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Unravelling the components of diffuse scattering using deep learning

Lucas Sebastian Powell-Rudden

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 ...

Int Union Crystallography2024

Stripping the Swiss discount curve using kernel ridge regression

Nicolas Etienne Camenzind, Damir Filipovic

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 ...

Springer Heidelberg2024

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

Nikolaos Geroliminis, Semin Kwak, Danya Li

© 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 ...

Nature Research2024

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

Devis Tuia, Antoine Bosselut, Javiera Francisca Castillo Navarro, Syrielle Mathilde Marie Montariol, João Luís Prado Vieira

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 ...

Institute of Electrical and Electronics Engineers Inc.2024

Multitask methods for predicting molecular properties from heterogeneous data

Michael Herbst

Data generation remains a bottleneck in training surrogate models to predict molecular properties. We demonstrate that multitask Gaussian process regression overcomes this limitation by leveraging both expensive and cheap data sources. In particular, we co ...

2024

Morse theoretic signal compression and reconstruction on chain complexes

Kelly Spry Maggs, Celia Camille Hacker, Stefania Ebli

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 ...

2024

AI-driven detection and analysis of label-free protein aggregates

Khalid Ashraf Mohie Ibrahim

In this Tools of the Trade article, Khalid Ibrahim (Radenovic and Lashuel labs) describes a tool for the artificial intelligence (AI)-driven detection of cellular aggregates that bypasses the need for fluorescent labelling. ...

2024

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

David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi

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 ...

Institute of Electrical and Electronics Engineers Inc.2024