Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks

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Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference b ...

A search is presented for the pair production of new heavy resonances, each decaying into a top quark (t) or antiquark and a gluon (g). The analysis uses data recorded with the CMS detector from proton-proton collisions at a center-of-mass energy of 13 TeV ...

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Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

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Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

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Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks

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Residual-based attention in physics-informed neural networks

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

Search for Pair Production of Heavy Particles Decaying to a Top Quark and a Gluon in the Lepton Plus Jets Final State in Proton-proton Collisions at √s=13 Tev

Gibbs sampling the posterior of neural networks

Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Reliable data-driven decision-making through optimal transport

CHAMELEON: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning

Scalable Multi-agent Reinforcement Learning for Residential Load Scheduling Under Data Governance

Driving and suppressing the human language network using large language models

A phase transition in diffusion models reveals the hierarchical nature of data

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

Incorporating Projective Geometry into Deep Learning

A multidomain approach to neighbourhood typology for urban environmental studies

Optimizing Dynamic Aperture Studies with Active Learning

On the number of regions of piecewise linear neural networks

Generalization of Noisy SGD in Unbounded Non-convex Settings