Distributed learning in wireless networks: Recent progress and future challenges
The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …
applications to efficiently analyze various types of data collected by edge devices for …
Photonics for artificial intelligence and neuromorphic computing
Research in photonic computing has flourished due to the proliferation of optoelectronic
components on photonic integration platforms. Photonic integrated circuits have enabled …
components on photonic integration platforms. Photonic integrated circuits have enabled …
Machine learning in the search for new fundamental physics
Compelling experimental evidence suggests the existence of new physics beyond the well-
established and tested standard model of particle physics. Various current and upcoming …
established and tested standard model of particle physics. Various current and upcoming …
Machine learning and the physical sciences
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …
for a vast array of data processing tasks, which has entered most scientific disciplines in …
The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics
G Kasieczka, B Nachman, D Shih… - Reports on progress …, 2021 - iopscience.iop.org
A new paradigm for data-driven, model-agnostic new physics searches at colliders is
emerging, and aims to leverage recent breakthroughs in anomaly detection and machine …
emerging, and aims to leverage recent breakthroughs in anomaly detection and machine …
Prospects and applications of photonic neural networks
C Huang, VJ Sorger, M Miscuglio… - … in Physics: X, 2022 - Taylor & Francis
Neural networks have enabled applications in artificial intelligence through machine
learning, and neuromorphic computing. Software implementations of neural networks on …
learning, and neuromorphic computing. Software implementations of neural networks on …
Machine learning in nuclear physics at low and intermediate energies
Abstract Machine learning (ML) is becoming a new paradigm for scientific research in
various research fields due to its exciting and powerful capability of modeling tools used for …
various research fields due to its exciting and powerful capability of modeling tools used for …
hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices
F Fahim, B Hawks, C Herwig, J Hirschauer… - arXiv preprint arXiv …, 2021 - arxiv.org
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient
devices and systems are extremely valuable across a broad range of application domains …
devices and systems are extremely valuable across a broad range of application domains …
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 …
larger and more complex algorithms, edge devices demand efficient inference and therefore …
Machine and deep learning for resource allocation in multi-access edge computing: A survey
With the rapid development of Internet-of-Things (IoT) devices and mobile communication
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …