pac learning, pac, advanced training
Probably Approximately Correct learning provides theoretical bounds on sample complexity for learning with high probability and accuracy.
212 technical terms and definitions
Probably Approximately Correct learning provides theoretical bounds on sample complexity for learning with high probability and accuracy.
Package decapsulation removes molding compound and heat spreaders exposing die for failure analysis.
Package failure analysis investigates defects in packaging materials interconnects and interfaces using cross-sectioning acoustic microscopy and X-ray.
Package thermal modeling predicts temperature distributions using finite element analysis and computational fluid dynamics.
Packed sequences concatenate examples without padding for memory efficiency.
Padding masks prevent attention to padded positions during processing.
Padding extends shorter sequences to batch length enabling parallel processing.
Paged attention manages KV cache in non-contiguous memory blocks like virtual memory.
PagedAttention (vLLM) manages KV cache like virtual memory. Reduces fragmentation. Enables larger batches.
Equivariant molecular network.
Polarizable Atom Interaction Neural Network uses equivariant message passing for molecular property prediction.
Paired t-tests compare related measurements on same units.
Pairwise comparisons judge relative quality between two responses.
Compare two outputs and pick better one.
Pairwise ranking learns relative preferences between item pairs for recommendation ordering.
Learn from item comparisons.
Google's large-scale language model.
Create wide panoramic images.
Duplicate inheritance structures.
Parallel sampling generates multiple sequence candidates simultaneously.
Parameter binding fills function arguments with appropriate values from context.
Tradeoff in resource allocation.
Total number of trainable weights in the model.
Parameter sharing applies same weights to multiple operations enabling efficient architectures with fewer parameters.
Activations with learnable parameters.
Pareto-optimal neural architecture search discovers architectures optimal for multiple objectives like accuracy and latency.
Tight control of Lipschitz constant.
Parti generates images through autoregressive sequence modeling of visual tokens.
Source has extra classes.
Particle filter performs sequential Monte Carlo inference in non-linear non-Gaussian state space models using weighted particle representations.
Particulate abatement removes solid particles from exhaust using filters or electrostatic precipitators.
Discriminate on patches.
Patch Time Series Transformer divides series into patches reducing tokens and improving long-horizon forecasting efficiency.
Analyze patent documents with NLP.
Analyze patent documents.
Categorize patents.
Help write patent applications.
Find similar patents.
Path encoding represents architectures through enumeration of computational paths from input to output.
Intervene on specific paths.
Analyze tissue slides.
Assess patient risk levels.
Model PBTI degradation.
PC algorithm learns directed acyclic graphs from data through conditional independence tests.
Partially Connected DARTS reduces memory costs by searching over subsets of edges rather than entire architecture graph.
PCMCI+ extends PCMCI with improved conditional independence testing for high-dimensional time series.
PCMCI combines PC algorithm with momentary conditional independence testing for causal discovery in multivariate time series.
Pruned Exact Linear Time algorithm efficiently detects multiple change points in univariate time series.
Different scales for each channel.
Single scale for entire tensor.