1 code implementation • 19 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 $36\%$.
no code implementations • 19 Jan 2024 • Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar
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 particle mechanics.
no code implementations • 29 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.
no code implementations • 27 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.
1 code implementation • 27 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.
no code implementations • 14 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.
no code implementations • 30 Nov 2022 • Jiaqi Gu, Ben Keller, Jean Kossaifi, Anima Anandkumar, Brucek Khailany, David Z. Pan
Transformers have attained superior performance in natural language processing and computer vision.
no code implementations • 28 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.
no code implementations • 27 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
1 code implementation • NeurIPS 2021 • Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, Zhangyang Wang
Diversity and hardness are two complementary dimensions of data augmentation to achieve robustness.
no code implementations • 26 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.
no code implementations • 7 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.
no code implementations • 31 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.
2 code implementations • 16 Apr 2021 • Grigorios G Chrysos, Markos Georgopoulos, Jiankang Deng, Jean Kossaifi, Yannis Panagakis, Anima Anandkumar
The efficacy of the proposed models is evaluated on standard image and audio classification benchmarks.
Ranked #2 on Audio Classification on Speech Commands
no code implementations • 17 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.
no code implementations • 16 Apr 2020 • Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar
PCA and other spectral techniques applied to matrices have several limitations.
3 code implementations • 25 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.
Ranked #2 on Pose Estimation on Leeds Sports Poses
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)
no code implementations • 12 Dec 2019 • Triantafyllos Kefalas, Konstantinos Vougioukas, Yannis Panagakis, Stavros Petridis, Jean Kossaifi, Maja Pantic
Speech-driven facial animation involves using a speech signal to generate realistic videos of talking faces.
no code implementations • 25 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.
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.
no code implementations • 16 Apr 2019 • Adrian Bulat, Jean Kossaifi, Georgios Tzimiropoulos, Maja Pantic
This paper is on improving the training of binary neural networks in which both activations and weights are binary.
no code implementations • 12 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.
1 code implementation • 11 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.
no code implementations • CVPR 2019 • Jean Kossaifi, Adrian Bulat, Georgios Tzimiropoulos, Maja Pantic
In this paper, we propose to fully parametrize Convolutional Neural Networks (CNNs) with a single high-order, low-rank tensor.
Ranked #35 on Pose Estimation on MPII Human Pose
no code implementations • 27 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.
no code implementations • 9 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.
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.
1 code implementation • ICLR 2018 • Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Anima Anandkumar
Neural networks are known to be vulnerable to adversarial examples.
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.
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.
no code implementations • 26 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.
no code implementations • 1 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.
1 code implementation • 29 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.
1 code implementation • 12 Dec 2014 • Alexandre Abraham, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Muller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, Gäel Varoquaux
Statistical machine learning methods are increasingly used for neuroimaging data analysis.