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Found 5 papers, 1 papers with code
BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models
This work presents BAdam, an optimizer that leverages the block coordinate optimization framework with Adam as the inner solver.
Low-Rank Structured Clutter Covariance Matrix Estimation for Airborne STAP Radar
In space-time adaptive processing (STAP) of the airborne radar system, it is very important to realize sparse restoration of the clutter covariance matrix with a small number of samples.
Finite-Time Analysis of Fully Decentralized Single-Timescale Actor-Critic
Most of the existing finite-time convergence results are derived based on either double-loop update or two-timescale step sizes rule, and this is the case even for centralized AC algorithm under a single-agent setting.
Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA), which aims to learn models in a partially labeled target domain with the assistance of the fully labeled source domain, attracts increasing attention in recent years.
Batch Group Normalization
Among previous normalization methods, Batch Normalization (BN) performs well at medium and large batch sizes and is with good generalizability to multiple vision tasks, while its performance degrades significantly at small batch sizes.
