activity
20202026
most citedAn Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection

15 citations · 28 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV2026

Prior-Conditioned Gaussian Discriminants for Generalizable AI-generated Image Detection

Shashank Kotyan, Makoto Shing, Yuki Imajuku +2

Diffusion-based generators have made synthetic images ubiquitous, but detectors often fail under simultaneous shifts in generator, prompt/style, and source-domain. We study AI-gene…

cs.LG2026

Diffusing Blame: Task-Dependent Credit Assignment in Biologically Plausible Dual-Stream Networks

Yutaro Yamada, Luca Grillotti, Rujikorn Charakorn +3

Biological neural circuits obey Dale's principle: each neuron's synapses are uniformly excitatory or inhibitory. Artificial networks that respect this constraint must coordinate se…

cs.CL2026

Doc-to-LoRA: Learning to Instantly Internalize Contexts

Rujikorn Charakorn, Edoardo Cetin, Shinnosuke Uesaka +1

Long input sequences are central to in-context learning, document understanding, and multi-step reasoning of Large Language Models (LLMs). However, the quadratic attention cost of…

cs.LG2025

Text-to-LoRA: Instant Transformer Adaption

Rujikorn Charakorn, Edoardo Cetin, Yujin Tang +1

While Foundation Models provide a general tool for rapid content creation, they regularly require task-specific adaptation. Traditionally, this exercise involves careful curation o…

cs.AI2025

From Grunts to Lexicons: Emergent Language from Cooperative Foraging

Maytus Piriyajitakonkij, Rujikorn Charakorn, Weicheng Tao +4

Language is a powerful communicative and cognitive tool. It enables humans to express thoughts, share intentions, and reason about complex phenomena. Despite our fluency in using a…

cs.LG2024★ 3 cited

Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning

Shengyi Huang, Quentin Gallouédec, Florian Felten +30

In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curv…