Rishit Dagli

This talk is a playbook for making any PyTorch physics engine: rigid bodies, deformables or fluids into a differentiable simulator. This is a very relevant problem for many learning problems like, policy search in robotics, video/SDS-based parameter estimation for simulators, and soft‑material...

Edge computing is becoming increasingly popular for applications that require low latency and high bandwidth. However, building machine learning runtimes for edge computing infra that is both scalable and optimized can be challenging. In this talk, we explore how PyTorch and WebAssembly (Wasm) can...

On-Device Training with ONNX Runtime for PyTorch Models - Kshama Pawar, Microsoft Corporation On-Device Training is a new capability in ONNX Runtime to train models on the edge for Federated Learning and Personalization scenarios. ONNX Runtime provides an efficient, local trainer, that trains with...