Machine Learning (lecture notes)

Winter 2024

@machine_learning_and_DL

Statistical Learning Theory(Winter 2016)

Percy Liang
@machine_learning_and_DL

SOTA🚀 YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

☄️ Github
☄️ Paper

🩻 Pose via Ray Diffusion 🩻

👉Novel distributed representation of camera pose that treats a camera as a bundle of rays. Naturally suited for set-level transformers, it's the new SOTA on camera pose estimation. Source code released 💙

👉Review https://t.ly/qBsFK
👉Paper arxiv.org/pdf/2402.14817.pdf
👉Project jasonyzhang.com/RayDiffusion
👉Code github.com/jasonyzhang/RayDiffusion

CyberDemo

Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation

We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks. By incorporating extensive data augmentation in a simulated environment, CyberDemo outperforms traditional in-domain real-world demonstrations when transferred to the real world, handling diverse physical and visual conditions. Regardless of its affordability and convenience in data collection, CyberDemo outperforms baseline methods in terms of success rates across various tasks and exhibits generalizability with previously unseen objects. For example, it can rotate novel tetra-valve and penta-valve, despite human demonstrations only involving tri-valves. Our research demonstrates the significant potential of simulated human demonstrations for real-world dexterous manipulation tasks.

paper page: https://huggingface.co/papers/2402.14795


🆔

Deep Learning with torch:: CHEAT SHEET

🫅FlowMDM: Human Composition🫅

👉FlowMDM, a diffusion-based approach capable of generating seamlessly continuous sequences of human motion from textual descriptions.

👉Review https://t.ly/pr2g_
👉Paper https://lnkd.in/daYRftdF
👉Project https://lnkd.in/dcRkv5Pc
👉Repo https://lnkd.in/dw-3JJks