Publications associées à Test de Wald

Machine Learning Security in Industry: A Quantitative Survey

Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze at ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

A note on universal inference

Anthony Christopher Davison, Timmy Rong Tian Tse

Universal inference enables the construction of confidence intervals and tests without regularity conditions by splitting the data into two parts and appealing to Markov's inequality. Previous investigations have shown that the cost of this generality is a ...

WILEY2022

Experimental characterisation of textile compaction response: A benchmark exercise

Véronique Michaud, Baris Çaglar, Christophe Lopez

This paper reports the results of an international benchmark exercise on the measurement of fibre bed compaction behaviour. The aim was to identify aspects of the test method critical to obtain reliable results and to arrive at a recommended test procedure ...

2021

A Fundamental Limit of Distributed Hypothesis Testing Under Memoryless Quantization

Ali H. Sayed, Emre Telatar, Mert Kayaalp, Yunus Inan

We consider a distributed binary hypothesis testing setup where multiple nodes send quantized information to a central processor, which is oblivious to the nodes’ statistics. We study the regime where the type-II error decays exponentially and the type-I e ...

2021

NovelT2Mapping for Evaluating Cervical Cancer Features by Providing QuantitativeT2Maps and Synthetic Morphologic Images: A Preliminary Study

Tobias Kober, Tom Hilbert, Yong Zhang, Meng Yang, Jie Liu

Background The application value of T(2)mapping in evaluating cervical cancer (CC) features remains unclear. Purpose To investigate the role of T(2)values in evaluating CC classification, grade, and lymphovascular space invasion (LVSI) in comparison to app ...

WILEY2020

Learning new physics from a machine

Raffaele Tito D'Agnolo, Andrea Wulzer

We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The virtues of neural networks as unbiased function approximants make them particu ...

AMER PHYSICAL SOC2019

On null hypotheses in survival analysis

Mats Julius Stensrud, Pal Christie Ryalen

The conventional nonparametric tests in survival analysis, such as the log-rank test, assess the null hypothesis that the hazards are equal at all times. However, hazards are hard to interpret causally, and other null hypotheses are more relevant in many s ...

2019

Angular Accuracy of Steerable Feature Detectors

Michaël Unser, Julien René Pierre Fageot, Arash Amini, Zsuzsanna Püspöki, John Paul Ward

The detection of landmarks or patterns is of interest for extracting features in biological images. Hence, algorithms for finding these keypoints have been extensively investigated in the literature, and their localization and detection properties are well ...

SIAM PUBLICATIONS2019

Validation of probabilistic classifiers

Michel Bierlaire, Timothy Michael Hillel

Non-parametric probabilistic classification models are increasingly being investigated as an alternative to Discrete Choice Models (DCMs), e.g. for predicting mode choice. There exist many strategies within the literature for model selection between DCMs, ...

2018

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Test de Wald

Machine Learning Security in Industry: A Quantitative Survey

A note on universal inference

Experimental characterisation of textile compaction response: A benchmark exercise

A Fundamental Limit of Distributed Hypothesis Testing Under Memoryless Quantization

NovelT2Mapping for Evaluating Cervical Cancer Features by Providing QuantitativeT2Maps and Synthetic Morphologic Images: A Preliminary Study

On long time behavior of solutions to nonlinear dispersive equations

Learning new physics from a machine

On null hypotheses in survival analysis

Angular Accuracy of Steerable Feature Detectors

Validation of probabilistic classifiers

Graph Chatbot

Chattez avec Graph Search

NovelT2Mapping for Evaluating Cervical Cancer Features by Providing QuantitativeT2Maps and Synthetic Morphologic Images: A Preliminary Study

Machine Learning Security in Industry: A Quantitative Survey

A note on universal inference

On long time behavior of solutions to nonlinear dispersive equations

Experimental characterisation of textile compaction response: A benchmark exercise

Validation of probabilistic classifiers

A Fundamental Limit of Distributed Hypothesis Testing Under Memoryless Quantization

Angular Accuracy of Steerable Feature Detectors

On null hypotheses in survival analysis

Learning new physics from a machine