Results for "structure prediction"

Deep learning system for protein structure prediction.

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

A function measuring prediction error (and sometimes calibration), guiding gradient-based optimization.

A model that assigns probabilities to sequences of tokens; often trained by next-token prediction.

Probabilistic graphical model for structured prediction.

Low-latency prediction per request.

Learning by minimizing prediction error.

Learning structure from unlabeled data, such as discovering groups, compressing representations, or modeling data distributions.

Training with a small labeled dataset plus a larger unlabeled dataset, leveraging assumptions like smoothness/cluster structure.

Training one model on multiple tasks simultaneously to improve generalization through shared structure.

Designing input features to expose useful structure (e.g., ratios, lags, aggregations), often crucial outside deep learning.

Automatically learning useful internal features (latent variables) that capture salient structure for downstream tasks.

When a model cannot capture underlying structure, performing poorly on both training and test data.

Crafting prompts to elicit desired behavior, often using role, structure, constraints, and examples.

Extension of convolution to graph domains using adjacency structure.

Simplified Boltzmann Machine with bipartite structure.

Recovering 3D structure from images.

Predicting protein 3D structure from sequence.

Training objective where the model predicts the next token given previous tokens (causal modeling).

Shift in model outputs.

Predicting case success probabilities.