2 citations · 2 across the 6 of their papers we have counts for
7 papers · 1 filter
Vector Symbolic Policy Gradient
Ryozo Masukawa, Sanggeon Yun, SungHeon Jeong +6
We answer this question with Vector-Symbolic Policy Gradient (VSPG), a discrete-action actor that represents each action by a unit-norm hypervector and scores it by similarity to t…
RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection
Quanling Zhao, Jiaying Yang, Ye Tian +5
Unsupervised tabular anomaly detection requires methods that are accurate, robust across heterogeneous datasets, and computationally efficient. Classical statistical detectors are…
-Musketeers: Reinforcement Learning Shapes Collaboration Among Language Models
Ryozo Masukawa, Sanggeon Yun, Hyunwoo Oh +8
Recent progress in reinforcement learning with verifiable rewards (RLVR) shows that small, specialized language models (SLMs) can exhibit structured reasoning without relying on la…
LogHD: Robust Compression of Hyperdimensional Classifiers via Logarithmic Class-Axis Reduction
Sanggeon Yun, Hyunwoo Oh, Ryozo Masukawa +3
Hyperdimensional computing (HDC) suits memory, energy, and reliability-constrained systems, yet the standard "one prototype per class" design requires memory (with clas…
HEAL: Brain-inspired Hyperdimensional Efficient Active Learning
Yang Ni, Zhuowen Zou, Wenjun Huang +6
Drawing inspiration from the outstanding learning capability of our human brains, Hyperdimensional Computing (HDC) emerges as a novel computing paradigm, and it leverages high-dime…
On the Benefits of Leveraging Structural Information in Planning Over the Learned Model
Jiajun Shen, Kananart Kuwaranancharoen, Raid Ayoub +2
Model-based Reinforcement Learning (RL) integrates learning and planning and has received increasing attention in recent years. However, learning the model can incur a significant…