Retrieval of Surface Solar Radiation through Implicit Albedo Recovery from Temporal Context

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Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer ...

Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and dep ...

The digitization of 3D deformable objects remains a significant challenge in computer graphics and vision, particularly in the accurate modeling of garments. Garments exhibit complex shape variability, non-rigid deformations, and frequent self-occlusion, m ...

The optimistic gradient method is useful in addressing minimax optimization problems. Motivated by the observation that the conventional stochastic version suffers from the need for a large batch size on the order of O(ϵ-2) to achieve an ϵ-stationary solut ...

Machine learning is increasingly integrated with decision making by powering automated systems and informing human decision makers in applications such as CV screening, portfolio allocation, loan risk assessment, and insurance approval. In this thesis, I e ...

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high ...

Object detection, a fundamental task in computer vision, is crucial for various intelligent edge computing applications. However, object detection algorithms are usually heavy in computation, hindering their deployments on resource-constrained edge devices ...

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

Self-supervised pretrained models exhibit competitive performance in automatic speech recognition (ASR) on finetuning, even with limited in-domain supervised data. However, popular pretrained models are not suitable for streaming ASR because they are train ...

Given a ground-level query image and a geo-referenced aerial image that covers the query’s local surroundings, fine-grained cross-view localization aims to estimate the location of the ground camera inside the aerial image. Recent works have focused on dev ...

Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mismatch with typical speech. Unfortunately, these approaches rely on transcrib ...

MammAlps is a multimodal and multi-view dataset of wildlife behavior monitoring. Nine camera-traps were placed at three monitoring sites in the Swiss National Park, from which we curated over 14 hours of video with audio, 2D segmentation maps and 8.5 hours ...

Synthesizability in generative molecular design remains a pressing challenge. Existing methods to assess synthesizability include heuristics-based metrics or retrosynthesis models which predict a synthetic pathway. By contrast, an explicit approach anchors ...

A robust anomaly detection mechanism should possess the capability to effectively remediate anomalies, restoring them to a healthy state, while preserving essential healthy information. Despite the efficacy of existing generative models in learning the und ...

As the demand for high channel counts and high-resolution recordings of neural activity continues to grow, the increased power and data rate generated impose hard constraints on the telemetry capabilities of wireless implantable neural interfaces. To addre ...

Fiber reinforced polymer confined-recycled aggregate concrete filled steel tube (FRP-RACFST) not only proposes excellent load-bearing capacity and corrosion resistance, but is also an environmentally friendly structure due to the incorporation of recycled ...

Retrieval of Surface Solar Radiation through Implicit Albedo Recovery from Temporal Context

Chattez avec Graph Search

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

On the added value of sequential deep learning for the upscaling of evapotranspiration

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Neural Garment Modeling

Diffusion Stochastic Optimization for Min-Max Problems

Machine Learning in Decision-Making Systems: Fairness, Robustness, and Data Bias

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

Enhancing skin lesion classification: a CNN approach with human baseline comparison

Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models

Adapting Fine-Grained Cross-View Localization to Areas Without Fine Ground Truth

Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR

MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps

Directly optimizing for synthesizability in generative molecular design using retrosynthesis models

Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet

A Reconfigurable High-Dynamic Range ∆Σ Front-End with Event-Based Decimation for Bandwidth-Efficient Implantable Neural Interfaces

Intelligent prediction framework for axial compressive capacity of FRP-RACFST columns