On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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3 results for “fault-revealing”

Manifold-based Test Generation for Image Classifierscs.LG

This paper proposes a method to generate realistic test cases for image classifiers.

problem Ensuring neural networks for image classification are correct with adequate realistic test data.

method Captures patterns in a large input data space using a manifold, then generates fault-revealing test cases.

result Generates thousands of realistic yet fault-revealing test cases efficiently for well-trained models.

image classification neural networks test generation conditional variational autoencoder manifold

2020-02-15abs ↗pdf ↗

Input Prioritization for Testing Neural Networkscs.SE

This paper proposes using neural network sentiment measures to prioritize test inputs.

problem High cost of obtaining test oracle data limits testing efficacy of neural networks.

method Proposes using confidence, uncertainty, and surprise measures to prioritize test inputs.

result Sentiment measures can effectively flag inputs revealing DNN weaknesses.

neural networks testing sentiment measures fault-revealing MNIST

2019-01-11abs ↗pdf ↗

Manifold for Machine Learning Assurancecs.LG

A novel ML verification technique using manifold learning.

problem Ensuring trust in machine learning systems.

method Variational autoencoder for extracting a low-dimensional manifold from high-dimensional training data.

result The manifold provides diverse test data, fault-revealing test cases, and independent runtime trust assessment.

machine learning verification validation manifold learning variational autoencoder

2020-02-08abs ↗pdf ↗