dimensional collapse, self-supervised learning
All samples map to same representation.
656 technical terms and definitions
All samples map to same representation.
Allowed variation in dimensions.
Compress high-dimensional embeddings (PCA UMAP) for efficiency.
Dimensionality reduction projects embeddings to lower dimensions while preserving information.
Reduce dimensions with PCA, t-SNE, UMAP. Visualization, efficiency.
Self-supervised vision learning.
High-quality features from DINO.
Self-supervised via distillation.
Improved DINO for vision foundation models.
Series diodes for ESD.
Diode temperature sensors exploit forward voltage temperature dependence for on-chip thermal monitoring.
Disentanglement through matching distributions.
Direct convolution implements spatial operation without transformations suitable for small kernels on specialized hardware.
Direct forecasting trains separate models for each forecast horizon avoiding error accumulation.
Optimize policy directly from preferences without explicit reward.
Tunneling through thin barriers.
Bond wafers without intermediate layer.
Directed information quantifies causal influence by measuring predictive information flow from one series to another.
Direct-Recursive strategy combines benefits of direct and recursive forecasting through ensemble or hybrid approaches.
Split monolithic design into chiplets.
Disagreement-based exploration uses prediction disagreement among ensemble models as intrinsic motivation for state exploration.
Plan to restore service after major failure.
Analyze text structure and coherence.
Predict connectives between sentences.
Identify how sentences relate.
Diffusion models for discrete data like text.
Model fab as discrete events.
Discrete representations use categorical latent variables enabling autoregressive modeling.
Systems with event-driven dynamics.
Discriminant analysis finds linear combinations of variables separating classes.
Discrimination ratio compares tolerance width to measurement uncertainty assessing adequacy.
Ability to detect small differences.
Use different rates for different layers.
Predict diagnosis from clinical notes.
Predict disease trajectory.
Separate content and position attention.
Separate independent factors of variation.
Disentangled representations separate independent factors of variation enabling controllable generation.
Over-polishing that creates concave dips in metal lines.
Identify deliberately false information.
Circular dislocations from damage.
When model affects groups differently.
Priorities for lot processing.
Rules for selecting which lots to process next.
Decide fate of non-conforming material.
Smaller faster distilled version ofBERT.
Knowledge distillation loss matches student to teacher soft targets. Temperature softens distributions.
Additional token for knowledge distillation.
Distillation trains small student model to mimic large teacher. Faster inference, similar quality.
Fast single-step models.