154 citations · 267 across the 12 of their papers we have counts for
13 papers
Mixture of GFlowNets
Omar G. Younis, Joseph D. Viviano, Chirayu Anant Haryan +3
Generative Flow Networks (GFlowNets) learn a policy that samples compositional objects in proportion to an unnormalized reward . This makes them useful wherever many diverse,…
Generative Active Learning for the Search of Small-molecule Protein Binders
Maksym Korablyov, Cheng-Hao Liu, Moksh Jain +31
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 si…
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design
Chuanrui Wang, Bozitao Zhong, Zuobai Zhang +3
Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years. However, a universally accepted method for evaluation has…
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
Yangtian Zhang, Zuobai Zhang, Bozitao Zhong +2
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…
Accelerating Barnes-Hut t-SNE Algorithm by Efficient Parallelization on Multi-Core CPUs
Narendra Chaudhary, Alexander Pivovar, Pavel Yakovlev +2
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 (B…
DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling
Md Vasimuddin, Ramanarayan Mohanty, Sanchit Misra +1
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 exa…
Efficient and Generic 1D Dilated Convolution Layer for Deep Learning
Narendra Chaudhary, Sanchit Misra, Dhiraj Kalamkar +5
Convolutional neural networks (CNNs) have found many applications in tasks involving two-dimensional (2D) data, such as image classification and image processing. Therefore, 2D con…
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks
Vasimuddin Md, Sanchit Misra, Guixiang Ma +6
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…
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads
Evangelos Georganas, Dhiraj Kalamkar, Sasikanth Avancha +16
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 har…
Deep Graph Library Optimizations for Intel(R) x86 Architecture
Sasikanth Avancha, Vasimuddin Md, Sanchit Misra +1
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 Networ…
Benchmarking Learned Indexes
Ryan Marcus, Andreas Kipf, Alexander van Renen +5
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 bench…
LISA: Towards Learned DNA Sequence Search
Darryl Ho, Jialin Ding, Sanchit Misra +4
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…
Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems
Vasimuddin Md, Sanchit Misra, Heng Li +1
Innovations in Next-Generation Sequencing are enabling generation of DNA sequence data at ever faster rates and at very low cost. Large sequencing centers typically employ hundreds…