Results for "full pass through data"

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

One complete traversal of the training dataset during training.

Generator produces limited variety of outputs.

Maximizing reward without fulfilling real goal.

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

Incrementally deploying new models to reduce risk.

Processes and controls for data quality, access, lineage, retention, and compliance across the AI lifecycle.

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

Tracking where data came from and how it was transformed; key for debugging and compliance.

Gradients shrink through layers, slowing learning in early layers; mitigated by ReLU, residuals, normalization.

Protecting data during network transfer and while stored; essential for ML pipelines handling sensitive data.

Human or automated process of assigning targets; quality, consistency, and guidelines matter heavily.

When information from evaluation data improperly influences training, inflating reported performance.

Competitive advantage from proprietary models/data.

Running predictions on large datasets periodically.

Maliciously inserting or altering training data to implant backdoors or degrade performance.

Expanding training data via transformations (flips, noise, paraphrases) to improve robustness.

Training across many devices/silos without centralizing raw data; aggregates updates, not data.

Learning from data by constructing “pseudo-labels” (e.g., next-token prediction, masked modeling) without manual annotation.

When a model fits noise/idiosyncrasies of training data and performs poorly on unseen data.

Increasing performance via more data.

A formal privacy framework ensuring outputs do not reveal much about any single individual’s data contribution.

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

Generative model that learns to reverse a gradual noise process.

Generating human-like speech from text.

Models whose weights are publicly available.

Hidden behavior activated by specific triggers, causing targeted mispredictions or undesired outputs.

Asking model to review and improve output.

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

Emergence of conventions among agents.