AIMC Topic: Algorithms

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Neuromorphic computing at scale.

Nature
Neuromorphic computing is a brain-inspired approach to hardware and algorithm design that efficiently realizes artificial neural networks. Neuromorphic designers apply the principles of biointelligence discovered by neuroscientists to design efficien...

Dual inhibition of AChE and MAO-B in Alzheimer's disease: machine learning approaches and model interpretations.

Molecular diversity
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases. Given the multifactorial pathophysiology of AD, monotargeted agents can only alleviate symptoms but not cure AD. Acetylcholinesterase (AChE) and Monoamine oxidase B (MA...

PEDRA-EFB0: colorectal cancer prognostication using deep learning with patch embeddings and dual residual attention.

Medical & biological engineering & computing
In computer-aided diagnosis systems, precise feature extraction from CT scans of colorectal cancer using deep learning is essential for effective prognosis. However, existing convolutional neural networks struggle to capture long-range dependencies a...

Continual learning with Bayesian compression for shared and private latent representations.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a new continual learning method with Bayesian Compression for Shared and Private Latent Representations (BCSPLR), which learns a compact model structure while preserving the accuracy. In Shared and Private Latent Representations (...

Fast Co-clustering via Anchor-guided Label Spreading.

Neural networks : the official journal of the International Neural Network Society
The attention towards clustering using anchor graph has grown due to its effectiveness and efficiency. As the most representative points in original data, anchors are also regarded as connecting the sample space to the label space. However, when ther...

A novel ANN-based feature subset selection in multi-scale granular ball neighborhood decision tables.

Neural networks : the official journal of the International Neural Network Society
As an effective data preprocessing method, feature subset selection has been widely explored in recent years. However, the feature subset selection for the Wu-Leung model and its extended model involves high time complexity. Therefore, we combine the...

Paying more attention on backgrounds: Background-centric attention for UAV detection.

Neural networks : the official journal of the International Neural Network Society
Under the advancement of artificial intelligence, Unmanned Aerial Vehicles (UAVs) exhibit efficient flexibility in military reconnaissance, traffic monitoring, and crop analysis. However, the UAV detection faces unique challenges due to the UAV's sma...

Tensor neural networks for high-dimensional Fokker-Planck equations.

Neural networks : the official journal of the International Neural Network Society
We solve high-dimensional steady-state Fokker-Planck equations on the whole space by applying tensor neural networks. The tensor networks are a linear combination of tensor products of one-dimensional feedforward networks or a linear combination of s...

On spectral bias reduction of multi-scale neural networks for regression problems.

Neural networks : the official journal of the International Neural Network Society
In this paper, we derive diffusion equation models in the spectral domain to study the evolution of the training error of two-layer multiscale deep neural networks (MscaleDNN) (Cai and Xu, 2019; Liu et al., 2020), which is designed to reduce the spec...

Multi-view learning with enhanced multi-weight vector projection support vector machine.

Neural networks : the official journal of the International Neural Network Society
Multi-view learning aims on learning from the data represented by multiple distinct feature sets. Various multi-view support vector machine methods have been successfully applied to classification tasks. However, the existed methods often face the pr...