Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

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A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

As a data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving cooperative residential load scheduling problems. However, centralized training, the most common paradigm for MARL, limits large-scale deployment i ...

The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

Traditionally, studies in schizophrenia research employ a single experimental paradigm. The results typically demonstrate a significant difference between patients and controls. Subsequent studies aim to describe the underlying abnormal mechanism at the ge ...

Visual affordance segmentation identifies image regions of an object an agent can interact with. Existing methods re-use and adapt learning-based architectures for semantic segmentation to the affordance segmentation task and evaluate on small-size dataset ...

Training data mixtures greatly impact the generalization performance of large language models. Existing domain reweighting methods often rely on costly weight computations and require retraining when new data is introduced. To this end, we introduce a flex ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

Decisions about a current visual stimulus are systematically biased by recently encountered stimuli, a phenomenon known as serial dependence. In human vision, for instance, we tend to report the features of current images as more similar â i.e., an attra ...

Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

Large language models (LLMs) are transforming research in psychology and the behavioral sciences by enabling advanced text analysis at scale. Their applications range from the analysis of social media posts to infer psychological traits to the automated sc ...

When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learning (ML) algorithms, in constant time complexity. Nonetheless, when performing ...

This work presents an embedded solution for detecting and classifying head-level objects using stereo vision to assist blind individuals. A custom dataset was created, featuring five classes of head-level objects, selected based on a survey of visually imp ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

Chattez avec Graph Search

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Scalable Multi-agent Reinforcement Learning for Residential Load Scheduling Under Data Governance

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

Sequential Reasoning with Socially Caused Beliefs

Variability is the rule: Neurophysiology and contextual visual processing in schizophrenia

Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

CHAMELEON: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Serial Dependence in Human Visual Perception and Decision-Making

A comparative analysis of gene expression profiling by statistical and machine learning approaches

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (1/6)

From Embeddings to Explainability: A Tutorial on Large-Language-Model-Based Text Analysis for Behavioral Scientists

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (2/6)

Embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedback

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (3/6)

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (4/6)

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (5/6)