The Food Recognition Benchmark: Using Deep Learning to Recognize Food in Images

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Uncovering the neural architectures that enable robust and flexible motor control is a fundamental challenge in neuroscience. Understanding the neural circuitry organisation at a cellular level is crucial for advancing our knowledge and developing interven ...

In crowding, perception of a target deteriorates in the presence of nearby elements. As the entire stimulus configuration across large parts of the visual field influences crowding and not just nearby elements, low-level explanations, such as local pooling ...

We present a novel pipeline that uses a convolutional neural network (CNN) to improve the detection capability of near-Earth asteroids (NEAs) in the context of planetary defense. Our work aims to minimize the dependency on human intervention of the current ...

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Over the years, multimodal mobile sensing has been used extensively for inferences regarding health and well-being, behavior, and context. However, a significant challenge hindering the widespread deployment of such models in real-world scenarios is the is ...

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of ...

This paper investigates the potential impact of deep generative models on the work of creative professionals, specifically focusing on fashion design. We argue that current generative modeling tools lack critical features that would make them useful creati ...

Currently, the indoor thermal environment in many buildings is controlled by conventional control techniques that maintain the indoor temperature within a prescribed deadband. The latest research provides evidence that more dynamic variations of the indoor ...

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Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic co ...

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In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relat ...

The Food Recognition Benchmark: Using Deep Learning to Recognize Food in Images

Chat with Graph Search

Uncovering neural architectures enabling robust and flexible motor control in Drosophila

Does surface completion fail to support uncrowding?

Deep Learning to Improve the Discovery of Near-earth Asteroids in the Zwicky Transient Facility

Computational Imaging: a Multi-Facet Perspective

M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial Training

A new age in protein design empowered by deep learning

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Dynamic indoor thermal environment using Reinforcement Learning-based controls: Opportunities and challenges

Understanding Deep Neural Networks using Adversarial Attacks

A Structured Dictionary Perspective on Implicit Neural Representations

Beyond potentials: Integrated machine learning models for materials

Multi-layer State Evolution Under Random Convolutional Design

Hierarchical graph representations in digital pathology

An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

The physics of energy-based models

Model-Based Systems Engineering Tool-Chain for Automated Parameter Value Selection

Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations

Toward a Collective Agenda on AI for Earth Science Data Analysis

node2coords: Graph Representation Learning with Wasserstein Barycenters