Results for "data → model"
A subfield of AI where models learn patterns from data to make predictions or decisions, improving with experience rather than explicit rule-coding.
Learning a function from input-output pairs (labeled data), optimizing performance on predicting outputs for unseen inputs.
Learning from data by constructing “pseudo-labels” (e.g., next-token prediction, masked modeling) without manual annotation.
Methods that learn training procedures or initializations so models can adapt quickly to new tasks with little data.
A scalar measure optimized during training, typically expected loss over data, sometimes with regularization terms.
A conceptual framework describing error as the sum of systematic error (bias) and sensitivity to data (variance).
Separating data into training (fit), validation (tune), and test (final estimate) to avoid leakage and optimism bias.
Letting an LLM call external functions/APIs to fetch data, compute, or take actions, improving reliability.
A formal privacy framework ensuring outputs do not reveal much about any single individual’s data contribution.
Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.
Logging hyperparameters, code versions, data snapshots, and results to reproduce and compare experiments.
Ability to replicate results given same code/data; harder in distributed training and nondeterministic ops.
Attacks that infer whether specific records were in training data, or reconstruct sensitive training examples.
Estimating parameters by maximizing likelihood of observed data.
Learning from data generated by a different policy.
Learning only from current policy’s data.
Recovering training data from gradients.
Neural networks that operate on graph-structured data by propagating information along edges.
Sequential data indexed by time.
Identifying abrupt changes in data generation.
Trend reversal when data is aggregated improperly.
Scaling law optimizing compute vs data.
Probability of data given parameters.
Updated belief after observing data.
Belief before observing data.
Software pipeline converting raw sensor data into structured representations.
Inferring the agent’s internal state from noisy sensor data.
Learning physical parameters from data.
Artificial sensor data generated in simulation.
Combining simulation and real-world data.