14 citations · 30 across the 6 of their papers we have counts for
10 papers
SuperJEPA: Pretraining with a Supervision-Augmented Joint-Embedding Predictive Architecture for High Energy Physics
Dylan Sheldon Rankin, Ho Fung Tsoi
Foundation models often obtain powerful results through very large unlabeled training datasets that dwarf available labeled datasets in size, but high energy physics already provid…
Similarity Pairing with Energy Mover's Distance for Self-Supervised Pre-Training at the LHC
Ho Fung Tsoi, Dylan Rankin
Many self-supervised methods for training foundation models at the Large Hadron Collider (LHC) rely on data augmentations to encourage the model to embed events into a representati…
Self-Supervised Learning for Robust Resonance Mass Regression in Cascade Decays
Ho Fung Tsoi, Alex Yang, Luis Felipe Gutierrez Zagazeta +2
Reconstructing the mass of a heavy resonance from its decay products with missing energy is one of the central tasks that directly determine the sensitivity in new physics searches…
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…
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…
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…
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…
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…
SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
Ho Fung Tsoi, Vladimir Loncar, Sridhara Dasu +1
Compact symbolic expressions have been shown to be more efficient than neural network models in terms of resource consumption and inference speed when implemented on custom hardwar…
Symbolic Regression on FPGAs for Fast Machine Learning Inference
Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar +7
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitiv…