Results for "meaning-based retrieval"

Space of all possible robot configurations.

Sampling-based motion planner.

Learning by minimizing prediction error.

Software regulated as a medical device.

Acting to minimize surprise or free energy.

Deep learning system for protein structure prediction.

Internal representation of the agent itself.

A subfield of AI where models learn patterns from data to make predictions or decisions, improving with experience rather than explicit rule-coding.

The field of building systems that perform tasks associated with human intelligence—perception, reasoning, language, planning, and decision-making—via algori...

Training with a small labeled dataset plus a larger unlabeled dataset, leveraging assumptions like smoothness/cluster structure.

Learning where data arrives sequentially and the model updates continuously, often under changing distributions.

A continuous vector encoding of an item (word, image, user) such that semantic similarity corresponds to geometric closeness.

A function measuring prediction error (and sometimes calibration), guiding gradient-based optimization.

One complete traversal of the training dataset during training.

Methods to set starting weights to preserve signal/gradient scales across layers.

Mechanism that computes context-aware mixtures of representations; scales well and captures long-range dependencies.

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

Injects sequence order into Transformers, since attention alone is permutation-invariant.

A high-capacity language model trained on massive corpora, exhibiting broad generalization and emergent behaviors.

Predicts masked tokens in a sequence, enabling bidirectional context; often used for embeddings rather than generation.

Maximum number of tokens the model can attend to in one forward pass; constrains long-document reasoning.

The text (and possibly other modalities) given to an LLM to condition its output behavior.

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

Model trained to predict human preferences (or utility) for candidate outputs; used in RLHF-style pipelines.

Controlled experiment comparing variants by random assignment to estimate causal effects of changes.

Ordering training samples from easier to harder to improve convergence or generalization.

Artificially created data used to train/test models; helpful for privacy and coverage, risky if unrealistic.

Central system to store model versions, metadata, approvals, and deployment state.

Search algorithm for generation that keeps top-k partial sequences; can improve likelihood but reduce diversity.

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