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https://arxiv.org/html/2608.02800v1
com in China, Walmart Marketplace and Target Plus in the United States, Otto in Germany, Flipkart in India, and Rakuten in Japan
https://arxiv.org/html/2403.16413v3
For testing a simple null hypothesis against a simple alternative one, Neyman-Pearson’s fundamental lemma yields an optimal power property of the likelihood ratio test even for the case of parameter-dependent support
https://arxiv.org/html/2607.28294v1
A second scheme preserves the realized count and sets the number of events in the bootstrap world as n∗=n(T)n^{\ast}=n(T)
https://arxiv.org/html/2508.08980v3
111Majumdar and Sen (1976) work with an asymmetric relation PP
https://arxiv.org/list/cs.LG/2022-12
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Machine Learning
Authors and titles for December 2022
Total of 2004 entries : 1
https://arxiv.org/html/2607.24970v2
Although IA is Pareto-efficient and DA is not, conditional on truth-telling, we do not know how much better IA’s placement is in quantitative terms
https://arxiv.org/html/2608.02851v1
The design relies on two affine Volterra transformations involving both Fredholm- and Volterra-type integral terms, which results in a four-PIDE kernel equations
https://arxiv.org/html/2508.20101v1
The model combines classical GARCH(pp, qq) dynamics with spatially correlated innovations and spatially varying parameters, estimated using local likelihood methods
https://arxiv.org/html/1802.00050v1
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https://arxiv.org/list/cs.IT/new
The physical world is not changed by a reconfiguration of the observer, but its complex representation in the observation world is
https://arxiv.org/html/2608.02943v1
After projection through the recursion, the quantile terms admit a residual-rank-and-spacing representation, yielding feasible inference without innovation-density estimation or quantile smoothing
https://arxiv.org/abs/2608.10863
s success story in terms of language design
https://arxiv.org/html/2608.02942v1
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https://arxiv.org/html/2607.29442v2
1footnoteFootnotefootnotesFootnotes1footnote 1We are grateful to Carsten Chong for fruitful discussions on rough noise. We benefitted from comments by and discussions with Kim Christensen, Mikko Pakkanen, Bent Jesper Christensen, and Mikkel
https://arxiv.org/list/cs.PF/current
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Performance
Authors and titles for August 2026
Total of 47 entries
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[1] arXiv:
https://arxiv.org/list/cs.AI/2018-02
AI)
Journal-ref: Problems in programming, 2012, (2-3), pp
https://arxiv.org/list/cs.DC/2019-11
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Distributed, Parallel, and Cluster Computing
Authors and titles for November 2019
Total of 166 entries : 1-50 51-100 101-150 151-166
https://arxiv.org/html/2608.03573v1
Multi-Task Learning Paradigms for LLMs
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Distributed, Parallel, and Cluster Computing
Authors and titles for December 2021
Total of 203 entries : 1-50 51-100 101-150 151-200
https://arxiv.org/html/2407.00419v1
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Artificially intelligent agents deployed in the real-world will require the ability to reliably cooperate with humans (as well as other, heterogeneous AI agents)
https://arxiv.org/list/cs.ET/current
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Emerging Technologies
Authors and titles for August 2026
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https://arxiv.org/html/2407.08254v2
Results suggest that, in the presence of frequent failures, our solution improves substantially over the best existing approaches in terms of global utility and scalability
https://arxiv.org/abs/2006.02833
Our results reveal that MongoDB and MySQL Cluster work efficient in terms of throughput and operations latency if they burst into a public cloud to supply their resources
https://arxiv.org/html/2407.20441v1
However, in real-world multi-agent systems, inter-node communication imposes strong constraints, such as limited link capacity and transmission delays [4, 5, 6]
https://arxiv.org/html/2511.06451v1
Problem statement and motivation.
Thesis: risk-neutral pricing as a structured operator.
From selective scan to a risk-neutral operator layer.
Geometry in the loop: Q-Align and convex–monotone decoding.
Spectral stability as a first-class
https://arxiv.org/html/2407.15077v2
2 Problem Definition
3 Method
3.1 Monotonic Improvement of B2MAPO scheme
3.2 B2MAPO Framework
3.2.1 Batch Sequence Generation Layer
3.2.2 Batch by Batch Policy Optimization Layer
4 DAG-based B2MAPO Algorithm
4.1 Hierarchical DAG
https://arxiv.org/list/eess/new
The design relies on two affine Volterra transformations involving both Fredholm- and Volterra-type integral terms, which results in a four-PIDE kernel equations
https://arxiv.org/list/math/new
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https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking
https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs