Hardware-in-the-loop training for lensless imaging with a programmable mask

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Background and Objective: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multiple app ...

Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unseen categories. We propose a practica ...

Recent advances in deep learning and Vision-Language Models (VLM) have enabled efficient transfer to downstream tasks even when limited labelled training data is available, as well as for text to be directly compared to image content. These properties of V ...

Reasoning about distance is indispensable for establishing or avoiding contact in manipulation tasks. To this end, we present an online approach for learning implicit representations of signed distance using piecewise polynomial basis functions. Starting f ...

In recent years, the field of Natural Language Processing has seen significant revolution by the introduction of Transformers, a stack of multiple layers of attention and non-linearity, capable of performing almost any task and the backbone for large found ...

With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage ...

Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time raw data into digital twins (DTs). However, sensors might be unreliable due to inherent issues and/or environmental conditions. This article aims at detecting anomal ...

Detecting learners in need of support is a complex process for both teachers and machines. Most prior work has devised visualization tools that allow teachers to do so by analyzing educational indicators. Other recent efforts have been devoted to models th ...

This study evaluates the efficiency of an explainable ensemble learning framework in precisely predicting the bond strength between steel sections with different surface treatments and various concrete types. Besides seven numerical features, two categoric ...

Recent generative adversarial networks (GANs) are able to generate impressive photo-realistic images. However, controllable generation with GANs remains an open research problem. Achieving controllable generation requires semantically interpretable and dis ...

We propose a computational method for controlling the output of a multimode fiber using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement meth ...

Hardware-in-the-loop training for lensless imaging with a programmable mask

Chattez avec Graph Search

FADE: Forecasting for anomaly detection on ECG

Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

Prompt-guided and multimodal landscape scenicness assessments with vision-language models

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

Discovering meaningful units from text sequences

Intraday solar irradiance forecasting using public cameras

A Machine-Learning Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins

How Close are Predictive Models to Teachers in Detecting Learners at Risk?

Explainable ensemble learning model for predicting steel section-concrete bond strength

Unsupervised Controllable Generation with Self-Training

Learning to See and Compute through Multimode Fibers