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https://arxiv.org/html/2407.13076v1
Index Terms:
LoRa, multigateway, system energy efficiency, matching-based, multiagent deep reinforcement learning
https://arxiv.org/html/2407.02342v1
com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learnin
https://arxiv.org/list/astro-ph.SR/new
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Solar and Stellar Astrophysics
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https://arxiv.org/html/2608.03077v1
cn
1 Introduction
Figure 1: Overview of vanilla reasoning-augmented MT and PAMT
https://arxiv.org/pdf/1911.06462
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https://arxiv.org/src/2006.15704
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https://arxiv.org/html/2608.03149v1
I Introduction
Real-time dispatch of steelmaking process loads (SPLs) in response to fluctuations in local renewable generation and grid electricity prices is essential for increasing on-site renewable energy utilization and reducing electricity procurement costs in industrial microgrids [TOP1, TOP4]
https://arxiv.org/abs/astro-ph/0004319
Since 1995, the northern Hubble deep field has been the focus of deep surveys at nearly all wavelengths
https://arxiv.org/abs/2002.02145
Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives, by Sanket Tavarageri and 5 other authors
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Abstract:At the heart of deep learning training and inferencing are computationally intensive primitives such as convolutions which form the building blocks of
https://arxiv.org/abs/2002.02145?context=cs
Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives, by Sanket Tavarageri and 5 other authors
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Abstract:At the heart of deep learning training and inferencing are computationally intensive primitives such as convolutions which form the building blocks of
https://arxiv.org/html/2608.02668v1
com/hazdzz/deep_connection \ncudata[Correspondence]Jie Zhang (),
Correspondence: Cheng-Fang Su ()
“Geometry is not true, it is advantageous
https://arxiv.org/html/2407.10064v4
An Overview of Applications and Insights
1 Introduction
2 Background
2.1 Review of Bridge Operation and maintenance Research
2.2 Overview of LLM-based Agents Research
2.2.1 Larger Language Models
2.2.2 LLM-based Agents
2.3 Why Bridge
https://arxiv.org/list/cs.LG/2022-12
00015 [pdf, other]
Title: Scalable Pathogen Detection from Next Generation DNA Sequencing with Deep Learning
Sai Narayanan, Sathyanarayanan N
https://arxiv.org/abs/astro-ph/0102037
at high redshift: High resolution EVN radio observations of the Hubble Deep Field, by M
https://arxiv.org/abs/1904.05873
PDF HTML (experimental)
Abstract:Attention mechanisms have become a popular component in deep neural networks, yet there has been little examination of how different influencing factors and methods for computing attention from these factors affect performance
https://arxiv.org/abs/astro-ph/0008509
Garrett (JIVE) and 3 other authors
Abstract: We present deep WSRT 1
https://arxiv.org/abs/1810.04805
Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers
https://arxiv.org/list/cs.LG/2023-09
00330 [pdf, other]
Title: Multitask Deep Learning for Accurate Risk Stratification and Prediction of Next Steps for Coronary CT Angiography Patients
Juan Lu, Mohammed Bennamoun, Jonathon Stewart, JasonK
https://arxiv.org/list/eess.IV/recent
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Image and Video Processing
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Wed, 5 Aug
https://arxiv.org/html/2501.13956v1
com
Abstract
We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark
https://arxiv.org/html/2407.19144v1/
In the field of Multi-Agent Reinforcement Learning (MARL) for cooperative tasks, the Centralized Training with Decentralized Execution (CTDE) [4] paradigm has emerged as a prominent approach, notably through recent advancements in deep reinforcement learning [5, 6, 7]
https://arxiv.org/html/2407.01501v2
We investigate methods of online learning in Neuro-Evolution and Deep Recurrent Q-Networks to enable agents to attempt the problem one-shot as is often required by wicked social problems
https://arxiv.org/html/2508.16279v1
Additionally, AgentScope integrates several built-in agents, including a browser-use agent, a deep research agent, and a meta-planner agent
https://arxiv.org/list/cs.IR/new
CL)
Existing deep-research agents use a search-visit workflow that retrieves and reads whole pages, without considering the addressable structure that web sources expose through titles, headings, sections, and metadata
https://arxiv.org/html/2607.28577v1
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https://arxiv.org/html/2511.06451v1
3 Arbitrage-free term structures and deep calibration
Concluding remark
https://arxiv.org/html/2608.02609v1
(2023) (50K), a shared translation task Deep Past Initiative
https://arxiv.org/html/2407.15077v2
cn Wenqing HU Yifan WANG Harbin Institute of Technology
Abstract
Most multi-agent reinforcement learning approaches adopt two types of policy optimization methods that either update policy simultaneously or sequentially
https://arxiv.org/list/eess.IV/new
Deep learning-based denoising methods have demonstrated strong potential for improving PET image quality
https://arxiv.org/html/2607.27188v1
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