Results for "compute-data-performance"

Optimal control for linear systems with quadratic cost.

Mathematical representation of friction forces.

Artificial environment for training/testing agents.

Planning via artificial force fields.

Humans assist or override autonomous behavior.

Human controlling robot remotely.

Hard constraints preventing unsafe actions.

Failure to detect present disease.

Deep learning system for protein structure prediction.

Modeling chemical systems computationally.

Rate at which AI capabilities improve.

Methods that learn training procedures or initializations so models can adapt quickly to new tasks with little data.

One complete traversal of the training dataset during training.

A parameterized function composed of interconnected units organized in layers with nonlinear activations.

A datastore optimized for similarity search over embeddings, enabling semantic retrieval at scale.

Networks with recurrent connections for sequences; largely supplanted by Transformers for many tasks.

Reinforcement learning from human feedback: uses preference data to train a reward model and optimize the policy.

A high-capacity language model trained on massive corpora, exhibiting broad generalization and emergent behaviors.

Ability to replicate results given same code/data; harder in distributed training and nondeterministic ops.

Model-generated content that is fluent but unsupported by evidence or incorrect; mitigated by grounding and verification.

Attacks that manipulate model instructions (especially via retrieved content) to override system goals or exfiltrate data.

Models that process or generate multiple modalities, enabling vision-language tasks, speech, video understanding, etc.

Updating beliefs about parameters using observed evidence and prior distributions.

Bayesian parameter estimation using the mode of the posterior distribution.

Using same parameters across different parts of a model.

A single attention mechanism within multi-head attention.

Probabilistic energy-based neural network with hidden variables.

Probabilistic graphical model for structured prediction.

Probabilistic model for sequential data with latent states.

Autoencoder using probabilistic latent variables and KL regularization.