Search Results for author: Jean Kossaifi

Found 35 papers, 12 papers with code

Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs

1 code implementation19 Mar 2024 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

On complex downstream tasks with limited data, such as fluid flow simulations and fluid-structure interactions, we found CoDA-NO to outperform existing methods on the few-shot learning task by over $36\%$.

Few-Shot Learning Self-Supervised Learning

Equivariant Graph Neural Operator for Modeling 3D Dynamics

1 code implementation19 Jan 2024 Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar

Comprehensive experiments in multiple domains, including particle simulations, human motion capture, and molecular dynamics, demonstrate the significantly superior performance of EGNO against existing methods, thanks to the equivariant temporal modeling.

Operator learning

Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs

no code implementations29 Sep 2023 Jean Kossaifi, Nikola Kovachki, Kamyar Azizzadenesheli, Anima Anandkumar

Our contributions are threefold: i) we enable parallelization over input samples with a novel multi-grid-based domain decomposition, ii) we represent the parameters of the model in a high-order latent subspace of the Fourier domain, through a global tensor factorization, resulting in an extreme reduction in the number of parameters and improved generalization, and iii) we propose architectural improvements to the backbone FNO.

Operator learning

Neural Operators for Accelerating Scientific Simulations and Design

no code implementations27 Sep 2023 Kamyar Azizzadenesheli, Nikola Kovachki, Zongyi Li, Miguel Liu-Schiaffini, Jean Kossaifi, Anima Anandkumar

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 deep domain expertise.

Super-Resolution Weather Forecasting

Guaranteed Approximation Bounds for Mixed-Precision Neural Operators

1 code implementation27 Jul 2023 Renbo Tu, Colin White, Jean Kossaifi, Boris Bonev, Nikola Kovachki, Gennady Pekhimenko, Kamyar Azizzadenesheli, Anima Anandkumar

Neural operators, such as Fourier Neural Operators (FNO), form a principled approach for learning solution operators for PDEs and other mappings between function spaces.

Operator learning

Score-based Diffusion Models in Function Space

no code implementations14 Feb 2023 Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli, Jean Kossaifi, Vikram Voleti, Jiaming Song, Karsten Kreis, Jan Kautz, Christopher Pal, Arash Vahdat, Anima Anandkumar

They consist of a forward process that perturbs input data with Gaussian white noise and a reverse process that learns a score function to generate samples by denoising.


Incremental Spatial and Spectral Learning of Neural Operators for Solving Large-Scale PDEs

no code implementations28 Nov 2022 Robert Joseph George, Jiawei Zhao, Jean Kossaifi, Zongyi Li, Anima Anandkumar

Fourier Neural Operators (FNO) offer a principled approach to solving challenging partial differential equations (PDE) such as turbulent flows.

Reinforcement Learning in Factored Action Spaces using Tensor Decompositions

no code implementations27 Oct 2021 Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar

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, factored action spaces using tensor decompositions.

Multi-agent Reinforcement Learning reinforcement-learning +1

Defensive Tensorization

no code implementations26 Oct 2021 Adrian Bulat, Jean Kossaifi, Sourav Bhattacharya, Yannis Panagakis, Timothy Hospedales, Georgios Tzimiropoulos, Nicholas D Lane, Maja Pantic

We propose defensive tensorization, an adversarial defence technique that leverages a latent high-order factorization of the network.

Audio Classification Image Classification

Tensor Methods in Computer Vision and Deep Learning

no code implementations7 Jul 2021 Yannis Panagakis, Jean Kossaifi, Grigorios G. Chrysos, James Oldfield, Mihalis A. Nicolaou, Anima Anandkumar, Stefanos Zafeiriou

Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions.

Representation Learning

Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning

no code implementations31 May 2021 Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar

Algorithms derived from Tesseract decompose the Q-tensor across agents and utilise low-rank tensor approximations to model agent interactions relevant to the task.

Learning Theory Multi-agent Reinforcement Learning +3

Unsupervised Controllable Generation with Self-Training

no code implementations17 Jul 2020 Grigorios G. Chrysos, Jean Kossaifi, Zhiding Yu, Anima Anandkumar

Instead, we propose an unsupervised framework to learn a distribution of latent codes that control the generator through self-training.


Toward fast and accurate human pose estimation via soft-gated skip connections

3 code implementations25 Feb 2020 Adrian Bulat, Jean Kossaifi, Georgios Tzimiropoulos, Maja Pantic

In addition, with a reduction of 3x in model size and complexity, we show no decrease in performance when compared to the original HourGlass network.

Pose Estimation

Convolutional Tensor-Train LSTM for Spatio-temporal Learning

2 code implementations NeurIPS 2020 Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, Animashree Anandkumar

Learning from spatio-temporal data has numerous applications such as human-behavior analysis, object tracking, video compression, and physics simulation. However, existing methods still perform poorly on challenging video tasks such as long-term forecasting.

 Ranked #1 on Video Prediction on KTH (Cond metric)

Activity Recognition Video Compression +1

Defensive Tensorization: Randomized Tensor Parametrization for Robust Neural Networks

no code implementations25 Sep 2019 Adrian Bulat, Jean Kossaifi, Sourav Bhattacharya, Yannis Panagakis, Georgios Tzimiropoulos, Nicholas D. Lane, Maja Pantic

As deep neural networks become widely adopted for solving most problems in computer vision and audio-understanding, there are rising concerns about their potential vulnerability.

Adversarial Defense Audio Classification +1

Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation

no code implementations CVPR 2020 Jean Kossaifi, Antoine Toisoul, Adrian Bulat, Yannis Panagakis, Timothy Hospedales, Maja Pantic

To alleviate this, one approach is to apply low-rank tensor decompositions to convolution kernels in order to compress the network and reduce its number of parameters.

Emotion Recognition Image Classification

Incremental multi-domain learning with network latent tensor factorization

no code implementations12 Apr 2019 Adrian Bulat, Jean Kossaifi, Georgios Tzimiropoulos, Maja Pantic

Adapting the learned classification to new domains is a hard problem due to at least three reasons: (1) the new domains and the tasks might be drastically different; (2) there might be very limited amount of annotated data on the new domain and (3) full training of a new model for each new task is prohibitive in terms of computation and memory, due to the sheer number of parameters of deep CNNs.

General Classification Image Classification +2

Improved training of binary networks for human pose estimation and image recognition

1 code implementation11 Apr 2019 Adrian Bulat, Georgios Tzimiropoulos, Jean Kossaifi, Maja Pantic

Big neural networks trained on large datasets have advanced the state-of-the-art for a large variety of challenging problems, improving performance by a large margin.

Binarization Classification with Binary Neural Network +4

Tensor Dropout for Robust Learning

no code implementations27 Feb 2019 Arinbjörn Kolbeinsson, Jean Kossaifi, Yannis Panagakis, Adrian Bulat, Anima Anandkumar, Ioanna Tzoulaki, Paul Matthews

CNNs achieve remarkable performance by leveraging deep, over-parametrized architectures, trained on large datasets.

Image Classification Inductive Bias

SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild

no code implementations9 Jan 2019 Jean Kossaifi, Robert Walecki, Yannis Panagakis, Jie Shen, Maximilian Schmitt, Fabien Ringeval, Jing Han, Vedhas Pandit, Antoine Toisoul, Bjorn Schuller, Kam Star, Elnar Hajiyev, Maja Pantic

Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are increasingly becoming an indispensable part of our life.

Robust Conditional Generative Adversarial Networks

1 code implementation ICLR 2019 Grigorios G. Chrysos, Jean Kossaifi, Stefanos Zafeiriou

Conditional generative adversarial networks (cGAN) have led to large improvements in the task of conditional image generation, which lies at the heart of computer vision.

Conditional Image Generation

Tensor Contraction & Regression Networks

no code implementations ICLR 2018 Jean Kossaifi, Zack Chase Lipton, Aran Khanna, Tommaso Furlanello, Anima Anandkumar

Second, we introduce tensor regression layers, which express the output of a neural network as a low-rank multi-linear mapping from a high-order activation tensor to the softmax layer.


GAGAN: Geometry-Aware Generative Adversarial Networks

no code implementations CVPR 2018 Jean Kossaifi, Linh Tran, Yannis Panagakis, Maja Pantic

Deep generative models learned through adversarial training have become increasingly popular for their ability to generate naturalistic image textures.

Face Generation

Tensor Regression Networks

no code implementations26 Jul 2017 Jean Kossaifi, Zachary C. Lipton, Arinbjorn Kolbeinsson, Aran Khanna, Tommaso Furlanello, Anima Anandkumar

First, we introduce Tensor Contraction Layers (TCLs) that reduce the dimensionality of their input while preserving their multilinear structure using tensor contraction.


Tensor Contraction Layers for Parsimonious Deep Nets

no code implementations1 Jun 2017 Jean Kossaifi, Aran Khanna, Zachary C. Lipton, Tommaso Furlanello, Anima Anandkumar

Specifically, we propose the Tensor Contraction Layer (TCL), the first attempt to incorporate tensor contractions as end-to-end trainable neural network layers.

Model Compression

TensorLy: Tensor Learning in Python

1 code implementation29 Oct 2016 Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic

In addition, using the deep-learning frameworks as backend allows users to easily design and train deep tensorized neural networks.

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