Results 31 to 40 of about 75,461 (296)
Hierarchical quantum circuit representations for neural architecture search
npj Quantum Information, 2023 Quantum circuit algorithms often require architectural design choices analogous to those made in constructing neural and tensor networks. These tend to be hierarchical, modular and exhibit repeating patterns.
Matt Lourens +4 more
doaj +1 more source
A comparison of manual and automated neural architecture search for white matter tract segmentation. [PDF]
Sci Rep, 2023 Segmentation of white matter tracts in diffusion magnetic resonance images is an important first step in many imaging studies of the brain in health and disease.
Tchetchenian A +5 more
europepmc +2 more sources
Symbolic Neural Architecture Search for Differential Equations
IEEE Access, 2023 In this paper, we introduce the first use of symbolic integration that leverages the machine learning infrastructure, such as automatic differentiation, to find analytical approximations of ordinary and partial differential equations.
Paulius Sasnauskas, Linas Petkevicius
doaj +1 more source
Neural Architecture Search: A Survey
J. Mach. Learn. Res., 2018 Deep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation. One crucial aspect for this progress are novel neural architectures. Currently employed architectures have mostly been developed manually by human experts, which is a time-consuming and error ...
Elsken, Thomas +2 more
openaire +4 more sources
RoCo-NAS: Robust and Compact Neural Architecture Search
, 2021 Deep model compression has been studied widely, and state-of-the-art methods can now achieve high compression ratios with minimum accuracy loss. Recent advances in adversarial attacks reveal the inherent vulnerability of deep neural networks to slightly ...
Mehdi Modarressi +7 more
core +1 more source
Neural Architecture Search for Keyword Spotting [PDF]
Interspeech 2020, 2020 Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice. In this paper, we apply neural architecture search to search for convolutional neural network models that can help boost the performance of keyword spotting based on features extracted from acoustic signals ...
Tong Mo +4 more
openaire +2 more sources
Revisiting Neural Architecture Search
CoRR, 2020 Neural Architecture Search (NAS) is a collection of methods to craft the way neural networks are built. Current NAS methods are far from ab initio and automatic, as they use manual backbone architectures or micro building blocks (cells), which have had minor breakthroughs in performance compared to random baselines.
Anubhav Garg +2 more
openaire +2 more sources
Efficient Neural Architecture Search using Genetic Algorithm [PDF]
, 2022 NASNet and AmoebaNet are state-of-the-art neural architecture search systems that were able to achieve better accuracy than state-of-the-art human-made convolutional neural networks.
Morgan, Brandon
core
Channel Configuration for Neural Architecture: Insights from the Search Space
, 2023 We consider search spaces associated with neural network channel configuration. Architectures and their accuracy are visualised using low-dimensional Euclidean embedding (LDEE). Optimisation dynamics are captured using local optima networks (LONs).
Thomson, Sarah L +3 more
core +1 more source
APNAS: Accuracy-and-Performance-Aware Neural Architecture Search for Neural Hardware Accelerators
IEEE Access, 2020 Designing resource-efficient deep neural networks (DNNs) is a challenging task due to the enormous diversity of applications as well as their time-consuming design, training, optimization, and evaluation cycles, especially the resource-constrained ...
Paniti Achararit +4 more
doaj +1 more source