Research Papers

Structural intelligence indexing in progress...

Structural intelligence indexing in progress...

Structural intelligence indexing in progress...

Structural intelligence indexing in progress...

Structural intelligence indexing in progress...

Structural intelligence indexing in progress...

Many real-world optimization problems have multiple interacting components. Each of these can be NP-hard and they can be

Boolean satisfiability is a propositional logic problem of interest in multiple fields, e.g., physics, mathematics, and

We present a multigrid-in-channels (MGIC) approach that tackles the quadratic growth of the number of parameters with re

Task-incremental learning involves the challenging problem of learning new tasks continually, without forgetting past kn

The amount of power generated by a wave farm depends on the Wave Energy Converter (WEC) arrangement along with the usual

With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce u

In this letter, by establishing the Schrödinger equation of the optimization problem, the optimization problem is transf

The ability to flexibly bind features into coherent wholes from different perspectives is a hallmark of cognition and in

We introduce LRT-NG, a set of techniques and an associated toolset that computes a reachtube (an over-approximation of t

3D Convolutional Neural Network (3D CNN) captures spatial and temporal information on 3D data such as video sequences. H

We combine two popular optimization approaches to derive learning algorithms for generative models: variational optimiza

Complex networks contain complete subgraphs such as nodes, edges, triangles, etc., referred to as simplices and cliques

The disjunctively constrained knapsack problem consists in packing a subset of pairwisely compatible items in a capacity

In this paper, we approach the problem of optimizing blackbox functions over large hybrid search spaces consisting of bo

In recent years, locomotion mechanisms exhibited by vertebrate animals have been the inspiration for the improvement in

The penalization method is a popular technique to provide particle swarm optimizers with the ability to handle constrain

In this paper, we propose a deep evolutionary learning (DEL) process that integrates fragment-based deep generative mode

In this work we consider multitasking in the context of solving multiple optimization problems simultaneously by conduct

Search-based test generation is guided by feedback from one or more fitness functions - scoring functions that judge sol

We present a first proof-of-concept use-case that demonstrates the efficiency of interfacing the algorithm framework Par

Since their inception, learning techniques under the Reservoir Computing paradigm have shown a great modeling capability

With the continued innovations of deep neural networks, spiking neural networks (SNNs) that more closely resemble biolog

Animals ranging from rats to humans can demonstrate cognitive map capabilities. We evolved weights in a biologically pla

State-of-the-art neural networks with early exit mechanisms often need considerable amount of training and fine tuning t

This paper documents an exploratory pilot study to define the term Autonomous Entity, and any characteristics that are r

Neuromorphic computing systems are embracing memristors to implement high density and low power synaptic storage as cros

Biological neurons have adaptive nature and perform complex computations involving the filtering of redundant informatio

Deep neural networks (DNNs) usually contain massive parameters, but there is redundancy such that it is guessed that the

Recent advances in Reinforcement Learning (RL) combined with Deep Learning (DL) have demonstrated impressive performance

We investigate the addition of constraints on the function image and its derivatives for the incorporation of prior know

This article proposes a new population-based optimization algorithm called the Tangent Search Algorithm (TSA) to solve o

Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicti

The capacitated arc routing problem (CARP) is a challenging combinatorial optimisation problem abstracted from many real

Traditionally Genetic Algorithm has been used for optimization of unimodal and multimodal functions. Earlier researchers

Most classical (non-spiking) neural network models disregard internal neuron dynamics and treat neurons as simple input

Despite the recent success of reconciling spike-based coding with the error backpropagation algorithm, spiking neural ne

Current theoretical results on optimization trajectories of neural networks trained by gradient descent typically have t

Over the past decades, more and more methods gain a giant development due to the development of technology. Evolutionary

There is an analogy between the ResNet (Residual Network) architecture for deep neural networks and an Euler solver for

Reservoir Computing (RC) offers a viable option to deploy AI algorithms on low-end embedded system platforms. Liquid Sta

Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have attracted great attentions from r

Recently, deep learning has been an area of intense research. However, as a kind of computing-intensive task, deep learn