Vora, Mira Lutfi, Michael Figurnov, Andrew Cowie, Nicole Hobbs, Pushmeet Kohli, Gerard Kleywegt, Ewan Birney, Demis Hassabis, and Sameer Velankar. (2022). "AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.” In: Nucleic Acids Research, Volume 50, Issue D1, Pages D439-D444. [doi:10.1093/nar/gkab1061](https://academic.oup.com/nar/article/50/D1/D439/6414273)
- NOTE: It describes the AlphaFold Protein Structure Database and its impact on protein structure research.
2021
- (Jumper et al., 2021) ⇒ John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis. (2021). "Highly accurate protein structure prediction with AlphaFold.” In: Nature, Volume 596, Issue 7873, Pages 583-589. [doi:10.1038/s41586-021-03819-2](https://www.nature.com/articles/s41586-021-03819-2)
- NOTE: It discusses how AlphaFold enhances protein structure prediction accuracy using advanced neural network techniques.
2018
- (Battaglia et al., 2018) ⇒ Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu. (2018). "Relational inductive biases, deep learning, and graph networks.” In: arXiv preprint arXiv:1806.01261. arXiv:1806.01261
- NOTE: It presents the concept of graph networks and their role in enhancing deep learning models with relational inductive biases.
2012
- (Silberman et al., 2012) ⇒ Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. (2012). "Indoor segmentation and support inference from RGB-D images.” In: Proceedings of the 12th European Conference on Computer Vision (ECCV). [doi:10.1007/978-3-642-33715-4_54](https://link.springer.com/chapter/10.1007/978-3-642-33715-4_54)
- NOTE: It explores techniques for segmenting indoor environments and identifying support structures using RGB-D imagery.
2011
- (Newcombe et al., 2011) ⇒ Richard A. Newcombe, Shahram Izadi, Otmar Hilliges, David Molyneaux, David Kim, Andrew J. Davison, Pushmeet Kohli, Jamie Shotton, Steve Hodges, and Andrew Fitzgibbon. (2011). "KinectFusion: Real-time dense surface mapping and tracking.” In: 2011 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Pages 127-136. [doi:10.1109/ISMAR.2011.6092378](https://ieeexplore.ieee.org/document/6092378)
- NOTE: It details a method for real-time dense surface mapping and tracking using Kinect sensor data.