Related papers: Machine Learning as a Service for HEP
The air-ground integrated network is a key component of future sixth generation (6G) networks to support seamless and near-instant super-connectivity. There is a pressing need to intelligently provision various services in 6G networks,…
Mobile Edge Computing (MEC) and Open Radio Access Networks (ORAN) are transformative technologies in the development of next-generation wireless communication systems. MEC pushes computational resources closer to end-users, enabling low…
Nowadays, we are witnessing a wide adoption of Machine learning (ML) models in many safety-critical systems, thanks to recent breakthroughs in deep learning and reinforcement learning. Many people are now interacting with systems based on…
With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows,…
Machine Learning (ML) inspired algorithms provide a flexible set of tools for analyzing and forecasting chaotic dynamical systems. We here analyze the performance of one algorithm for the prediction of extreme events in the two-dimensional…
Machine learning (ML) empowers biomedical systems with the capability to optimize their performance through modeling of the available data extremely well, without using strong assumptions about the modeled system. Especially in nano-scale…
End-users can get functions-as-a-service from serverless platforms, which promise lower hosting costs, high availability, fault tolerance, and dynamic flexibility for hosting individual functions known as microservices. Machine learning…
Machine learning has recently gained traction as a way to overcome the slow accelerator generation and implementation process on an FPGA. It can be used to build performance and resource usage models that enable fast early-stage design…
The ATLAS Event Streaming Service (ESS) at the LHC is an approach to preprocess and deliver data for Event Service (ES) that has implemented a fine-grained approach for ATLAS event processing. The ESS allows one to asynchronously deliver…
The Scalable Systems Laboratory (SSL), part of the IRIS-HEP Software Institute, provides Institute participants and HEP software developers generally with a means to transition their R&D from conceptual toys to testbeds to production-scale…
Machine learning models are often provisioned as a cloud-based service where the clients send their data to the service provider to obtain the result. This setting is commonplace due to the high value of the models, but it requires the…
Machine learning (ML) plays an increasingly important role in both online and offline event reconstruction and identification at CMS experiment. A variety of ML techniques are used to improve the identification of physics objects. Dedicated…
The implementation of artificial intelligence (AI) in business applications holds considerable promise for significant improvements. The development of AI systems is becoming increasingly complex, thereby underscoring the growing importance…
Running Large Language Models (LLMs) on edge devices is constrained by high compute and memory demands posing a barrier for real-time applications in sectors like healthcare, education, and embedded systems. Current solutions such as…
Human Activity Recognition~(HAR) is the classification of human movement, captured using one or more sensors either as wearables or embedded in the environment~(e.g. depth cameras, pressure mats). State-of-the-art methods of HAR rely on…
Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a…
Extracting actionable information rapidly from data produced by instruments such as the Linac Coherent Light Source (LCLS-II) and Advanced Photon Source Upgrade (APS-U) is becoming ever more challenging due to high (up to TB/s) data rates.…
In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned from previous tasks. However, a critical challenge in meta-learning is task uncertainty and heterogeneity, which can not be handled via globally…
Machine-learning models in high-energy physics are often trained on simulated data, where fully simulated samples are computationally expensive while fast simulation provides large statistics at reduced realism. In this work, we…
Multimodal Large Language Models (MLLMs) struggle with continual learning, often suffering from catastrophic forgetting when adapting to sequential tasks. We introduce a routing-based architecture that integrates new capabilities while…