244 citations · 1.3k across the 42 of their papers we have counts for
8 papers · 1 filter
TensorLy-Quantum: Quantum Machine Learning with Tensor Methods
Taylor L. Patti, Jean Kossaifi, Susanne F. Yelin +1
Simulation is essential for developing quantum hardware and algorithms. However, simulating quantum circuits on classical hardware is challenging due to the exponential scaling of…
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…
AugMax: Adversarial Composition of Random Augmentations for Robust Training
Haotao Wang, Chaowei Xiao, Jean Kossaifi +3
Data augmentation is a simple yet effective way to improve the robustness of deep neural networks (DNNs). Diversity and hardness are two complementary dimensions of data augmentati…
Tensor Methods in Computer Vision and Deep Learning
Yannis Panagakis, Jean Kossaifi, Grigorios G. Chrysos +4
Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions. Inherently able to efficiently capture structured, latent…
Variational Quantum Optimization with Multi-Basis Encodings
Taylor L. Patti, Jean Kossaifi, Anima Anandkumar +1
Despite extensive research efforts, few quantum algorithms for classical optimization demonstrate realizable quantum advantage. The utility of many quantum algorithms is limited by…
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…
Augmenting Deep Classifiers with Polynomial Neural Networks
Grigorios G Chrysos, Markos Georgopoulos, Jiankang Deng +3
Deep neural networks have been the driving force behind the success in classification tasks, e.g., object and audio recognition. Impressive results and generalization have been ach…