Results for "data-driven"

Devices measuring physical quantities (vision, lidar, force, IMU, etc.).

External sensing of surroundings (vision, audio, lidar).

High-fidelity virtual model of a physical system.

Randomizing simulation parameters to improve real-world transfer.

Differences between simulated and real physics.

RL using learned or known environment models.

Estimating robot position within a map.

AI systems assisting clinicians with diagnosis or treatment decisions.

Differences between training and deployed patient populations.

AI predicting crime patterns (highly controversial).

Requirement to reveal AI usage in legal decisions.

Identifying suspicious transactions.

AI proposing scientific hypotheses.

Training one model on multiple tasks simultaneously to improve generalization through shared structure.

Configuration choices not learned directly (or not typically learned) that govern training or architecture.

A scalar measure optimized during training, typically expected loss over data, sometimes with regularization terms.

A robust evaluation technique that trains/evaluates across multiple splits to estimate performance variability.

Halting training when validation performance stops improving to reduce overfitting.

A gradient method using random minibatches for efficient training on large datasets.

Randomly zeroing activations during training to reduce co-adaptation and overfitting.

Nonlinear functions enabling networks to approximate complex mappings; ReLU variants dominate modern DL.

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

Attention where queries/keys/values come from the same sequence, enabling token-to-token interactions.

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

Crafting prompts to elicit desired behavior, often using role, structure, constraints, and examples.

Achieving task performance by providing a small number of examples inside the prompt without weight updates.

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

Updating a pretrained model’s weights on task-specific data to improve performance or adapt style/behavior.

Fine-tuning on (prompt, response) pairs to align a model with instruction-following behaviors.

A preference-based training method optimizing policies directly from pairwise comparisons without explicit RL loops.