Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models

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Accurate human trajectory prediction is one of the most crucial tasks for autonomous driving, ensuring its safety. Yet, existing models often fail to fully leverage the visual cues that humans subconsciously communicate when navigating the space. In this w ...

Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in public rep ...

Elucidating the functional effects of missense variants is crucial yet challenging. To investigate their impact, we fine-tuned protein language models, including ESM2 and ProtT5, to classify 20 protein features at amino acid resolution. In addition, we tra ...

Phase retrieval, a nonlinear problem prevalent in imaging applications, has been extensively studied using random models, some of which with i.i.d. sensing matrix components. While these models offer robust reconstruction guarantees, they are computational ...

Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challengi ...

Recent advancements in natural language processing, driven by the success of large language models, have led to the scaling up of models to unprecedented sizes. While these models have achieved remarkable performance across various tasks, their computation ...

Cell tracking is a ubiquitous image analysis task in live-cell microscopy. Unlike multiple object tracking (MOT) for natural images, cell tracking typically involves hundreds of similar-looking objects that can divide in each frame, making it a particularl ...

We present a method to control a text-to-image generative model to produce training data useful for supervised learning. Unlike previous works that employ an open-loop approach via pre-defined prompts to generate new data using either a language model or h ...

The Songdo Traffic dataset delivers precisely georeferenced vehicle trajectories captured through high-altitude bird's-eye view (BeV) drone footage over Songdo International Business District, South Korea. Comprising approximately 700,000 unique trajectori ...

Density-functional theory with extended Hubbard functionals (DFT + U + V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to ...

While writing peer reviews resembles an important task in science, education, and large organizations, providing fruitful suggestions to peers is not a straightforward task, as different user interaction designs of text suggestion interfaces can have diver ...

Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘pre ...

© The Author(s) 2024.Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, h ...

Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes ...

Many technologically important material properties are underpinned by disorder and short-range structural correlations; therefore, elucidating structure-property relationships in functional materials requires understanding both the average and the local st ...

We consider a family of boundary integral operators supported on a collection of parametrically defined bounded Lipschitz boundaries. Consequently, the boundary integral operators themselves also depend on the parametric variables, thus leading to a parame ...

Recently, generalist Vision Language Models (VLMs) have shown exceptional progress in tasks previously dominated by specialized computer vision models. This becomes more prevalent when visual grounding capabilities, such as the ability to reason over input ...

We analyze and implement the kernel ridge regression (KR) method developed in Filipovic et al. (Stripping the discount curve-a robust machine learning approach. Swiss Finance Institute Research Paper No. 22-24. SSRN. https://ssrn.com/abstract=4058150, 2022 ...

Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system ...

High-frequency (HF) signals are ubiquitous in the industrial world and are of great use for monitoring of industrial assets. Most deep-learning tools are designed for inputs of fixed and/or very limited size and many successful applications of deep learnin ...

Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models

Chattez avec Graph Search

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem

Fine-tuning protein language models to understand the functional impact of missense variants

Structured Random Model for Fast and Robust Phase Retrieval

Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches

Parameter-efficient Methods for Training and Inference of Transformer-based Models

TRACKASTRA: Transformer-Based Cell Tracking for Live-Cell Microscopy

Controlled Training Data Generation with Diffusion Models

Songdo Traffic: High Accuracy Georeferenced Vehicle Trajectories from a Large-Scale Study in a Smart City

Machine learning Hubbard parameters with equivariant neural networks

Enhancing Peer Review with AI-Powered Suggestion Generation Assistance: Investigating the Design Dynamics

A prediction rigidity formalism for low-cost uncertainties in trained neural networks

A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting

Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process

Unravelling the components of diffuse scattering using deep learning

PARAMETRIC SHAPE HOLOMORPHY OF BOUNDARY INTEGRAL OPERATORS WITH APPLICATIONS

Training Visual Language Models with Object Detection: Grounded Change Descriptions in Satellite Images

Stripping the Swiss discount curve using kernel ridge regression

Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data

Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series