research
∙
02/21/2023
AutoML in The Wild: Obstacles, Workarounds, and Expectations
Automated machine learning (AutoML) is envisioned to make ML techniques ...
share
research
∙
12/31/2022
An end-to-end multi-scale network for action prediction in videos
In this paper, we develop an efficient multi-scale network to predict ac...
research
∙
12/04/2022
MiLMo:Minority Multilingual Pre-trained Language Model
Pre-trained language models are trained on large-scale unsupervised data...
research
∙
11/26/2022
Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data
In biomanufacturing, developing an accurate model to simulate the comple...
research
∙
11/26/2022
Enhancing Constraint Programming via Supervised Learning for Job Shop Scheduling
Constraint programming (CP) is an effective technique for solving constr...
research
∙
10/31/2022
Adaptive Population-based Simulated Annealing for Uncertain Resource Constrained Job Scheduling
Transporting ore from mines to ports is of significant interest in minin...
research
∙
10/14/2022
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
Recent neural methods for vehicle routing problems always train and test...
research
∙
08/24/2022
Prerequisite-driven Q-matrix Refinement for Learner Knowledge Assessment: A Case Study in Online Learning Context
The ever growing abundance of learning traces in the online learning pla...
research
∙
05/15/2022
TiBERT: Tibetan Pre-trained Language Model
The pre-trained language model is trained on large-scale unlabeled text ...
research
∙
05/09/2022
What Do You Get from Turning on Your Video? Effects of Videoconferencing Affordances on Remote Class Experience During COVID-19
The outbreak of COVID-19 forced schools to swiftly transition from in-pe...
research
∙
01/07/2022
Probabilistic spatial clustering based on the Self Discipline Learning (SDL) model of autonomous learning
Unsupervised clustering algorithm can effectively reduce the dimension o...
research
∙
12/08/2021
Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring
Column Generation (CG) is an effective method for solving large-scale op...
research
∙
07/02/2021
Learning Primal Heuristics for Mixed Integer Programs
This paper proposes a novel primal heuristic for Mixed Integer Programs,...
research
∙
03/30/2021
Public Transport Planning: When Transit Network Connectivity Meets Commuting Demand
In this paper, we make a first attempt to incorporate both commuting dem...
research
∙
12/18/2020
Instance Space Analysis for the Car Sequencing Problem
In this paper, we investigate an important research question in the car ...
research
∙
10/13/2020
On the Efficiency of K-Means Clustering: Evaluation, Optimization, and Algorithm Selection
This paper presents a thorough evaluation of the existing methods that a...
research
∙
07/29/2020
Boosting Ant Colony Optimization via Solution Prediction and Machine Learning
This paper introduces an enhanced meta-heuristic (ML-ACO) that combines ...
research
∙
05/12/2020
Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman Problems
Combinatorial optimization plays an important role in real-world problem...
research
∙
10/28/2019
On the Degree of Boolean Functions as Polynomials over Z_m
Polynomial representations of Boolean functions over various rings such ...
research
∙
12/24/2018
Improving MMD-GAN Training with Repulsive Loss Function
Generative adversarial nets (GANs) are widely used to learn the data sam...
research
∙
10/09/2018
On the Relationship between Energy Complexity and Other Boolean Function Measures
In this work we investigate into energy complexity, a Boolean function m...
research
∙
04/11/2016