Publications

Our publications provide insight into some of our leading-edge research.

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2025

Attention on the Sphere
Boris Bonev, Max Rietmann, Andrea Paris, Alberto Carpentieri, Thorsten Kurth
Augmenting Simulated Noisy Quantum Data Collection by Orders of Magnitude Using Pre-Trajectory Sampling with Batched Execution
Taylor Patti, Thien Nguyen, Justin Lietz, Alex McCaskey, Brucek Khailany
Isaac Lab: A GPU Accelerated Simulation Framework For Multi-Modal Robot Learning
Mayank Mittal, Kelly Guo, Gavriel State, Spencer Huang
Huge ensembles–Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators
Ankur Mahesh, William D. Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Joshua Elms, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis O'Brian, Mike Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard
Huge ensembles – Part 2: Properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators
Ankur Mahesh, William D. Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis O'Brian, Mike Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard
FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
Boris Bonev, Thorsten Kurth, Ankur Mahesh, Mauro Bisson, Jean Kossaifi, Karthik Kashinath, Anima Anandkumar, William D. Collins, Mike Pritchard, Alex Keller
Helix Parallelism: Rethinking Sharding Strategies for Interactive Multi-Million-Token LLM Decoding
Nidhi Bhatia, Ankit More, Ritika Borkar, Tiyasa Mitra, Ramon Matas, Ritchie Zhao, Maximilian Golub, Dheevatsa Mudigere, Brian Pharris, Bita Darvish Rouhani
scene_synthesizer: A Python Library for Procedural Scene Generation in Robot Manipulation
Clemens Eppner, Adithya Murali, Caelan Garrett, Rowland O'Flaherty, Tucker Hermans, Wei Yang, Dieter Fox

2024

Pretraining codomain attention neural operators for solving multiphysics pdes
Md Ashiqur Rahman, Robert Joseph George, Mogab Elleithy, Daniel Leibovici, Zongyi Li, Boris Bonev, Colin White, Julius Berner, Raymond A. Yeh, Jean Kossaifi, Kamyar Azizzadenesheli, Anima Anandkumar
Differentiable GPU-Parallelized Task and Motion Planning
William Shen, Caelan Garrett, Nishanth Kumar, Ankit Goyal, Tucker Hermans, Leslie Pack Kaelbling, Tomás Lozano-Pérez, Fabio Ramos
Constructability-driven design of frame structures with state-space search methods
Yijiang Huang, Caelan Garrett, Caitlin Mueller
Conformer without Convolutions
Matthijs Van keirsbilck, Alex Keller
Neural operators with localized integral and differential kernels
Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar
Improving Hyperparameter Optimization with Checkpointed Model Weights
Nikhil Mehta, Jonathan Lorraine, Steve Masson, Ramanathan Arunachalam, Zaid Pervaiz Bhat, James Lucas, Arun George Zachariah

2023

Compact Neural Graphics Primitives with Learned Hash Probing
Towaki Takikawa, Thomas Müller, Merlin Nimier-David, Alex Evans, Sanja Fidler, Alec Jacobson, Alex Keller
Graph Metanetworks for Processing Diverse Neural Architectures
Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas
Graph Neural Networks for Enhanced Decoding of Quantum LDPC Codes
Anqi Gong, Sebastian Cammerer, Joseph M. Renes
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar

2022

Deep Learning-Based Synchronization for Uplink NB-IoT
Fayçal Aït Aoudia , Jakob Hoydis, Sebastian Cammerer, Matthijs Van keirsbilck, Alex Keller
Graph Neural Networks for Channel Decoding
Sebastian Cammerer, Jakob Hoydis, Fayçal Aït Aoudia , Alex Keller
Unbiased Inverse Volume Rendering With Differential Trackers
Merlin Nimier-David, Thomas Müller, Alex Keller, Wenzel Jakob
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
Charbel Sakr, Steve Dai, Rangharajan Venkatesan, Brian Zimmer, Brucek Khailany, William Dally
Adaptive Neural Network-based OFDM Receivers
Moritz Benedikt Fischer, Sebastian Dörner, Sebastian Cammerer, Takayuki Shimizu, Hongsheng Lu, Stephan ten Brink
MatBuilder: Mastering Sampling Uniformity Over Projections
Loïs Paulin, Nicolas Bonneel, David Coeurjolly, Jean-Claude Iehl, Alex Keller, Victor Ostromoukhov
Artificial Neural Networks generated by Low Discrepancy Sequences
Matthijs Van keirsbilck, Alex Keller
Sionna: An Open-Source Library for Next-Generation Physical Layer Research
Jakob Hoydis, Sebastian Cammerer, Fayçal Aït Aoudia , Avinash Vem, Nikolaus Binder, Guillermo Marcus, Alex Keller
Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks
Haoyu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Mark Kilgard, Anima Anandkumar, Brucek Khailany, Vivek Singh, Mark Haoxing Ren

2021

Graph Learning-Based Arithmetic Block Identification
Zhuolun He, Ziyi Wang, Chen Bai, Haoyu Yang, Bei Yu
Compressing 1D Time-Channel Separable Convolutions using Sparse Random Ternary Matrices
Goncalo Mordido, Matthijs Van keirsbilck, Alex Keller

2020

DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement
Yibo Lin, Zixuan Jiang, Jiaqi Gu, Wuxi Li, Shounak Dhar, Mark Haoxing Ren, Brucek Khailany, David Z. Pan
2021 IEEE Transactions on Computer-Aided Design Donald O. Pederson Best Paper Award
Monte Carlo Gradient Quantization
Goncalo Mordido, Matthijs Van keirsbilck, Alex Keller

2019

Instant Quantization of neural networks using Monte Carlo Methods
Gonçalo Mordido, Matthijs Van keirsbilck, Alex Keller