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Found 35 papers, 12 papers with code
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
Personalized FL (PFL) is an approach that aims to reduce the impact of statistical heterogeneity by developing personalized models for individual users, while also inherently providing benefits in terms of fairness and robustness.
Affective Behavior Analysis using Task-adaptive and AU-assisted Graph Network
In this paper, we present our solution and experiment result for the Multi-Task Learning Challenge of the 7th Affective Behavior Analysis in-the-wild(ABAW7) Competition.
ISPO: An Integrated Ontology of Symptom Phenotypes for Semantic Integration of Traditional Chinese Medical Data
Methods: To construct an integrated ontology of symptom phenotypes (ISPO), we manually annotated classical TCM textbooks and large-scale Chinese electronic medical records (EMRs) to collect symptom terms with support from a medical text annotation system.
Efficient k-means with Individual Fairness via Exponential Tilting
Our experiments demonstrate that TKM outperforms state-of-the-art methods in effectiveness, fairness, and efficiency.
Selecting the Number of Communities for Weighted Degree-Corrected Stochastic Block Models
We investigate how to select the number of communities for weighted networks without a full likelihood modeling.
Adaptive Stabilization Based on Machine Learning for Column Generation
This process continues until the dual values converge to the optimal dual solution to the original problem.
Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous Changes
Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems.
Enhanced Multi-Target Tracking in Dynamic Environments: Distributed Control Methods Within the Random Finite Set Framework
Tracking multiple targets in dynamic environments using distributed sensor networks is a challenging problem that has received significant attention in recent years.
CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process
Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by transferring the user preference derived from the source domain.
Improving the Accuracy of Transaction-Based Ponzi Detection on Ethereum
The Ponzi scheme, an old-fashioned fraud, is now popular on the Ethereum blockchain, causing considerable financial losses to many crypto investors.
Action Pick-up in Dynamic Action Space Reinforcement Learning
Then, we design two different AP methods: frequency-based global method and state clustering-based local method, based on the prior optimal policy.
Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization
Designing an intelligent volume-weighted average price (VWAP) strategy is a critical concern for brokers, since traditional rule-based strategies are relatively static that cannot achieve a lower transaction cost in a dynamic market.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Knowledge Graph Quality Evaluation under Incomplete Information
Winner of the game is thus considered to have better qualities.
Enhancing Constraint Programming via Supervised Learning for Job Shop Scheduling
Finally, we demonstrate that hybridising the machine learning-based variable ordering methods with traditional domain-based methods is beneficial.
Adaptive Population-based Simulated Annealing for Uncertain Resource Constrained Job Scheduling
While a number of optimisation methods have been proposed to tackle the deterministic problem, the uncertainty associated with resource availability, an inevitable challenge in mining operations, has received less attention.
Network medicine framework reveals generic herb-symptom effectiveness of Traditional Chinese Medicine
We validate our framework with real-world hospital patient data by showing that (1) shorter network distance between symptoms of inpatients correlates with higher relative risk (co-occurrence), and (2) herb-symptom network proximity is indicative of patients' symptom recovery rate after herbal treatment.
Efficient Joint DOA and TOA Estimation for Indoor Positioning with 5G Picocell Base Stations
Secondly, based on the deployment reality that 5G picocell gNBs only have a small-scale antenna array but have a large signal bandwidth, the proposed scheme decouples the estimation of time-of-arrival (TOA) and direction-of-arrival (DOA) to reduce the huge complexity induced by two-dimensional joint processing.
Low-latency Monaural Speech Enhancement with Deep Filter-bank Equalizer
To improve the performance of traditional low-latency speech enhancement algorithms, a deep filter-bank equalizer (FBE) framework was proposed, which integrated a deep learning-based subband noise reduction network with a deep learning-based shortened digital filter mapping network.
A deep complex multi-frame filtering network for stereophonic acoustic echo cancellation
In hands-free communication system, the coupling between loudspeaker and microphone generates echo signal, which can severely influence the quality of communication.
Pseudo-labelling and Meta Reweighting Learning for Image Aesthetic Quality Assessment
In the tasks of image aesthetic quality evaluation, it is difficult to reach both the high score area and low score area due to the normal distribution of aesthetic datasets.
Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems
Topology optimisation of trusses can be formulated as a combinatorial and multi-modal problem in which locating distinct optimal designs allows practitioners to choose the best design based on their preferences.
Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring
In each iteration of CG, our MLPH leverages an ML model to predict the optimal solution of the pricing problem, which is then used to guide a sampling method to efficiently generate multiple high-quality columns.
Unbiased IoU for Spherical Image Object Detection
In this paper, we first identify that spherical rectangles are unbiased bounding boxes for objects in spherical images, and then propose an analytical method for IoU calculation without any approximations.
Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies
Classification sub-module supplies classifying of images according to the eras, nationalities and garment types; Parsing sub-network supplies the semantic for person contours, clothing and background in the image to achieve more accurate colorization of clothes and persons and prevent color overflow.
Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark
This document describes the generalized moving peaks benchmark (GMPB) and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems.
Learning Linear Polytree Structural Equation Models
We are interested in the problem of learning the directed acyclic graph (DAG) when data are generated from a linear structural equation model (SEM) and the causal structure can be characterized by a polytree.
Learning Primal Heuristics for Mixed Integer Programs
This paper proposes a novel primal heuristic for Mixed Integer Programs, by employing machine learning techniques.
Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)
This document introduces the Generalized Moving Peaks Benchmark (GMPB), a tool for generating continuous dynamic optimization problem instances that is used for the CEC 2024 Competition on Dynamic Optimization.
A Robust Maximum Likelihood Distortionless Response Beamformer based on a Complex Generalized Gaussian Distribution
The proposed beamformer can be regarded as a generalization of the minimum power distortionless response beamformer and its improved variations.
Confused Modulo Projection based Somewhat Homomorphic Encryption -- Cryptosystem, Library and Applications on Secure Smart Cities
To make this algorithm easy to use, we also designed and implemented an efficient general blind computing library based on CMP-SWHE.
Instance Space Analysis for the Car Sequencing Problem
We investigate an important research question for solving the car sequencing problem, that is, which characteristics make an instance hard to solve?
A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction
In this paper, we propose a Deep Drift-Diffusion (DDD) model inspired by psychologists to predict aesthetic score distribution from images.
Consistency of Spectral Clustering on Hierarchical Stochastic Block Models
We study the hierarchy of communities in real-world networks under a generic stochastic block model, in which the connection probabilities are structured in a binary tree.
Rapid, Robust, and Reliable Blind Deconvolution via Nonconvex Optimization
To the best of our knowledge, our algorithm is the first blind deconvolution algorithm that is numerically efficient, robust against noise, and comes with rigorous recovery guarantees under certain subspace conditions.
Information Theory Information Theory
Stable Principal Component Pursuit
We further prove that the solution to a related convex program (a relaxed PCP) gives an estimate of the low-rank matrix that is simultaneously stable to small entrywise noise and robust to gross sparse errors.
Information Theory Information Theory
