Research Papers

The cutting edge of artificial intelligence research. Sharded from the global knowledge mesh.

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2010.06746

by Community

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2010.09280

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2010.11328

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2010.13309

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2010.14894

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2011.01926

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2011.05081

by Community

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

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2011.06551

by Community

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

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2011.09128

by Community

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

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2011.10568

by Azhar Shaikh

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

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Estimator Model for Prediction of Power Output of Wave Farms Using Machine Learning Methods

by Bhavana Burramukku

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

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NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration

by Zhengang Li

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

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Quantum Dynamics of Optimization Problems

by Peng Wang

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

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Binding and Perspective Taking as Inference in a Generative Neural Network Model

by Mahdi Sadeghi

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

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Lagrangian Reachtubes: The Next Generation

by Sophie Gruenbacher

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

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Weakly-Supervised Action Localization and Action Recognition using Global-Local Attention of 3D CNN

by Novanto Yudistira

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

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Evolutionary Variational Optimization of Generative Models

by Jakob Drefs

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

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Computing Cliques and Cavities in Networks

by Dinghua Shi

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

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A threshold search based memetic algorithm for the disjunctively constrained knapsack problem

by Zequn Wei

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

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ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search Spaces

by Xingyou Song

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

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Neuromorphic adaptive spiking CPG towards bio-inspired locomotion of legged robots

by Pablo Lopez-Osorio

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

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Pseudo-Adaptive Penalization to Handle Constraints in Particle Swarm Optimizers

by Mauro S. Innocente

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

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Deep Evolutionary Learning for Molecular Design

by Yifeng Li

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

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Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions

by Eneko Osaba

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

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Learning How to Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection

by Hussein Almulla

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

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Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks

by Amine Aziz-Alaoui

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

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On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification

by Alejandro Barredo Arrieta

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

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Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification

by Naoya Muramatsu

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

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Neuroevolution of a Recurrent Neural Network for Spatial and Working Memory in a Simulated Robotic Environment

by Xinyun Zou

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

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E$^2$CM: Early Exit via Class Means for Efficient Supervised and Unsupervised Learning

by Alperen Görmez

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

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Pilot Investigation for a Comprehensive Taxonomy of Autonomous Entities

by William Wagner

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

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Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware

by Twisha Titirsha

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

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Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks

by Oleg Nikitin

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

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Low Dimensional Landscape Hypothesis is True: DNNs can be Trained in Tiny Subspaces

by Tao Li

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

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Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Driving

by Briti Gangopadhyay

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

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Shape-constrained Symbolic Regression -- Improving Extrapolation with Prior Knowledge

by Gabriel Kronberger

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

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The Tangent Search Algorithm for Solving Optimization Problems

by Abdesslem Layeb

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

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The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks

by Salva Rühling Cachay

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

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A Novel Generalised Meta-Heuristic Framework for Dynamic Capacitated Arc Routing Problems

by Hao Tong

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

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A Rank based Adaptive Mutation in Genetic Algorithm

by Avijit Basak

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

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Continuous Learning and Adaptation with Membrane Potential and Activation Threshold Homeostasis

by Alexander Hadjiivanov

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

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SpikE: spike-based embeddings for multi-relational graph data

by Dominik Dold

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

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Universal scaling laws in the gradient descent training of neural networks

by Maksim Velikanov

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

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Covariance Matrix Adaptation Evolution Strategy Assisted by Principal Component Analysis

by Yangjie Mei

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

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HeunNet: Extending ResNet using Heun's Methods

by Mehrdad Maleki

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

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PLSM: A Parallelized Liquid State Machine for Unintentional Action Detection

by Dipayan Das

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

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Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks

by Jianhao Ding

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

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TENSILE: A Tensor granularity dynamic GPU memory scheduling method toward multiple dynamic workloads system

by Kaixin Zhang

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