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Found 12 papers, 4 papers with code
Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval
The model uses a co-alignment strategy to generate codes optimized for maximizing retrieval efficiency.
ExMAG: Learning of Maximally Ancestral Graphs
Stochastic processes are represented by its vertices, and weighted oriented edges suggest the strength of the causal relationships.
Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration
In this work, we address the limitations of denoising diffusion models (DDMs) in image restoration tasks, particularly the shape and color distortions that can compromise image quality.
A Historical Trajectory Assisted Optimization Method for Zeroth-Order Federated Learning
In this work, we propose a non-isotropic sampling method to improve the gradient estimation procedure.
Cultural Adaptation of Menus: A Fine-Grained Approach
Machine Translation of Culture-Specific Items (CSIs) poses significant challenges.
Long-Range Vision-Based UAV-assisted Localization for Unmanned Surface Vehicles
The global positioning system (GPS) has become an indispensable navigation method for field operations with unmanned surface vehicles (USVs) in marine environments.
Causal Learning in Biomedical Applications: A Benchmark
Learning causal relationships between a set of variables is a challenging problem in computer science.
Joint Problems in Learning Multiple Dynamical Systems
Clustering of time series is a well-studied problem, with applications ranging from quantitative, personalized models of metabolism obtained from metabolite concentrations to state discrimination in quantum information theory.
Distributed Evolution Strategies for Black-box Stochastic Optimization
This work concerns the evolutionary approaches to distributed stochastic black-box optimization, in which each worker can individually solve an approximation of the problem with nature-inspired algorithms.
MMES: Mixture Model based Evolution Strategy for Large-Scale Optimization
This work provides an efficient sampling method for the covariance matrix adaptation evolution strategy (CMA-ES) in large-scale settings.
A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee
In addition, we theoretically analyze and prove the convergence of CMFL under different election and selection strategies, which coincides with the experimental results.
Blind Inverse Gamma Correction with Maximized Differential Entropy
Unwanted nonlinear gamma distortion frequently occurs in a great diversity of images during the procedures of image acquisition, processing, and/or display.
