Results for "full pass through data"

Sequential data indexed by time.

Artificial sensor data generated in simulation.

Combining simulation and real-world data.

A learning paradigm where an agent interacts with an environment and learns to choose actions to maximize cumulative reward.

An RNN variant using gates to mitigate vanishing gradients and capture longer context.

Generates sequences one token at a time, conditioning on past tokens.

Stepwise reasoning patterns that can improve multi-step tasks; often handled implicitly or summarized for safety/privacy.

Breaking documents into pieces for retrieval; chunk size/overlap strongly affect RAG quality.

Constraining outputs to retrieved or provided sources, often with citation, to improve factual reliability.

Ensuring model behavior matches human goals, norms, and constraints, including reducing harmful or deceptive outputs.

Rules and controls around generation (filters, validators, structured outputs) to reduce unsafe or invalid behavior.

Techniques to understand model decisions (global or local), important in high-stakes and regulated settings.

Studying internal mechanisms or input influence on outputs (e.g., saliency maps, SHAP, attention analysis).

Training a smaller “student” model to mimic a larger “teacher,” often improving efficiency while retaining performance.

Raw model outputs before converting to probabilities; manipulated during decoding and calibration.

Reconstructing a model or its capabilities via API queries or leaked artifacts.

A discipline ensuring AI systems are fair, safe, transparent, privacy-preserving, and accountable throughout lifecycle.

Tendency to trust automated suggestions even when incorrect; mitigated by UI design, training, and checks.

A system that perceives state, selects actions, and pursues goals—often combining LLM reasoning with tools and memory.

Early architecture using learned gates for skip connections.

Methods for breaking goals into steps; can be classical (A*, STRIPS) or LLM-driven with tool calls.

Tradeoffs between many layers vs many neurons per layer.

Assigning labels per pixel (semantic) or per instance (instance segmentation) to map object boundaries.

Strategy mapping states to actions.

Expected return of taking action in a state.

Combines value estimation (critic) with policy learning (actor).

Coordination arising without explicit programming.

Required human review for high-risk decisions.

Extracting system prompts or hidden instructions.

Embedding signals to prove model ownership.