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

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Holography enables quantitative phase sensing through light wave interference, allowing interesting imaging modalities applied in computational microscopy where phase shift is used as a way to generate image contrast. Recovering both amplitude and phase di ...

Visual anomaly detection (AD) presents significant challenges due to the scarcity of anomalous data samples. While numerous works have been proposed to synthesize anomalous samples, these synthetic anomalies often lack authenticity or require extensive tra ...

Pose-invariant face recognition has become a challenging problem for modern AI-based face recognition systems. It aims at matching a profile face captured in the wild with a frontal face registered in a database. Existing methods perform face frontalizatio ...

Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low sig ...

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 ...

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 ...

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 ...

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 ...

Complex systems with multiple processes evolving on different temporal scales are naturally described by multilayer networks, where each layer represents a different timescale. In this work, we show how the multilayer structure shapes the generation and pr ...

Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical tech ...

Origami-inspired structures facilitate the design of compliant and compact robots. However, physical origami robots possess inherent material compliance and mechanical imperfections, presenting challenges in modeling and redundant actuation for accurate co ...

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, ...

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

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

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

Nonparametric network summaries

FADE: Forecasting for anomaly detection on ECG

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

Multi-Objective Management for Many-Core Computing Servers

GraphOntology Dataset

Cross-Net: Joint In-Line Holographic Image Reconstruction and Refocusing

Anomaly Anything: Promptable Unseen Visual Anomaly Generation

Pose-Invariant Face Recognition via Feature-Space Pose Frontalization

Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression

Fundamental Social Learning Scaling Law for Tracking Hidden Markov Models

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

Intraday solar irradiance forecasting using public cameras

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

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

Information propagation in Gaussian processes on multilayer networks

Programming the scalable optical learning operator with spatial-spectral optimization

Data-Driven Kinematic Modeling of Physical Origami Robots

Towards Robust Vision Models