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30 result(s) for “okapi bm25 site terms com pp site www term olimia com site does could goode com site:arxiv.org” of 126 matched · page 2 · 513 ms
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https://arxiv.org/html/2607.27188v1
5 Real NIFTY data: adaptation helps but does not close the gap
6 Discussion
6
https://arxiv.org/html/2608.00328v2
The exception is a restriction to maritime traffic in the South and East China Sea, which could raise costs by 10 $/t for China, whilst reducing costs for other importers in the region by similar levels
https://arxiv.org/html/2608.03529v1
(2024), where a human expert and an automated system might describe information in different terms
https://arxiv.org/html/2608.03452v1
And when a learner reproduces the pattern, does it thereby represent it, or only its visible alternations
https://arxiv.org/html/2608.02966v1
(c) Because these distractor preferences vary with ability, their identities provide Fisher information that correctness does not retain
https://arxiv.org/html/2608.00648v1
Distributed Multi-Agent Systems
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https://arxiv.org/html/2608.02918v1
Miranda is also with LCTI, Télécom Paris, IP Paris, France
https://arxiv.org/html/2609.03027v1
Such compromises could severely curtail their potential to discover transient phenomena
https://arxiv.org/html/2607.27063v1
The design does not claim that every momentum or reversal episode is caused by herding; instead, it provides a disciplined mechanism through which herding can amplify informational price adjustment
https://arxiv.org/html/2607.28577v1
Production forecasting systems retrain models regularly, but a retrained candidate does not necessarily outperform a continuously maintained incumbent that has continued to learn
https://arxiv.org/html/2608.03358v1
However, it does not satisfy these requirements
https://arxiv.org/html/2407.10811v1
Recently, employing reinforcement learning (RL) to solve TSC has become a research hotspot since it does not require too much artificial priori and can improve performance significantly through the paradigm of trial and error
https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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https://arxiv.org/html/2608.03086v1
Physics-Guided Deep Learning Model
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https://arxiv.org/html/2608.03480v1
com Arabic Institute for Translation, Algiers, Algeria CERIST, Algeria Nasredine Semmar CEA LIST, Paris-Saclay, France Hassina Aliane CERIST, Algeria
Abstract
The adoption of large pre-trained multilingual models for neural machine translation (MNMT) faces a major challenge: excessive memory and computational
https://arxiv.org/html/2607.28410v1
com/zaneopen/PACE
1 Introduction
Reducing execution costs is a central objective for financial institutions (Bertsimas and Lo 1998)
https://arxiv.org/pdf/2608.03157
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https://arxiv.org/html/2608.03154v1
com/COMBINI-Hub/Multi_Agent_Relation_Extraction
https://arxiv.org/html/2608.02813v1
Wind Turbine Generators
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https://arxiv.org/html/2608.03011v1
via Disentangled Audio Infilling
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https://arxiv.org/html/2608.02971v1
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https://arxiv.org/html/2608.03233v1
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https://arxiv.org/html/2608.03545v1
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https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2407.00762v1
Attackers
I Introduction
II Problem Formulation
II-A Notation
II-B Problem Statement
III Preliminary: Single-Attacker Game
IV Main Results
IV-A Optimization Formulation of a Geometric Solution
IV-B Proof of Equilibrium
V Numerical
https://arxiv.org/html/2608.03141v1
Token Communication
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https://arxiv.org/html/2608.03655v1
Summarization
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https://arxiv.org/pdf/2102.13654
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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/pdf/2508.02691
7 % 1 0 obj > endobj 2 0 obj > endobj 3 0 obj > stream Statistical modeling of SOFR term structureTeemu PennanenWaleed Taoumhttp://arxiv