244 citations · 1.3k across the 42 of their papers we have counts for
32 papers · 1 filter
A library for differentiable signal processing and machine learning on the sphere
Thorsten Kurth, Max Rietmann, Mauro Bisson +6
The two-dimensional sphere embedded in three-dimensional Euclidean space S2, plays a central role in a variety of scientific and engineering domains, including geophysics, planetar…
Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting
Jean Kossaifi, Nikola Kovachki, Morteza Mardani +15
The recent revolution in data-driven methods for weather forecasting has lead to a fragmented landscape of complex, bespoke architectures and training strategies, obscuring the fun…
Fourier Neural Operators Explained: A Practical Perspective
Valentin Duruisseaux, Jean Kossaifi, Anima Anandkumar
Partial differential equations (PDEs) govern a wide variety of dynamical processes in science and engineering, yet obtaining their numerical solutions often requires high-resolutio…
Analyzing Political Text at Scale with Online Tensor LDA
Sara Kangaslahti, Danny Ebanks, Jean Kossaifi +3
This paper proposes a topic modeling method that scales linearly to billions of documents. We make three core contributions: i) we present a topic modeling method, Tensor Latent Di…
FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
Boris Bonev, Thorsten Kurth, Ankur Mahesh +7
FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting. The approach is designed…
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
Julius Berner, Miguel Liu-Schiaffini, Jean Kossaifi +4
A wide range of scientific problems, such as those described by continuous-time dynamical systems and partial differential equations (PDEs), are naturally formulated on function sp…
Enabling Automatic Differentiation with Mollified Graph Neural Operators
Ryan Y. Lin, Julius Berner, Valentin Duruisseaux +5
Physics-informed neural operators offer a powerful framework for learning solution operators of partial differential equations (PDEs) by combining data and physics losses. However,…
TensorGRaD: Tensor Gradient Robust Decomposition for Memory-Efficient Neural Operator Training
Sebastian Loeschcke, David Pitt, Robert Joseph George +5
Scientific problems require resolving multi-scale phenomena across different resolutions and learning solution operators in infinite-dimensional function spaces. Neural operators p…
A Library for Learning Neural Operators
Jean Kossaifi, Nikola Kovachki, Zongyi Li +8
We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimens…
Exploring the design space of deep-learning-based weather forecasting systems
Shoaib Ahmed Siddiqui, Jean Kossaifi, Boris Bonev +4
Despite tremendous progress in developing deep-learning-based weather forecasting systems, their design space, including the impact of different design choices, is yet to be well u…
Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs
Md Ashiqur Rahman, Robert Joseph George, Mogab Elleithy +9
Existing neural operator architectures face challenges when solving multiphysics problems with coupled partial differential equations (PDEs) due to complex geometries, interactions…
Equivariant Graph Neural Operator for Modeling 3D Dynamics
Minkai Xu, Jiaqi Han, Aaron Lou +6
Modeling the complex three-dimensional (3D) dynamics of relational systems is an important problem in the natural sciences, with applications ranging from molecular simulations to…
Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs
Jean Kossaifi, Nikola Kovachki, Kamyar Azizzadenesheli +1
Memory complexity and data scarcity have so far prohibited learning solution operators of partial differential equations (PDEs) at high resolutions. We address these limitations by…
Neural Operators for Accelerating Scientific Simulations and Design
Kamyar Azizzadenesheli, Nikola Kovachki, Zongyi Li +3
Scientific discovery and engineering design are currently limited by the time and cost of physical experiments, selected mostly through trial-and-error and intuition that require d…
Geometry-Informed Neural Operator for Large-Scale 3D PDEs
Zongyi Li, Nikola Borislavov Kovachki, Chris Choy +8
We propose the geometry-informed neural operator (GINO), a highly efficient approach to learning the solution operator of large-scale partial differential equations with varying ge…
Guaranteed Approximation Bounds for Mixed-Precision Neural Operators
Renbo Tu, Colin White, Jean Kossaifi +5
Neural operators, such as Fourier Neural Operators (FNO), form a principled approach for learning solution operators for PDEs and other mappings between function spaces. However, m…
Score-based Diffusion Models in Function Space
Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista +10
Diffusion models have recently emerged as a powerful framework for generative modeling. They consist of a forward process that perturbs input data with Gaussian white noise and a r…
HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression
Jiaqi Gu, Ben Keller, Jean Kossaifi +3
Transformers have attained superior performance in natural language processing and computer vision. Their self-attention and feedforward layers are overparameterized, limiting infe…
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions
Anuj Mahajan, Mikayel Samvelyan, Lei Mao +6
We present an extended abstract for the previously published work TESSERACT [Mahajan et al., 2021], which proposes a novel solution for Reinforcement Learning (RL) in large, factor…
Defensive Tensorization
Adrian Bulat, Jean Kossaifi, Sourav Bhattacharya +5
We propose defensive tensorization, an adversarial defence technique that leverages a latent high-order factorization of the network. The layers of a network are first expressed as…
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
Anuj Mahajan, Mikayel Samvelyan, Lei Mao +6
Reinforcement Learning in large action spaces is a challenging problem. Cooperative multi-agent reinforcement learning (MARL) exacerbates matters by imposing various constraints on…
Unsupervised Controllable Generation with Self-Training
Grigorios G Chrysos, Jean Kossaifi, Zhiding Yu +1
Recent generative adversarial networks (GANs) are able to generate impressive photo-realistic images. However, controllable generation with GANs remains a challenging research prob…
Spectral Learning on Matrices and Tensors
Majid Janzamin, Rong Ge, Jean Kossaifi +1
Spectral methods have been the mainstay in several domains such as machine learning and scientific computing. They involve finding a certain kind of spectral decomposition to obtai…
Speech-driven facial animation using polynomial fusion of features
Triantafyllos Kefalas, Konstantinos Vougioukas, Yannis Panagakis +3
Speech-driven facial animation involves using a speech signal to generate realistic videos of talking faces. Recent deep learning approaches to facial synthesis rely on extracting…