Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 6 of 6 for “"Differential testing"”.
-
ULTRA SCALABLE METHODS FOR DIFFERENTIAL TESTING OF SPATIAL-OMIC DATA
… a statistical framework for modeling niche-differential patterns in spatial-omic data. At its core, SpotGLM applies a mixture-based generalized linear model to test for molecular features that vary in a cell type-specific manner across spatial niches. This general pipeline supports a wide …
-
Interpretability and Debugging for Distributed Privacy Preserving Machine Learning
… fault localization, we redesign traditional differential testing to operate on neuron activations produced by auto-generated inputs, exploiting the fact that faulty clients produce models with divergent activations. Second, we introduce neuron provenance, which decouples data-influence …
-
STATISTICAL FOUNDATIONS OF SINGLE CELL OPEN CHROMATIN ASSAYS
… in Single cells (PACS), which aims to conduct differential testing while addressing the sparsity and presence of multiple causal factors in complex datasets. By introducing a missing-data-corrected Cumulative Logistic Regression framework (mcCLR), we extend the conventional Generalized Linear …
-
Optimal Allocation of Resources for Screening of Donated Blood
… last extension we investigated is to include a ``differential" testing policy, in which an optimal solution is allowed to contain multiple test sets, each applied to a fraction of the total blood units. In particular, the decision-maker faces the problem of selecting a collection of test sets as …
-
Automatic test generation for the detection of performance bugs in code optimization
… that software behaves in ways it is expected to. Testing is a widely accepted method for improving software quality. Testing detects the presence of bugs by comparing the actual outcome to the expected outcome of a computation. Testing for correctness is a well-studied problem. Testing for …
-
Multiple-implementation testing of supervised learning software
… Thus, it is very critical to conduct effective testing of ML software to detect and eliminate its faults. However, testing ML software is difficult, especially on producing test oracles used for checking behavior correctness (such as using expected properties or expected test outputs). To tackle …