Results for "representation learning"

Chooses which experts process each token.

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

Formal framework for sequential decision-making under uncertainty.

Set of all actions available to the agent.

Expected cumulative reward from a state or state-action pair.

Fundamental recursive relationship defining optimal value functions.

Inferring sensitive features of training data.

Embedding signals to prove model ownership.

Models that define an energy landscape rather than explicit probabilities.

Models that learn to generate samples resembling training data.

Learns the score (∇ log p(x)) for generative sampling.

Assigning category labels to images.

Joint vision-language model aligning images and text.

Predicting future values from past observations.

End-to-end process for model training.

Centralized repository for curated features.

Running predictions on large datasets periodically.

Measures similarity and projection between vectors.

Using production outcomes to improve models.

Ensuring AI systems pursue intended human goals.

Ensuring learned behavior matches intended objective.

Using limited human feedback to guide large models.

Model behaves well during training but not deployment.

Asking model to review and improve output.

Applying learned patterns incorrectly.

Train/test environment mismatch.

Model relies on irrelevant signals.

Startup latency for services.

Running models locally.

AI systems that perceive and act in the physical world through sensors and actuators.