dual-channel hin, graph neural networks
Dual-channel heterogeneous information networks process structure and semantics separately then combine.
3,145 technical terms and definitions
Dual-channel heterogeneous information networks process structure and semantics separately then combine.
Duane model represents reliability growth as power law relating failures to test time.
Reliability growth model.
Automate M&A due diligence.
Duet AI is Google Cloud assistant. Coding, cloud operations.
Find copy-pasted code.
Identify repeated tokens.
Dye penetration uses colored fluids to reveal package cracks delamination and moisture ingress paths.
Network structure adapts during inference.
Dynamic batching forms groups from arriving requests without waiting for fixed batch sizes.
Vary number of layers per input.
Dynamic factor models represent multivariate time series as driven by smaller number of unobserved dynamic factors.
Handle evolving graph structures.
Dynamic inference adapts computation per input using early exit or conditional execution.
Dynamic Linear Models represent time series through observation and system equations with Gaussian distributions.
Dynamic NeRF models time-varying scenes with deformation or flow fields.
Networks that adapt structure at runtime.
Dynamic precision adapts numeric precision based on training phase or layer requirements.
Dynamic pruning adapts sparsity patterns during inference based on input characteristics.
Determine quantization at runtime.
Process different input resolutions.
Route information between capsules.
Train sparse networks from scratch.
Vary channel count per input.
DyRep models dynamic graphs through temporal point processes and representation learning with self-attention.
Dynamic Self-Attention Network uses structural and temporal self-attention to learn node representations in dynamic graphs with evolving topologies.
E(n) equivariant graph networks preserve symmetries under rotations translations and reflections in n-dimensional Euclidean space.
Find relevant documents in litigation.
GNNs respecting 3D symmetries.
Electronic waste recycling recovers valuable materials from end-of-life semiconductor devices through dismantling and material separation.
Early exit networks allow samples to exit at intermediate layers when confidence is sufficient.
Exit early from network when confident.
Early exit allows simpler queries to terminate processing at shallow layers.
Combine raw features early.
Early stopping in NAS terminates poor architecture training early based on learning curve predictions.
Stop training when validation performance stops improving.
Efficient Channel Attention uses 1D convolution for lightweight channel attention.
Interpret electrocardiograms.
Economic lot size optimizes production batch quantities considering setup and carrying costs.
Economic order quantity minimizes total ordering and holding costs for purchased items.
Economizers increase outdoor air intake for cooling when conditions are favorable reducing mechanical cooling.
Advanced timing model.
Easy Data Augmentation applies simple operations like synonym replacement random insertion deletion and swap to augment text.
Edge AI deploys models on local devices minimizing latency and privacy concerns.
Edge conditioning uses edge maps to control generated image structure.
Edge pooling contracts edges to coarsen graphs learning which edges to collapse for hierarchical representations.
Pool edges to coarsen graph.
Discover subnetworks via masking.
Split computation between edge and cloud.
Electronic Data Interchange automates business document exchange between supply chain partners.