no code implementations • 2 May 2024 • Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, SaiKrishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampášek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin Segler, Michael Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge.
1 code implementation • 30 Nov 2023 • Chuanrui Wang, Bozitao Zhong, Zuobai Zhang, Narendra Chaudhary, Sanchit Misra, Jian Tang
Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years.
1 code implementation • NeurIPS 2023 • Yangtian Zhang, Zuobai Zhang, Bozitao Zhong, Sanchit Misra, Jian Tang
In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space.
no code implementations • 22 Dec 2022 • Narendra Chaudhary, Alexander Pivovar, Pavel Yakovlev, Andrey Gorshkov, Sanchit Misra
t-SNE remains one of the most popular embedding techniques for visualizing high-dimensional data.
no code implementations • 11 Nov 2022 • Md Vasimuddin, Ramanarayan Mohanty, Sanchit Misra, Sasikanth Avancha
DistGNN-MB trains GraphSAGE and GAT 10x and 17. 2x faster, respectively, as compute nodes scale from 2 to 32.
1 code implementation • 16 Apr 2021 • Narendra Chaudhary, Sanchit Misra, Dhiraj Kalamkar, Alexander Heinecke, Evangelos Georganas, Barukh Ziv, Menachem Adelman, Bharat Kaul
Finally, we demonstrate the performance of our optimized 1D convolution layer by utilizing it in the end-to-end neural network training with real genomics datasets and achieve up to 6. 86x speedup over the oneDNN library-based implementation on Cascade Lake CPUs.
no code implementations • 14 Apr 2021 • Vasimuddin Md, Sanchit Misra, Guixiang Ma, Ramanarayan Mohanty, Evangelos Georganas, Alexander Heinecke, Dhiraj Kalamkar, Nesreen K. Ahmed, Sasikanth Avancha
Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible.
3 code implementations • 12 Apr 2021 • Evangelos Georganas, Dhiraj Kalamkar, Sasikanth Avancha, Menachem Adelman, Deepti Aggarwal, Cristina Anderson, Alexander Breuer, Jeremy Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Brian Retford, Barukh Ziv, Alexander Heinecke
The TPP specification is platform-agnostic, thus code expressed via TPPs is portable, whereas the TPP implementation is highly-optimized and platform-specific.
1 code implementation • 13 Jul 2020 • Sasikanth Avancha, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty
The Deep Graph Library (DGL) was designed as a tool to enable structure learning from graphs, by supporting a core abstraction for graphs, including the popular Graph Neural Networks (GNN).
no code implementations • 10 Oct 2019 • Darryl Ho, Jialin Ding, Sanchit Misra, Nesime Tatbul, Vikram Nathan, Vasimuddin Md, Tim Kraska
Next-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics.