7 papers
Machine Can Automatically Discover Parametric Functions to Model HEP Data
Ho Fung Tsoi, Dylan Rankin, Cecile Caillol +5
In HEP data analyses, finding an adequate function to model binned data has largely relied on a manual process: guess a functional form by intuition, fit, examine, then repeat unti…
SparsePixels: Efficient Convolution for Sparse Data on FPGAs
Ho Fung Tsoi, Dylan Rankin, Vladimir Loncar +1
Inference of standard convolutional neural networks (CNNs) on FPGAs often incurs high latency and a long initiation interval due to the deep nested loops required to densely convol…
jBOT: Semantic Jet Representation Clustering Emerges from Self-Distillation
Ho Fung Tsoi, Dylan Rankin
Self-supervised learning, in the context of foundation model training, is a powerful pre-training method for learning feature representations without labels, which often capture ge…
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…
hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware
Jan-Frederik Schulte, Benjamin Ramhorst, Chang Sun +50
We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can b…
SymbolFit: Automatic Parametric Modeling with Symbolic Regression
Ho Fung Tsoi, Dylan Rankin, Cecile Caillol +6
We introduce SymbolFit, a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data while simultaneously…