Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Misra, Sanchit"'
Autor:
Korablyov, Maksym, Liu, Cheng-Hao, Jain, Moksh, van der Sloot, Almer M., Jolicoeur, Eric, Ruediger, Edward, Nica, Andrei Cristian, Bengio, Emmanuel, Lapchevskyi, Kostiantyn, St-Cyr, Daniel, Schuetz, Doris Alexandra, Butoi, Victor Ion, Rector-Brooks, Jarrid, Blackburn, Simon, Feng, Leo, Nekoei, Hadi, Gottipati, SaiKrishna, Vijayan, Priyesh, Gupta, Prateek, Rampášek, Ladislav, Avancha, Sasikanth, Bacon, Pierre-Luc, Hamilton, William L., Paige, Brooks, Misra, Sanchit, Jastrzebski, Stanislaw Kamil, Kaul, Bharat, Precup, Doina, Hernández-Lobato, José Miguel, Segler, Marwin, Bronstein, Michael, Marinier, Anne, Tyers, Mike, Bengio, Yoshua
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. We introduce LambdaZero, a generative active lear
Externí odkaz:
http://arxiv.org/abs/2405.01616
Autor:
Wang, Chuanrui, Zhong, Bozitao, Zhang, Zuobai, Chaudhary, Narendra, Misra, Sanchit, Tang, Jian
Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years. However, a universally accepted method for evaluation has not been established, since the wet-lab validation can be overly time-
Externí odkaz:
http://arxiv.org/abs/2312.00080
Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. Accurately predicting the conformation of protein side-chains given their backbones is important for application
Externí odkaz:
http://arxiv.org/abs/2306.01794
t-SNE remains one of the most popular embedding techniques for visualizing high-dimensional data. Most standard packages of t-SNE, such as scikit-learn, use the Barnes-Hut t-SNE (BH t-SNE) algorithm for large datasets. However, existing CPU implement
Externí odkaz:
http://arxiv.org/abs/2212.11506
Training Graph Neural Networks, on graphs containing billions of vertices and edges, at scale using minibatch sampling poses a key challenge: strong-scaling graphs and training examples results in lower compute and higher communication volume and pot
Externí odkaz:
http://arxiv.org/abs/2211.06385
Autor:
Chaudhary, Narendra, Misra, Sanchit, Kalamkar, Dhiraj, Heinecke, Alexander, Georganas, Evangelos, Ziv, Barukh, Adelman, Menachem, Kaul, Bharat
Convolutional neural networks (CNNs) have found many applications in tasks involving two-dimensional (2D) data, such as image classification and image processing. Therefore, 2D convolution layers have been heavily optimized on CPUs and GPUs. However,
Externí odkaz:
http://arxiv.org/abs/2104.08002
Autor:
Md, Vasimuddin, Misra, Sanchit, Ma, Guixiang, Mohanty, Ramanarayan, Georganas, Evangelos, Heinecke, Alexander, Kalamkar, Dhiraj, Ahmed, Nesreen K., Avancha, Sasikanth
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. It is challenging due to large memory capacity and bandwidth requirements
Externí odkaz:
http://arxiv.org/abs/2104.06700
Autor:
Georganas, Evangelos, Kalamkar, Dhiraj, Avancha, Sasikanth, Adelman, Menachem, Aggarwal, Deepti, Anderson, Cristina, Breuer, Alexander, Bruestle, Jeremy, Chaudhary, Narendra, Kundu, Abhisek, Kutnick, Denise, Laub, Frank, Md, Vasimuddin, Misra, Sanchit, Mohanty, Ramanarayan, Pabst, Hans, Retford, Brian, Ziv, Barukh, Heinecke, Alexander
During the past decade, novel Deep Learning (DL) algorithms, workloads and hardware have been developed to tackle a wide range of problems. Despite the advances in workload and hardware ecosystems, the programming methodology of DL systems is stagnan
Externí odkaz:
http://arxiv.org/abs/2104.05755
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). DGL contains implementations of all core graph operations fo
Externí odkaz:
http://arxiv.org/abs/2007.06354
Autor:
Marcus, Ryan, Kipf, Andreas, van Renen, Alexander, Stoian, Mihail, Misra, Sanchit, Kemper, Alfons, Neumann, Thomas, Kraska, Tim
Recent advancements in learned index structures propose replacing existing index structures, like B-Trees, with approximate learned models. In this work, we present a unified benchmark that compares well-tuned implementations of three learned index s
Externí odkaz:
http://arxiv.org/abs/2006.12804