Publications associées à Robust measures of scale

Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Zhenyu Zhu

We study the average robustness notion in deep neural networks in (selected) wide and narrow, deep and shallow, as well as lazy and non-lazy training settings. We prove that in the under-parameterized setting, width has a negative effect while it improves ...

2022

Towards Robust Vision Transformer

Shaokai Ye, Yuan He

Recent advances on Vision Transformer (ViT) and its improved variants have shown that self-attention-based networks surpass traditional Convolutional Neural Networks (CNNs) in most vision tasks. However, existing ViTs focus on the standard accuracy and com ...

IEEE COMPUTER SOC2022

Robustness Against Read Committed: A Free Transactional Lunch

Christoph Koch, Bas Ketsman

Transaction processing is a central part of most database applications. While serializability remains the gold standard for desirable transactional semantics, many database systems offer improved transaction throughput at the expense of introducing potenti ...

ASSOC COMPUTING MACHINERY2022

Scientific rewards for biomedical specialization are large and persistent

Gaétan Jean A de Rassenfosse, Orion B Penner, Kyle William Higham

Background While specialization plays an essential role in how scientific research is pursued, we understand little about its effects on a researcher's impact and career. In particular, the extent to which one specializes within their chosen fields likely ...

BMC2022

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas

Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data. To fix this vulnerability, prior works have built complex data augmentation strategies, combining multi ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

SafeAMC: Adversarial training for robust modulation classification models

Pascal Frossard, Javier Alejandro Maroto Morales

In communication systems, there are many tasks, like modulation classification, for which Deep Neural Networks (DNNs) have obtained promising performance. However, these models have been shown to be susceptible to adversarial perturbations, namely impercep ...

IEEE2022

Report of RILEM TC 267-TRM phase 2: optimization and testing of the robustness of the R3 reactivity tests for supplementary cementitious materials

Karen Scrivener, Alastair Thomas Matthew Marsh, Ruben Anton Snellings, Mohsen Ben Haha, François Henri Avet, Shashank Bishnoi, Xuerun Li, Pawel Tadeusz Durdzinski, Diana Londoño Zuluaga, Anuj Parashar

The results of phase 1 of an interlaboratory test, coordinated by the RILEM TC 267-TRM "Tests for Reactivity of Supplementary Cementitious Materials" showed that the R-3 (rapid, relevant, reliable) test method, by measurement of heat release or bound water ...

SPRINGER2022

Deciding Robustness for Lower SQL Isolation Levels

Christoph Koch, Bas Ketsman

While serializability always guarantees application correctness, lower isolation levels can be chosen to improve transaction throughput at the risk of introducing certain anomalies. A set of transactions is robust against a given isolation level if every p ...

ASSOC COMPUTING MACHINERY2022

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

Daniel Kuhn, Viet Anh Nguyen, Bahar Taskesen

Least squares estimators, when trained on a few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additional labeled training samples from a source distribution that is close to th ...

2021

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Robust measures of scale

Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)

Towards Robust Vision Transformer

Robustness Against Read Committed: A Free Transactional Lunch

Scientific rewards for biomedical specialization are large and persistent

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

SafeAMC: Adversarial training for robust modulation classification models

Report of RILEM TC 267-TRM phase 2: optimization and testing of the robustness of the R3 reactivity tests for supplementary cementitious materials

Concurrency control for database theorists

Deciding Robustness for Lower SQL Isolation Levels

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

Graph Chatbot

Chattez avec Graph Search

Towards Robust Vision Transformer

Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)

Scientific rewards for biomedical specialization are large and persistent

Robustness Against Read Committed: A Free Transactional Lunch

SafeAMC: Adversarial training for robust modulation classification models

Report of RILEM TC 267-TRM phase 2: optimization and testing of the robustness of the R3 reactivity tests for supplementary cementitious materials

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

Concurrency control for database theorists

Deciding Robustness for Lower SQL Isolation Levels

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts