Results for "compute-data-performance"

Increasing model capacity via compute.

Hardware resources used for training/inference; constrained by memory bandwidth, FLOPs, and parallelism.

Regulating access to large-scale compute.

Scaling law optimizing compute vs data.

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

Stored compute or algorithms enabling rapid jumps.

Storing results to reduce compute.

Control using real-time sensor feedback.

Number of samples per gradient update; impacts compute efficiency, generalization, and stability.

Techniques that fine-tune small additional components rather than all weights to reduce compute and storage.

GNN using attention to weight neighbor contributions dynamically.

Directly optimizing control policies.

Exact likelihood generative models using invertible transforms.

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

Variability introduced by minibatch sampling during SGD.

Measures similarity and projection between vectors.

Attention mechanisms that reduce quadratic complexity.

Approximating expectations via random sampling.

Optimizing policies directly via gradient ascent on expected reward.

Internal representation of environment layout.

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

Increasing performance via more data.

Observing model inputs/outputs, latency, cost, and quality over time to catch regressions and drift.

Separating data into training (fit), validation (tune), and test (final estimate) to avoid leakage and optimism bias.

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

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

Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.

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

A table summarizing classification outcomes, foundational for metrics like precision, recall, specificity.

Harmonic mean of precision and recall; useful when balancing false positives/negatives matters.