Reinforcement learning for combinatorial optimization: A survey
Many traditional algorithms for solving combinatorial optimization problems involve using
hand-crafted heuristics that sequentially construct a solution. Such heuristics are designed …
hand-crafted heuristics that sequentially construct a solution. Such heuristics are designed …
Approximation and online algorithms for multidimensional bin packing: A survey
The bin packing problem is a well-studied problem in combinatorial optimization. In the
classical bin packing problem, we are given a list of real numbers in (0, 1] and the goal is to …
classical bin packing problem, we are given a list of real numbers in (0, 1] and the goal is to …
Difusco: Graph-based diffusion solvers for combinatorial optimization
Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …
promising results in solving various NP-complete (NPC) problems without relying on hand …
Dimes: A differentiable meta solver for combinatorial optimization problems
Recently, deep reinforcement learning (DRL) models have shown promising results in
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …
Combinatorial optimization with graph convolutional networks and guided tree search
We present a learning-based approach to computing solutions for certain NP-hard
problems. Our approach combines deep learning techniques with useful algorithmic …
problems. Our approach combines deep learning techniques with useful algorithmic …
Mixed-modality speech recognition and interaction using a wearable artificial throat
Researchers have recently been pursuing technologies for universal speech recognition
and interaction that can work well with subtle sounds or noisy environments. Multichannel …
and interaction that can work well with subtle sounds or noisy environments. Multichannel …
Deep learning ensemble 2D CNN approach towards the detection of lung cancer
In recent times, deep learning has emerged as a great resource to help research in medical
sciences. A lot of work has been done with the help of computer science to expose and …
sciences. A lot of work has been done with the help of computer science to expose and …
Synthesized classifiers for zero-shot learning
Given semantic descriptions of object classes, zero-shot learning aims to accurately
recognize objects of the unseen classes, from which no examples are available at the …
recognize objects of the unseen classes, from which no examples are available at the …
[HTML][HTML] A survey of recently developed metaheuristics and their comparative analysis
A Alorf - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
The aim of this study was to gather, discuss, and compare recently developed
metaheuristics to understand the pace of development in the field of metaheuristics and …
metaheuristics to understand the pace of development in the field of metaheuristics and …
Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery
Recent technological advances in remote sensing sensors and platforms, such as high-
resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of …
resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of …