ambipolar diffusion, device physics
Coupled electron-hole diffusion.
186 technical terms and definitions
Coupled electron-hole diffusion.
AmoebaNet uses regularized evolution for neural architecture search demonstrating that simple aging-based regularization improves discovered architectures.
AMSAA model provides statistical framework for reliability growth analysis.
Army reliability growth model.
Stochastic diffusion sampling.
Stochastic sampling following original process.
High-precision rules explaining predictions.
Neural network potential for organic molecules.
Use schedule of noise levels.
Specific augmentation strategy for Neural ODEs.
Anomaly detection identifies unusual inputs warranting careful handling.
Ansor generates optimized tensor programs through hierarchical search and learned cost models.
Create therapeutic antibodies.
Antifuse-based repair creates permanent connections to redundant memory elements by applying high voltage to form conductive links.
Support arbitrary bit-widths.
Ahead-of-time compilation generates optimized binaries before deployment reducing startup time.
Auto-generate API docs from code.
Train models to understand and use external APIs.
Generate correct sequences of API calls.
Cost of inspection and testing.
Decline genuinely harmful requests.
Approximate computing trades accuracy for efficiency using lower-precision or simplified operations.
Architecture crossover combines parent architectures by exchanging substructures creating offspring networks.
Architecture encoding represents network structures as vectors graphs or sequences enabling architecture optimization.
Architecture mutations in evolutionary NAS modify network structures through operations like adding layers or changing connections.
Invertible functions for discrete data.
Time series forecasting.
AutoRegressive Integrated Moving Average models time series through differencing and combining autoregression with moving average components.
Generate test assertions.
Different penalties for different errors.
Asynchronous generation handles multiple requests concurrently maximizing throughput.
Async/await enables concurrent I/O without threads. Event loop. Python asyncio, JavaScript promises.
Retrieval-augmented model for knowledge-intensive tasks.
Study how far attention reaches.
Trace attention patterns through network.
Attention mechanisms in time series weight historical values adaptively for improved forecasting accuracy.
Different functions of attention heads.
Scale attention by head dimension.
Binary mask indicating which tokens to attend to vs ignore (for padding).
Attention-based graph pooling learns soft cluster assignments through self-attention over node features.
Aggregate attention across layers.
Aggregate attention across layers for interpretability.
Keep first tokens for stable attention.
Transfer attention maps from teacher.
Visualize what model attends to.
Inspect attention weights to see what the model focuses on.
Attention-based explanations visualize which user history items or features influenced specific recommendations.
Use attention to weight modalities.
AttentionNAS discovers efficient attention mechanisms and architectures jointly through neural architecture search.
AttentiveNAS uses attention mechanisms to predict architecture performance from training statistics without full training.