Traffic Signal Prediction on Transportation Networks Using Spatio-Temporal Correlations on Graphs

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The increasing accumulation of medical data brings the hope of data-driven medical decision-making, but data's increasing complexity—as text or images in electronic health records—calls for complex models, such as machine learning. Here, we review how mach ...

Current progress in artificial intelligence is centered around so-called large language models that consist of neural networks processing long sequences of high-dimensional vectors called tokens. Statistical physics provides powerful tools to study the fun ...

This thesis is concerned with improving the interpretability of network summaries in both a theoretical and an applied framework, with an application to clinically relevant problems in neuroscience. The theoretical part of this thesis is concerned with gra ...

The EPFL Graph Platform is an open-source data infrastructure designed for academic institutions. It organizes educational and institutional data into a semantically interconnected knowledge graph, making it accessible through a graph-based search engine a ...

This paper studies the problem of interconnected agents collaborating to track a dynamic state from partially informative observations, where the dynamic state evolves according to a slowly varying finite-state Markov chain. Although the centralized versio ...

Variational quantum computing offers a flexible computational approach with a broad range of applications. However, a key obstacle to realizing their potential is the barren plateau (BP) phenomenon. When a model exhibits a BP, its parameter optimization la ...

Since the 1960s, the prevalence of pollen allergy has increased worldwide in developed countries and this trend is continuing, exacerbated by climate change. In the 20th century, operational pollen monitoring networks have been set-up to respond to the nee ...

In recent decades, the rapid growth of cloud computing has transformed the technological landscape and profoundly impacted our daily lives, driven by the demand for secure, flexible, and cost-effective computing solutions. To meet these demands, computing ...

Time course measurements are used for many applications in biomedical research, ranging from growth curves to drug efficacy testing and high-throughput screening. Statistical methods used to analyze the resulting longitudinal data, such as t-tests or repea ...

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 ...

Continued greenhouse gas emissions will lead to increasing global warming. Effective adaptation and mitigation policies depend on accurately estimating climate sensitivity, the Earth's surface temperature response to increasing CO2 emissions. Global Climat ...

Cross-view localization, the task of estimating a camera's 3-degrees-of-freedom (3-DoF) pose by aligning ground-level images with satellite images, is crucial for large-scale outdoor applications like autonomous navigation and augmented reality. Existing m ...

Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evaded when trying to approximate even a single ReLU neuron (Bach, 2017). In thi ...

Project-based learning (PBL), as one of the effective teaching and learning approaches in engineering education, can create opportunities for students to address problems with real-world applications, integrate and apply knowledge, and practice and develop ...

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 ...

Reaction additives are critical in dictating the outcomes of chemical processes making their effective screening vital for research. Conventional high-throughput experimentation tools can screen multiple reaction components rapidly. However, they are prohi ...

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 ...

Many-body systems at low temperature have revealed non-trivial phases of materials, such as spin liquids, which have found applications in the evolving fields of superconductivity, nanoelectronics, and quantum computing. Their exponentially large state spa ...

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, re ...

Deep convolutional neural networks (DCNNs) have significantly advanced the field of computer vision and are often considered as plausible models for the human visual system. However, they still have several vulnerabilities in terms of adversarial attacks, ...

Traffic Signal Prediction on Transportation Networks Using Spatio-Temporal Correlations on Graphs

Chattez avec Graph Search

From Prediction to Prescription: Machine Learning and Causal Inference for the Heterogeneous Treatment Effect

Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-Dimensional Tokens

Nonparametric network summaries

GraphOntology Dataset

Fundamental Social Learning Scaling Law for Tracking Hidden Markov Models

Barren plateaus in variational quantum computing

Automatic identification of atmospheric bioaerosol using real-time imaging and fluorescence

Multi-Objective Management for Many-Core Computing Servers

biogrowleR: Enhancing Longitudinal Data Analysis

FADE: Forecasting for anomaly detection on ECG

Using dimensionality reduction and causal inference to constrain precipitation and climate

GeoDistill: Geometry-Guided Self-Distillation for Weakly Supervised Cross-View Localization

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks

Project-Based Learning Approach for an Engineering Course: Challenges and Learnings of Teaching Transversal Skills

Intraday solar irradiance forecasting using public cameras

Bayesian optimisation for additive screening and yield improvements - beyond one-hot encoding

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

Exact Sampling for Classical and Quantum Many-Body Systems

SuperAnimal pretrained pose estimation models for behavioral analysis

Towards Robust Vision Models