contrastive learning for disentanglement,representation learning
Use contrastive loss for disentanglement.
1,005 technical terms and definitions
Use contrastive loss for disentanglement.
Contrastive learning trains embeddings to separate positive from negative pairs.
Learn representations by pulling similar examples together and pushing different ones apart.
Contrastive learning trains on positive/negative pairs. Self-supervised.
Attract positives repel negatives.
Self-supervised learning via predicting future in latent space.
Contrastive prompting presents both positive and negative examples clarifying task boundaries.
Contrastive search balances coherence and diversity through degeneration penalty.
Balance coherence and diversity.
Contribution plots identify variables responsible for multivariate control violations.
Choose appropriate chart type.
Variables we can set.
Boundaries on control chart.
Control methods prevent errors from occurring through design constraints.
Control plans document monitoring and control methods for critical characteristics.
Document specifying quality controls.
Control points force hard-to-control nodes to specific values improving controllability.
Guide generation with constraints.
Steer outputs toward desired attributes (tone style sentiment).
Generate captions with constraints.
Model sequences as responses to control signals.
Test change under controlled conditions.
ControlNet adds spatial conditioning to diffusion models through auxiliary networks.
Strength of control signal.
Add spatial control to diffusion.
ControlNet adds spatial conditioning (edges, pose, depth) to diffusion models for precise image control.
ControlNet adds spatial conditioning to diffusion. Pose, edge, depth control. Precise image generation.
Add spatial control signals (edges depth poses) to guide image generation.
Conv-TasNet improves TasNet by using deeper temporal convolutional networks with increased receptive fields.
ConvE applies 2D convolutions over reshaped embeddings for efficient knowledge graph completion.
When training loss stops improving model has learned the pattern.
Multi-turn conversation. Context-aware responses.
Multi-turn conversation maintains context across messages. History grows, eventually truncate.
Track conversation history.
Convolutional LSTM for video.
Simple convolution-based mixing.
Simple architecture using convolutions.
Pure attention or MLP models.
Cooling water systems dissipate heat from process tools and facilities.
Location-aware attention mechanism.
3D measurement system.
Coordinator agents manage task allocation and synchronization among team members.
Causal reasoning.
Leads in same plane.
Heat treatment for grain growth.
Copper chemical mechanical polishing removes excess copper after electroplating achieving planar surfaces for subsequent layer deposition.
Cu impurity causing junction leakage.
Inlay copper into trenches.
Damascene process using copper as interconnect metal.
Use Cu instead of Al for lower resistance.