Results for "demonstration-based"

Methods like Adam adjusting learning rates dynamically.

Guaranteed response times.

Software simulating physical laws.

Artificial environment for training/testing agents.

Predicts next state given current state and action.

Directly optimizing control policies.

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.

Architecture that retrieves relevant documents (e.g., from a vector DB) and conditions generation on them to reduce hallucinations.