Unravelling the components of diffuse scattering using deep learning

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

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 ...

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 ...

With the advent of surveys like Euclid and Vera C. Rubin, astrophysicists will have access to both deep, high-resolution images and multiband images. However, these two types are not simultaneously available in any single dataset. It is therefore vital to ...

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 ...

Modeling spatial-temporal interactions among neighboring agents is at the heart of multi-agent problems such as motion forecasting and crowd navigation. Despite notable progress, it remains unclear to which extent modern representations can capture the cau ...

Snow height measurements are still the backbone of any snow cover monitoring, whether based on modeling or remote sensing. These ground-based measurements are often realized using ultrasonic or laser technologies. In challenging environments, such as high ...

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 ...

Sports helmets provide incomplete protection against brain injuries. Here we aim to improve helmet liner efficiency by employing a novel approach that optimizes their properties. By exploiting a finite element model that simulates head impacts, we develope ...

Adversarial robustness has been studied extensively in image classification, especially for the ℓ∞-threat model, but significantly less so for related tasks such as object detection and semantic segmentation, where attacks turn out to be a much harder opti ...

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 ...

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 ...

Cellular function and behavior are governed not only by molecular composition but also by a cell's native environmental context. By leveraging in-depth molecular quantification and high-resolution imaging methods to study microenvironmental composition and ...

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 ...

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 ...

Unravelling the components of diffuse scattering using deep learning

Chattez avec Graph Search

Machine learning Hubbard parameters with equivariant neural networks

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

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

Joint Multiband Deconvolution for Euclid and Vera C. Rubin Images

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

Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations

Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2)

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

Helmet material design for mitigating traumatic axonal injuries through AI-driven constitutive law enhancement

Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models

Controlled Training Data Generation with Diffusion Models

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

Computational approaches for spatialomics data: construction, integration and analysis of biomolecular atlases

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

Structured Random Model for Fast and Robust Phase Retrieval