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[HTML][HTML] Hyperparameter optimization: Classics, acceleration, online, multi-objective, and tools
JM Tan, H Liao, W Liu, C Fan, J Huang… - Mathematical …, 2024 - aimspress.com
Hyperparameter optimization (HPO) has been well-developed and evolved into a well-
established research topic over the decades. With the success and wide application of deep …
established research topic over the decades. With the success and wide application of deep …
Recycle: Fast and efficient long time series forecasting with residual cyclic transformers
Transformers have recently gained prominence in long time series forecasting by elevating
accuracies in a variety of use cases. Regrettably, in the race for better predictive …
accuracies in a variety of use cases. Regrettably, in the race for better predictive …
Short paper: Accelerating hyperparameter optimization algorithms with mixed precision
Hyperparameter Optimization (HPO) of Neural Networks (NNs) is a computationally
expensive procedure. On accelerators, such as NVIDIA Graphics Processing Units (GPUs) …
expensive procedure. On accelerators, such as NVIDIA Graphics Processing Units (GPUs) …
Harnessing Orthogonality to Train Low-Rank Neural Networks
This study explores the learning dynamics of neural networks by analyzing the singular
value decomposition (SVD) of their weights throughout training. Our investigation reveals …
value decomposition (SVD) of their weights throughout training. Our investigation reveals …
AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability
Optimizing smart grid operations relies on critical decision-making informed by uncertainty
quantification, making probabilistic forecasting a vital tool. Designing such forecasting …
quantification, making probabilistic forecasting a vital tool. Designing such forecasting …
Beyond Backpropagation: Optimization with Multi-Tangent Forward Gradients
The gradients used to train neural networks are typically computed using backpropagation.
While an efficient way to obtain exact gradients, backpropagation is computationally …
While an efficient way to obtain exact gradients, backpropagation is computationally …
Resource-Adaptive Successive Doubling for Hyperparameter Optimization with Large Datasets on High-Performance Computing Systems
On High-Performance Computing (HPC) systems, several hyperparameter configurations
can be evaluated in parallel to speed up the Hyperparameter Optimization (HPO) process …
can be evaluated in parallel to speed up the Hyperparameter Optimization (HPO) process …
PETNet–Coincident Particle Event Detection using Spiking Neural Networks
Spiking neural networks (SNN) hold the promise of being a more biologically plausible, low-
energy alternative to conventional artificial neural networks. Their time-variant nature makes …
energy alternative to conventional artificial neural networks. Their time-variant nature makes …
Parallel and Scalable Hyperparameter Optimization for Distributed Deep Learning Methods on High-Performance Computing Systems
M Aach - 2025 - opinvisindi.is
The design of Deep Learning (DL) models is a complex task, involving decisions on the
general architecture of the model (eg, the number of layers of the Neural Network (NN)) and …
general architecture of the model (eg, the number of layers of the Neural Network (NN)) and …
Design of Cluster-Computing Architecture to Improve Training Speed of the Neuroevolution Algorithm
In this paper, we review the key features and major drawbacks of the NeuroEvolution of
Augmenting Topologies (NEAT) algorithm, such as slow training speed that limits its area of …
Augmenting Topologies (NEAT) algorithm, such as slow training speed that limits its area of …