Results for "body as compute"

Motion of solid objects under forces.

Increasing model capacity via compute.

Hardware resources used for training/inference; constrained by memory bandwidth, FLOPs, and parallelism.

Regulating access to large-scale compute.

Scaling law optimizing compute vs data.

Internal sensing of joint positions, velocities, and forces.

Software simulating physical laws.

Number of samples per gradient update; impacts compute efficiency, generalization, and stability.

Letting an LLM call external functions/APIs to fetch data, compute, or take actions, improving reliability.

Techniques that fine-tune small additional components rather than all weights to reduce compute and storage.

Variability introduced by minibatch sampling during SGD.

Attention mechanisms that reduce quadratic complexity.

Empirical laws linking model size, data, compute to performance.

Exact likelihood generative models using invertible transforms.

Optimizing policies directly via gradient ascent on expected reward.

Measures similarity and projection between vectors.

GNN using attention to weight neighbor contributions dynamically.

Approximating expectations via random sampling.

Storing results to reduce compute.

Control using real-time sensor feedback.

Directly optimizing control policies.

Internal representation of environment layout.

Stored compute or algorithms enabling rapid jumps.