neuron coverage, interpretability
Neuron coverage measures percentage of neurons activated by test inputs for test adequacy.
265 technical terms and definitions
Neuron coverage measures percentage of neurons activated by test inputs for test adequacy.
Study individual neurons.
NeVAE generates molecules by combining VAE with neural networks for structure generation.
Advanced exploration method.
System that learns indefinitely.
Launch new product.
Create email newsletters.
Follow AI newsletters and research blogs to stay current. Field moves fast; continuous learning essential.
QA from news articles.
Predict if sentences are consecutive.
Next token prediction is GPT-style training. Predict next token given previous. Autoregressive generation.
NextItNet uses dilated convolutional neural networks for session-based recommendation capturing patterns at multiple temporal scales.
Next.js is React fullstack framework. SSR, API routes. Modern web apps.
High-performance nets without normalization.
Never Give Up combines episodic and life-long curiosity to maintain exploration throughout training by tracking state visitation in embedding space.
NHWC layout stores tensors in batch-height-width-channel order optimizing certain operations.
Ni impurity effects.
Alternative silicide material.
Current generation of quantum computers.
Algorithms for noisy intermediate-scale quantum.
Calibrated against US national standards.
Incorporate nitrogen into oxide to improve reliability.
Grow Si3N4 using SiH4 + NH3 or similar.
Nitrogen dopant or impurity.
Replace air with nitrogen.
Table-based timing model.
nlpaug augments text data. Synonym, back-translation, contextual.
Reason about image pairs.
Non-negative Matrix Factorization constrains factors to be non-negative enabling interpretable part-based representations for recommendation.
No-code AI tools. Build without programming.
Flux not requiring removal.
Apply before reflow.
No-repeat n-gram blocking prevents exact phrase repetition.
Prevent repeating n-grams.
Adaptive HMC algorithm.
Node migration transitions designs to newer process technologies.
Learn node embeddings via random walks.
Noise augmentation adds background sounds to clean speech improving recognition in noisy conditions.
Train by contrasting data and noise distributions.
Learn by contrasting data with noise.
Noise contrastive estimation trains unnormalized models by discriminating between data samples and artificially generated noise samples.
Uncontrollable variables causing variation.
Minimum detectable signal.
Noise multiplier scales added noise controlling privacy-utility tradeoff.
Variance schedule for diffusion.
Train robust models despite label errors.
Noisy student training iteratively generates pseudo-labels with a teacher model and trains larger student models with added noise.
Nominal-the-best characteristics should achieve specific target values.
Generate all tokens at once.
Generate entire sequence in parallel.