Constrained Zero-Shot Neural Architecture Search on Small Classification Dataset

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Nuance Matters: Probing Epistemic Consistency in Causal Reasoning

Boi Faltings, Shaobo Cui, Luca Mouchel, Yiyang Feng

Previous research on causal reasoning often overlooks the subtleties crucial to understanding causal reasoning. To address this gap, our study introduces the concept of causal epistemic consistency, which focuses on the self-consistency of Large Language M ...

2024

WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models

Devis Tuia, Alexander Mathis, Marc Conrad Russwurm, Valentin Alexandre Guy Gabeff

WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models ############# Authors: Valentin Gabeff, Marc Russwurm, Devis Tuia & Alexander Mathis Affiliation: EPFL Date: January, 2024 Link to the article: ...

Zenodo2024

Privacy and Confidentiality in Machine Learning and Data Analysis: Understanding Risks and Developing Protections

Valentin Hartmann

Without the ability to collect, access and analyze data, most of nowadays research would be impossible. Without data to learn from, the field of machine learning (ML) would not exist. However, much of the particularly useful data---medical records, human b ...

EPFL2024

Facilitated machine learning for image-based fruit quality assessment

Fernando Perez Cruz

Image-based machine learning models can be used to make the sorting and grading of agricultural products more efficient. In many regions, implementing such systems can be difficult due to the lack of centralization and automation of postharvest supply chai ...

2023

RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior

Dian Wu

Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG ...

IOP Publishing Ltd2022

Depth Estimation for Egocentric Rehabilitation Monitoring Using Deep Learning Algorithms

Kamiar Aminian

Upper limb impairment is one of the most common problems for people with neurological disabilities, affecting their activity, quality of life (QOL), and independence. Objective assessment of upper limb performance is a promising way to help patients with n ...

MDPI2022

Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning

Henry Markram, Marc-Oliver Gewaltig

From the computational point of view, musculoskeletal control is the problem of controlling high degrees of freedom and dynamic multi-body system that is driven by redundant muscle units. A critical challenge in the control perspective of skeletal joints w ...

2022

Cellpose training data and scripts from "Machine learning for histological annotation and quantification of cortical layers"

Olivier Burri, Julie Meystre, Jean Nicolas Jacquemier

This Workflow contains all the material necessary to reproduce the cells detection, thanks to the QuPath performed in the paper "Machine learning for histological annotation and quantification of cortical layers" Inside this workflow and dataset, you wi ...

Zenodo2021

Detecting Freezing of Gait in Parkinson's Disease Patient via Deep Residual Network

Mohamed Bouri, Solaiman Shokur

Freezing of Gait (FoG) is a common condition in patients with Parkinson's disease (PD). It often leads to falls, and it severely affects the patient's quality of life. Although the neural mechanism of FoG is not well-known, wearable sensorbased assistive s ...

IEEE2021