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

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

© 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 ...

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 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 ...

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 ...

Despite being genetically identical and reared under identical conditions, individual animals exhibit behavioural differences, which we call individuality. It remains unclear where these differences emerge in the causal cascade from transcription to gene p ...

The rapid urbanization taking place in Africa is resulting in the emergence of large urban agglomerations. Despite the potential and typical economic benefits associated with the emergence of such agglomerations elsewhere, not much can be said of Africa's ...

Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the poten ...

This multi-partner-project contribution introduces the midway results of the Horizon 2020 FVLLMONTI project. In this project we develop a new and ultra-efficient class of ANN accelerators, the neural network compute cube (N2C2), which is specifically desig ...

At the intersection of Topological Data Analysis (TDA) and machine learning, the field of cellular signal processing has advanced rapidly in recent years. In this context, each signal on the cells of a complex is processed using the combinatorial Laplacian ...

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

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

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

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

Controlled Training Data Generation with Diffusion Models

Joint Multiband Deconvolution for Euclid and Vera C. Rubin Images

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

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

Unravelling the components of diffuse scattering using deep learning

Stripping the Swiss discount curve using kernel ridge regression

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

Unraveling behavioral individuality in Drosophila melanogaster: An automated high-throughput screening approach

Effects of urbanization and international trade on economic growth, productivity, and employment: Case of selected countries in Africa

Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity

FVLLMONTI: The 3D Neural Network Compute Cube (N2C2) Concept for Efficient Transformer Architectures Towards Speech-to-Speech Translation

Morse theoretic signal compression and reconstruction on chain complexes