Search Results for author:
Found 10 papers, 5 papers with code
Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement
The goal of video quality enhancement is to reduce compression artifacts and reconstruct a visually-pleasant result.
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems.
Perceptual Generative Autoencoders
We therefore propose to map both the generated and target distributions to a latent space using the encoder of a standard autoencoder, and train the generator (or decoder) to match the target distribution in the latent space.
Removing the Feature Correlation Effect of Multiplicative Noise
Multiplicative noise, including dropout, is widely used to regularize deep neural networks (DNNs), and is shown to be effective in a wide range of architectures and tasks.
A Block-wise, Asynchronous and Distributed ADMM Algorithm for General Form Consensus Optimization
Many machine learning models, including those with non-smooth regularizers, can be formulated as consensus optimization problems, which can be solved by the alternating direction method of multipliers (ADMM).
Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth Optimization
We study stochastic algorithms for solving nonconvex optimization problems with a convex yet possibly nonsmooth regularizer, which find wide applications in many practical machine learning applications.
Online Job Scheduling in Distributed Machine Learning Clusters
In a shared cluster handling multiple training jobs, a fundamental issue is how to efficiently schedule jobs and set the number of concurrent workers to run for each job, such that server resources are maximally utilized and model training can be completed in time.
Distributed, Parallel, and Cluster Computing
Device-to-Device Load Balancing for Cellular Networks
The idea is to shift traffic from a congested cell to its adjacent under-utilized cells by leveraging inter-cell D2D communication, so that the traffic can be served without using extra spectrum, effectively improving the spectrum temporal efficiency.
Networking and Internet Architecture
Normalized Direction-preserving Adam
Adaptive optimization algorithms, such as Adam and RMSprop, have shown better optimization performance than stochastic gradient descent (SGD) in some scenarios.
Expectile Matrix Factorization for Skewed Data Analysis
Matrix factorization is a popular approach to solving matrix estimation problems based on partial observations.
