Publications

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

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2022

MatBuilder: Mastering Sampling Uniformity Over Projections
Loïs Paulin, Nicolas Bonneel, David Coeurjolly, Jean-Claude Iehl, Alex Keller, Victor Ostromoukhov
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
Rinon Gal, Or Patashnik, Haggai Maron, Amir Bermano, Gal Chechik, Daniel Cohen-Or
Accelerated Policy Learning with Parallel Differentiable Simulation
Jie Xu, Viktor Makoviychuk, Yashraj Narang, Fabio Ramos, Wojciech Matusik, Animesh Garg, Miles Macklin
Artificial Neural Networks generated by Low Discrepancy Sequences
Matthijs Van keirsbilck, Alex Keller
A Dataset and Explorer for 3D Signed Distance Functions
Towaki Takikawa, Andrew Glassner, Morgan McGuire
Neural Fields in Visual Computing and Beyond
Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, Srinath Sridhar
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris Kitani
Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators
"This is my unicorn, Fluffy": Personalizing frozen vision-language representations
Niv Cohen, Rinon Gal, Gal Chechik, Yuval Atzmon
Oral
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
DefGraspSim: Physics-Based Simulation of Grasp Outcomes on 3D Deformable Objects
Isabella Huang, Yashraj Narang, Clemens Eppner, Balakumar Sundaralingam, Miles Macklin, Ruzena Bajcsy, Tucker Hermans, Dieter Fox
DiGamma: Domain-aware Genetic Algorithm for HW-Mapping Co-optimization for DNN Accelerators
Sheng-Chun Kao, Michael Pellauer, Angshuman Parashar, Tushar Krishna
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, Haoxing (Mark) Ren
Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
Jingkai Chen, Jiaoyang Li, Yijiang Huang, Caelan Garrett, Dawei Sun, Chuchu Fan, Andreas Hofmann, Caitlin Mueller, Sven Koenig, Brian Williams
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
Tim Dockhorn, Arash Vahdat, Karsten Kreis
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
Zhisheng Xiao, Karsten Kreis, Arash Vahdat
Marvel: A Data-Centric Approach for Mapping Deep Learning Operators on Spatial Accelerators
Prasanth Chatarasi, Hyoukjun Kwon, Angshuman Parashar, Michael Pellauer, Tushar Krishna, Vivek Sarkar
Reinforcement Learning for Datacenter Congestion Control
Chen Tessler, Yuval Shpigelman, Gal Dalal, Amit Mendelbaum, Doron Kazakov, Benjamin Fuhrer, Gal Chechik, Shie Mannor
Machine Learning and Algorithms: Let Us Team Up for EDA
Haoxing (Mark) Ren, Brucek Khailany, Matt Fojtik, Yanqing Zhang

2021

ATISS: Autoregressive Transformers for Indoor Scene Synthesis
Despoina Paschalidou, Amlan Kar, Maria Shugrina, Karsten Kreis, Andreas Geiger, Sanja Fidler
Alias-Free Generative Adversarial Networks
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, Timo Aila
EditGAN: High-Precision Semantic Image Editing
Huan Ling, Karsten Kreis, Daiqing Li, Seung Wook Kim, Antonio Torralba, Sanja Fidler
NVCell: Standard Cell Layout in Advanced Technology Nodes with Reinforcement Learning
Haoxing (Mark) Ren, Matt Fojtik, Brucek Khailany
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers
Jacob R. Stevens, Rangharajan Venkatesan, Steve Dai, Brucek Khailany, Anand Raghunathan
KAMA: 3D Keypoint Aware Body Mesh Articulation
Umar Iqbal, Kevin Xie, Kelly Guo, Jan Kautz, Pavlo Molchanov
Standard vs. Learning-based Codecs for Real Time Endoscopic Video Transmission
Iuri Frosio, Aldo Marzullo, Martina Golini, Elena De Momi, Michele Catellani, Francesco Calimeri, Giuseppe Fiameni
Extracting Triangular 3D Models, Materials, and Lighting From Images
Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, Thomas Müller, Sanja Fidler
Noise-Aware Video Saliency Prediction
Ekta Prashnani, Orazio Gallo, Joohwan Kim, Josef Spjut, Pradeep Sen, Iuri Frosio
A Contrastive Learning Approach for Training Variational Autoencoder Priors
Jyoti Aneja, Alexander Schwing, Jan Kautz, Arash Vahdat
Controllable and Compositional Generation with Latent-Space Energy-Based Models
Weili Nie, Arash Vahdat, Anima Anandkumar
Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, Karsten Kreis
Score-based Generative Modeling in Latent Space
Arash Vahdat, Karsten Kreis, Jan Kautz