196 citations · 305 across the 13 of their papers we have counts for
15 papers
Real-time Anomaly Detection for Liquid Argon Time Projection Chambers
Seokju Chung, Jack Cleeve, Akshay Malige +4
We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Bas…
Measuring the Groundedness of Legal Question-Answering Systems
Dietrich Trautmann, Natalia Ostapuk, Quentin Grail +4
In high-stakes domains like legal question-answering, the accuracy and trustworthiness of generative AI systems are of paramount importance. This work presents a comprehensive benc…
Knowledge Distillation for Anomaly Detection
Adrian Alan Pol, Ekaterina Govorkova, Sonja Gronroos +5
Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model siz…
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…
Towards Optimal Compression: Joint Pruning and Quantization
Ben Zandonati, Glenn Bucagu, Adrian Alan Pol +3
Model compression is instrumental in optimizing deep neural network inference on resource-constrained hardware. The prevailing methods for network compression, namely quantization…
FIT: A Metric for Model Sensitivity
Ben Zandonati, Adrian Alan Pol, Maurizio Pierini +2
Model compression is vital to the deployment of deep learning on edge devices. Low precision representations, achieved via quantization of weights and activations, can reduce infer…