💬🗣️ Public talks I presented historically in Demo Day, Demo Inn, KubeCon, @DaoCloud, all built with @slidevjs

Learning Large-scale Neural Fields via Context Pruned Meta-Learning (NeurIPS 2023)

Official repository for Controllable Style Transfer via Test-time Training of Implicit Neural Representation

implicit displacement field

A library for scientific machine learning and physics-informed learning

Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"

PyTorch implementation of Neural Dual Contouring.

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

POT : Python Optimal Transport

ML and DL related contests, competitions and conference challenges.

code for our ICCV 2021 paper "DeepCAD: A Deep Generative Network for Computer-Aided Design Models"

Latex code for making neural networks diagrams

Atlas: End-to-End 3D Scene Reconstruction from Posed Images

Node-based Visual Programming Toolbox

This repository contains implementations and illustrative code to accompany DeepMind publications

Implementation of the paper "Layout Embedding via Combinatorial Optimization"

The public CGAL repository, see the README below

pytorch implementation for "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

This is an implementation for the paper entitled "Fast mesh denoising with data driven normal filtering using deep variational autoencoders" published in IEEE Transactions on Industrial Informatics…

Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer (NeurIPS 2019)

Learning Deformable Tetrahedral Meshes for 3D Reconstruction (NeurIPS 2020)

PointFlow : 3D Point Cloud Generation with Continuous Normalizing Flows

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

Learning Continuous Signed Distance Functions for Shape Representation

Crawl & visualize ICLR papers and reviews.

This repository contains code released by DiffEqML Research

This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"