Results for "learning like humans"

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

Attacks that infer whether specific records were in training data, or reconstruct sensitive training examples.

Mechanisms for retaining context across turns/sessions: scratchpads, vector memories, structured stores.

AI focused on interpreting images/video: classification, detection, segmentation, tracking, and 3D understanding.

Optimization with multiple local minima/saddle points; typical in neural networks.

Variability introduced by minibatch sampling during SGD.

A narrow minimum often associated with poorer generalization.

Early architecture using learned gates for skip connections.

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

Chooses which experts process each token.

Set of all actions available to the agent.

Formal framework for sequential decision-making under uncertainty.

Fundamental recursive relationship defining optimal value functions.

Expected cumulative reward from a state or state-action pair.

Inferring sensitive features of training data.

Embedding signals to prove model ownership.

Models that define an energy landscape rather than explicit probabilities.

Models that learn to generate samples resembling training data.

Learns the score (∇ log p(x)) for generative sampling.

Assigning category labels to images.

Joint vision-language model aligning images and text.

Predicting future values from past observations.

End-to-end process for model training.

Running predictions on large datasets periodically.

Centralized repository for curated features.

Using production outcomes to improve models.

Mathematical foundation for ML involving vector spaces, matrices, and linear transformations.

Measures similarity and projection between vectors.

Ensuring learned behavior matches intended objective.

Model behaves well during training but not deployment.