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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4 results for “coverage-guided”

TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzingstat.ML

TensorFuzz automates neural network debugging with fuzzing.

problem Debugging neural networks for rare inputs is challenging.

method Coverage-guided fuzzing with approximate nearest neighbor algorithms.

result TensorFuzz discovers numerical errors and undesirable behavior.

neural networks debugging fuzzing coverage-guided approximate nearest neighbor

2018-07-28abs ↗pdf ↗

DeepSmartFuzzer: Reward Guided Test Generation For Deep Learningcs.LG

A new method for testing deep learning models using CGF.

problem Traditional testing methods fail to cover corner cases in DNNs.

method Monte Carlo Tree Search for coverage-guided search.

result Generated inputs result in higher coverage than previous methods.

Deep Learning Coverage Guided Fuzzing Monte Carlo Tree Search Deep Neural Networks Adversarial Inputs

2019-11-24abs ↗pdf ↗

Learning Transferable Graph Explorationcs.LG

Paper tackles efficient exploration of unseen graph-structured environments.

problem Efficient exploration of unseen graph-structured environments.

method Learning to explore framework using reinforcement learning and graph-structured memory.

result Approach outperforms hand-engineered methods in software testing and real-world applications.

reinforcement learning graph exploration software testing map building coverage-guided testing

2019-10-28abs ↗pdf ↗

DeepEvolution: A Search-Based Testing Approach for Deep Neural Networkscs.LG

DeepEvolution improves testing of deep neural networks by generating diverse test cases.

problem Lack of diversity in generated test cases from random fuzzing or transformations.

method Search-based approach using metaheuristics to ensure diversity in test cases.

result DeepEvolution significantly increases neuronal coverage and detects latent defects.

Deep Learning Testing Metaheuristics Coverage Diversity

2019-09-05abs ↗pdf ↗