Introducing TabFM, a foundation model designed specifically for tabular data classification & regression. This approach allows generation of high-quality predictions on previously unseen tables in a single forward pass.
Learn more and try out the model →https://t.co/OTbVQ8oUQs
STEAL Top 0.1% Tsinghua AI Researcher's Paper Idea
A Tsinghua robotics researcher (PhD in Hong Kong) walks through a robotics reinforcement learning paper idea.
The idea: keep RL for low-level control, then stack a sampling-based planner on top to actually complete high-level tasks like moving a desk or making coffee. He also argues the architecture shouldn't be static - you can distill the sampling planner back into a policy, so RL and sampling blend into one "fluid" system.
0:00 A Tsinghua researcher on robotics RL
0:31 RL is great at low-level control, not whole tasks
1:04 Put a sampling-based MPC planner on top
1:40 RL vs classical planning: which part goes where
2:27 Distill sampling into a policy (a fluid architecture)
arxiv - https://t.co/7QXrhPAfzg
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