AIMC Topic: Neural Networks, Computer

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Meta-tuning and fast optimization of machine learning models for dynamic methane prediction in anaerobic digestion.

Bioresource technology
This study evaluates the performance of several optimization algorithms for tuning a data preparation and hyperparameter optimization pipeline applied to machine and deep learning models predicting methane production. Bayesian ridge regression and re...

Predicting growth parameters of biofertilizer inoculated pepper, using root capacitance assessments and artificial neural networks in two soils.

Biologia futura
Monitoring the root system plays an important role in understanding plant physiological processes; however, its assessment using non-destructive methods remains challenging. Here, we evaluate the utility of root capacitance (C) as a practical indicat...

Robust temporal knowledge inference via pathway snapshots with liquid neural network.

Methods (San Diego, Calif.)
Static graphs play a pivotal role in modeling and analyzing biological and biomedical data. However, many real-world scenarios-such as disease progression and drug pharmacokinetic processes-exhibit dynamic behaviors. Consequently, static graph method...

Interpretable inverse iteration mean shift networks for clustering tasks.

Neural networks : the official journal of the International Neural Network Society
Neural networks have become the standard approach for tasks such as computer vision, machine translation and pattern recognition. While they exhibit significant feature representation capabilities, they often lack interpretability. This suggests that...

Unsupervised feature selection with evolutionary sparsity.

Neural networks : the official journal of the International Neural Network Society
The ℓ-norm is playing an increasingly important role in unsupervised feature selection. However, existing algorithm for optimization problem with ℓ-norm constraint has two problems: First, they cannot automatically determine the sparsity, also known ...

Physics-informed multi-output Gaussian process for dynamical system modeling.

Neural networks : the official journal of the International Neural Network Society
Learning accurate dynamics models is crucial for model-based reinforcement learning. Gaussian processes (GPs), as a probabilistic modeling approach, have been widely used for dynamical system modeling. However, standard GPs are designed for single-ou...

FedPPD: Towards effective subgraph federated learning via pseudo prototype distillation.

Neural networks : the official journal of the International Neural Network Society
Subgraph federated learning (subgraph-FL) is a distributed machine learning paradigm enabling cross-client collaborative training of graph neural networks (GNNs). However, real-world subgraph-FL scenarios often face subgraph heterogeneity problem, i....

A pipeline for enabling Nearshore Infrared Video Super-resolution to learn more high-frequency foreground information.

Neural networks : the official journal of the International Neural Network Society
A key challenge in Nearshore Infrared Video Super-resolution (NIVSR) is the limited high-frequency foreground information. The most common approach is to fuse frames in order to learn cross-temporal information. However, existing methods struggle to ...

Enhancing realism in LiDAR scene generation with CSPA-DFN and linear cross-attention via Diffusion Transformer model.

Neural networks : the official journal of the International Neural Network Society
Point cloud diffusion models have found extensive applications in autonomous driving and robotics. However, there is still a big gap between their generated LiDAR scene samples and real-world data in terms of visual quality. This discrepancy primaril...

Insights From Inputs: Enhancing Revision Total Joint Arthroplasty Resource Allocation With Machine Learning Prediction.

The Journal of arthroplasty
BACKGROUND: Revision total knee arthroplasty (rTKA) and revision total hip arthroplasty (rTHA) are among the most resource-intensive orthopaedic procedures. The primary aim of this study was to compare the accuracy of machine learning models between ...