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20162023
most citedImproving the Expected Improvement Algorithm

39 citations · 157 across the 33 of their papers we have counts for

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Showing 2023Show all

11 papers · 1 filter

cs.CV20237 cited

Unsupervised Video Summarization via Iterative Training and Simplified GAN

Hanqing Li, Diego Klabjan, Jean Utke

This paper introduces a new, unsupervised method for automatic video summarization using ideas from generative adversarial networks but eliminating the discriminator, having a simp…

cs.CV2023

Semi-supervised 3D Video Information Retrieval with Deep Neural Network and Bi-directional Dynamic-time Warping Algorithm

Yintai Ma, Diego Klabjan

This paper presents a novel semi-supervised deep learning algorithm for retrieving similar 2D and 3D videos based on visual content. The proposed approach combines the power of dee…

cs.LG2023

Regret Lower Bounds in Multi-agent Multi-armed Bandit

Mengfan Xu, Diego Klabjan

Multi-armed Bandit motivates methods with provable upper bounds on regret and also the counterpart lower bounds have been extensively studied in this context. Recently, Multi-agent…

cs.LG20231 cited

Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints

Jaeyeon Jang, Diego Klabjan, Han Liu +5

This paper proposes a novel multi-agent reinforcement learning (MARL) method to learn multiple coordinated agents under directed acyclic graph (DAG) constraints. Unlike existing MA…

cs.CV2023

S-Omninet: Structured Data Enhanced Universal Multimodal Learning Architecture

Ye Xue, Diego Klabjan, Jean Utke

Multimodal multitask learning has attracted an increasing interest in recent years. Singlemodal models have been advancing rapidly and have achieved astonishing results on various…

cs.LG2023

Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewards

Mengfan Xu, Diego Klabjan

We study a decentralized multi-agent multi-armed bandit problem in which multiple clients are connected by time dependent random graphs provided by an environment. The reward distr…