Tootfinder

Comparative Analysis of Parameterized Action Actor-Critic Reinforcement Learning Algorithms for Web Search Match Plan Generation
Ubayd Bapoo, Clement N Nyirenda
https://arxiv.org/abs/2510.03064

Comparative Analysis of Parameterized Action Actor-Critic Reinforcement Learning Algorithms for Web Search Match Plan Generation
This study evaluates the performance of Soft Actor Critic (SAC), Greedy Actor Critic (GAC), and Truncated Quantile Critics (TQC) in high-dimensional decision-making tasks using fully observable environments. The focus is on parametrized action (PA) spaces, eliminating the need for recurrent networks, with benchmarks Platform-v0 and Goal-v0 testing discrete actions linked to continuous action-parameter spaces. Hyperparameter optimization was performed with Microsoft NNI, ensuring reproducibility…

Distributed Source Coding for Compressing Vector-Linear Functions
Xuan Guang, Xiufang Sun, Ruze Zhang
https://arxiv.org/abs/2508.02996 https://arxiv.org/pd…

Distributed Source Coding for Compressing Vector-Linear Functions
Inspired by mobile satellite communication systems and the important and prevalent applications of computational tasks, we consider a distributed source coding model for compressing vector-linear functions, which consists of multiple sources, multiple encoders and a decoder linked to all the encoders. Each encoder has access to a certain subset of the sources and the decoder is required to compute with zero error a vector-linear function of the source information, which corresponds to a matrix …

Echoes of the past: A unified perspective on fading memory and echo states
Juan-Pablo Ortega, Florian Rossmannek
https://arxiv.org/abs/2508.19145 https://a…

Echoes of the past: A unified perspective on fading memory and echo states
Recurrent neural networks (RNNs) have become increasingly popular in information processing tasks involving time series and temporal data. A fundamental property of RNNs is their ability to create reliable input/output responses, often linked to how the network handles its memory of the information it processed. Various notions have been proposed to conceptualize the behavior of memory in RNNs, including steady states, echo states, state forgetting, input forgetting, and fading memory. Although…