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

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Despite the plethora of AI-based algorithms developed for anomaly detection in radiology, subsequent integration into clinical setting is rarely evaluated. In this work, we assess the applicability and utility of an AI-based model for brain aneurysm detect ...

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 ...

The CRC-aided successive cancellation list (CA-SCL) decoding algorithm for polar codes has gained widespread adoption thanks to its outstanding performance. However, with the evolution of 6G technologies, the high complexity of CA-SCL decoding poses a chal ...

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 ...

Autoregressive (AR) Large Language Models (LLMs) have demonstrated significant success across numerous tasks. However, the AR modeling paradigm presents certain limitations; for instance, contemporary autoregressive LLMs are trained to generate one token a ...

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 ...

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 ...

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 ...

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 ...

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

Chat with Graph Search

FADE: Forecasting for anomaly detection on ECG

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

GraphOntology Dataset

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

Multi-Objective Management for Many-Core Computing Servers

Anomaly Anything: Promptable Unseen Visual Anomaly Generation

Pose-Invariant Face Recognition via Feature-Space Pose Frontalization

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

Nonparametric network summaries

Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: A multi-reader study

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

GenPolar: Generative AI-Aided Complexity Reduction for Polar SCL Decoding

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

Beyond Autoregression: Fast LLMs via Self-Distillation Through Time

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

SuperAnimal pretrained pose estimation models for behavioral analysis

Discovering meaningful units from text sequences

Intraday solar irradiance forecasting using public cameras

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