News

Guest speaker at the Oak Ridge National Laboratory's AI Expo, where I presented my work on Neural Operators for AI in Science and Engineering

Co-authored a book chapter, "Tensor methods in deep learning", in the book Signal Processing and Machine Learning Theory

Co-organized workshop with Topal team at INRIA Bordeaux on efficient scaling of neural architectures, covering re-materialization, offloading, scheduling and model pipelining

Co-organizer, ICML Workshop on Advancing Neural Network Training (WANT): Computational Efficiency, Scalability, and Resource Optimization

Organizer, NeurIPS Workshop on Advancing Neural Network Training (WANT): Computational Efficiency, Scalability, and Resource Optimization

Invited speaker at the Scale by the Bay, bay area AI, 2023, hosted by IBM on AI for Science with Neural Operators

New blog post on Weather Modeling with Spherical Neural Operators

We set a new world record, for the largest quantum circuit simulation, using TensorLy-Quantum, NVIDIA's cuQuantum library and a new methodology we developped. Using 896 GPUs to simulate 1,688 qubits, we were able to solve the MaxCut problem for a graph with 3,375 vertices!

New NVIDIA blog post on Tensor Methods for Deep Learning

Co-organizer, NeurIPS Second Workshop on Quantum Tensor Networks in Machine Learning

Accepted paper at CVPR 2020: Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation

New AAAI paper: Incremental multi-domain learning with network latent tensor factorization

New book on Spectral Learning on Matrices and Tensors published

Invited speaker at the Third International Workshop on “Robust Subspace Learning and Applications in Computer Vision” at ICCV 2019

Delivered a tutorial on advanced deep learning, tensor methods and quantization at Caltech

Delivered a GTC talk “Take Your Machine Learning to Higher Dimensions with Tensor Methods”, together with my colleague Chris Choy

Co-organizer of the CVPR tutorial Cause-and-Effect in a Tensor Framework with Alex Vasilescu and Lieven De Lathauwer

Paper on T-Net: Parametrizing Fully Convolutional Nets with a Single High-Order Tensor accepted at CVPR'19

Delivered a tutorial on Deep Learning and Tensor Methods at Caltech

Taught the class on Tensor Methods for Large Scale Machine-Learning at the IfI Summer School 2018 on Machine Learning

New paper accepted at IEEE CVPR on Geometry-Aware Generative Adversarial Networks

Co-organizing the ICCV 2017 Workshop on Matrix and Tensor Factorization Methods for Computer Vision

Paper on Tensor Contraction Layers for Parsimonious Deep Nets accepted at CVPR'17 Workshop on Tensor Methods in Computer Vision