Zobrazeno 1 - 10
of 19 209
pro vyhledávání: '"A, Arda"'
Autor:
Eren, Mehmet Arda, Oztop, Erhan
In self-supervised robot learning, robots actively explore their environments and generate data by acting on entities in the environment. Therefore, an exploration policy is desired that ensures sample efficiency to minimize robot execution costs whi
Externí odkaz:
http://arxiv.org/abs/2412.02331
In this paper, we propose a novel lightweight learning from demonstration (LfD) model based on reservoir computing that can learn and generate multiple movement trajectories with prediction intervals, which we call as Context-based Echo State Network
Externí odkaz:
http://arxiv.org/abs/2412.00541
Virtualization technology, Network Function Virtualization (NFV), gives flexibility to communication and 5G core network technologies for dynamic and efficient resource allocation while reducing the cost and dependability of the physical infrastructu
Externí odkaz:
http://arxiv.org/abs/2411.12851
Autor:
Güçlü, Arda, Bose, Subhonmesh
We propose and analyze TRAiL (Tangential Randomization in Linear Bandits), a computationally efficient regret-optimal forced exploration algorithm for linear bandits on action sets that are sublevel sets of strongly convex functions. TRAiL estimates
Externí odkaz:
http://arxiv.org/abs/2411.12154
Recent advances in foundational Vision Language Models (VLMs) have reshaped the evaluation paradigm in computer vision tasks. These foundational models, especially CLIP, have accelerated research in open-vocabulary computer vision tasks, including Op
Externí odkaz:
http://arxiv.org/abs/2411.12044
This paper presents a novel approach to one-class classifier fusion through locally adaptive learning with dynamic $\ell$p-norm constraints. We introduce a framework that dynamically adjusts fusion weights based on local data characteristics, address
Externí odkaz:
http://arxiv.org/abs/2411.06406
Autor:
AL, Altera., Ahn, Andrew, Becker, Nic, Carroll, Stephanie, Christie, Nico, Cortes, Manuel, Demirci, Arda, Du, Melissa, Li, Frankie, Luo, Shuying, Wang, Peter Y, Willows, Mathew, Yang, Feitong, Yang, Guangyu Robert
AI agents have been evaluated in isolation or within small groups, where interactions remain limited in scope and complexity. Large-scale simulations involving many autonomous agents -- reflecting the full spectrum of civilizational processes -- have
Externí odkaz:
http://arxiv.org/abs/2411.00114
Recent advances in large language models have demonstrated promising capabilities in following simple instructions through instruction tuning. However, real-world tasks often involve complex, multi-step instructions that remain challenging for curren
Externí odkaz:
http://arxiv.org/abs/2410.18529
Following the success of Large Language Models (LLMs), expanding their boundaries to new modalities represents a significant paradigm shift in multimodal understanding. Human perception is inherently multimodal, relying not only on text but also on a
Externí odkaz:
http://arxiv.org/abs/2410.18325
Autor:
Aydin, Arda, Barg, Alexander
We construct a general family of quantum codes that protect against all emission, absorption, dephasing, and raising/lowering errors up to an arbitrary fixed order. Such codes are known in the literature as absorption-emission (AE) codes. We derive s
Externí odkaz:
http://arxiv.org/abs/2410.03562