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20192026
most citedMulti-Agent Reinforcement Learning for Power Grid Topology Optimization

5 citations · 10 across the 12 of their papers we have counts for

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cs.LG2026

Decodable but not structured: linear probing enables Underwater Acoustic Target Recognition with pretrained audio embeddings

Hilde I. Hummel, Sandjai Bhulai, Rob D. van der Mei +1

Increasing levels of anthropogenic noise from ships contribute significantly to underwater sound pollution, posing risks to marine ecosystems. This makes monitoring crucial to unde…

cs.LG2024

A Machine Learning Approach for Simultaneous Demapping of QAM and APSK Constellations

Arwin Gansekoele, Alexios Balatsoukas-Stimming, Tom Brusse +3

As telecommunication systems evolve to meet increasing demands, integrating deep neural networks (DNNs) has shown promise in enhancing performance. However, the trade-off between a…

cs.LG20235 cited

Multi-Agent Reinforcement Learning for Power Grid Topology Optimization

Erica van der Sar, Alessandro Zocca, Sandjai Bhulai

Recent challenges in operating power networks arise from increasing energy demands and unpredictable renewable sources like wind and solar. While reinforcement learning (RL) shows…

cs.LG20223 cited

The Dutch Draw: Constructing a Universal Baseline for Binary Prediction Models

Etienne van de Bijl, Jan Klein, Joris Pries +3

Novel prediction methods should always be compared to a baseline to know how well they perform. Without this frame of reference, the performance score of a model is basically meani…

cs.LG2020

Invertible DenseNets

Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai

We introduce Invertible Dense Networks (i-DenseNets), a more parameter efficient alternative to Residual Flows. The method relies on an analysis of the Lipschitz continuity of the…