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Found 9 papers, 3 papers with code
Knowledge Distillation for Anomaly Detection
Unsupervised deep learning techniques are widely used to identify anomalous behaviour.
Symbolic Regression on FPGAs for Fast Machine Learning Inference
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints.
Towards Optimal Compression: Joint Pruning and Quantization
Model compression is instrumental in optimizing deep neural network inference on resource-constrained hardware.
FIT: A Metric for Model Sensitivity
This response is non-linear and heterogeneous throughout the network.
Lightweight Jet Reconstruction and Identification as an Object Detection Task
We experiment with 8-bit and ternary quantization, benchmarking their accuracy and inference latency against a single-precision floating-point.
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains.
Anomaly Detection With Conditional Variational Autoencoders
Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question.
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore reduction in model size, latency and energy consumption.
Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider
Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment.
