43 citations · 49 across the 9 of their papers we have counts for
10 papers
KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting
Jonas Petersen, Pratik Jawahar, Riccardo Maggioni +6
Forecasting rare, high-cost events in multivariate time-series systems is hard because their precursors are seldom observed, so a purely data-driven model has little to learn from.…
CHLU: The Causal Hamiltonian Learning Unit as a Symplectic Primitive for Deep Learning
Pratik Jawahar, Maurizio Pierini
Current deep learning primitives dealing with temporal dynamics suffer from a fundamental dichotomy: they are either discrete and unstable (LSTMs) \citep{pascanu_difficulty_2013},…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…
SMARTHEP: training PhD students in real-time analysis at the LHC and in industry
Johannes Albrecht, Laura Boggia, Leon Bozianu +18
In this invited Editorial for Software and Computing for Big Science, we describe the SMARTHEP Innovative Training Network funded via the Marie Skłodowska-Curie Actions between 202…
Knowledge is Overrated: A zero-knowledge machine learning and cryptographic hashing-based framework for verifiable, low latency inference at the LHC
Pratik Jawahar, Caterina Doglioni, Maurizio Pierini
Low latency event-selection (trigger) algorithms are essential components of Large Hadron Collider (LHC) operation. Modern machine learning (ML) models have shown great offline per…
Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
Laura Boggia, Carlos Cocha, Fotis Giasemis +11
The field of high energy physics (HEP) has seen a marked increase in the use of machine learning (ML) techniques in recent years. The proliferation of applications has revolutionis…
The SMARTHEP European Training Network
James Andrew Gooding, Leon Bozianu, Carlos Cocha Toapaxi +2
Synergies between MAchine learning, Real-Time analysis and Hybrid architectures for efficient Event Processing and decision-making (SMARTHEP) is a European Training Network, traini…
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…
Baler -- Machine Learning Based Compression of Scientific Data
Fritjof Bengtsson, Caterina Doglioni, Per Alexander Ekman +6
Storing and sharing increasingly large datasets is a challenge across scientific research and industry. In this paper, we document the development and applications of Baler - a Mac…
Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
Pratik Jawahar, Thea Aarrestad, Nadezda Chernyavskaya +5
We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a wo…