Zobrazeno 1 - 10
of 1 346
pro vyhledávání: '"P. Sedova"'
Autor:
Skryabin, N. N., Biriukov, Yu. A., Dryazgov, M. A., Fldzhyan, S. A., Zhuravitskii, S. A., Argenchiev, A. S., Kondratyev, I. V., Tsoma, L. A., Okhlopkov, K. I., Gruzinov, I. M., Taratorin, K. V., Saygin, M. Yu., Dyakonov, I. V., Rakhlin, M. V., Galimov, A. I., Klimko, G. V., Sorokin, S. V., Sedova, I. V., Kulagina, M. M., Zadiranov, Yu. M., Toropov, A. A., Evlashin, S. A., Korneev, A. A., Kulik, S. P., Straupe, S. S.
We present an experimental platform for linear-optical quantum information processing. Our setup utilizes multiphoton generation using a high-quality single-photon source, which is demultiplexed across multiple spatial channels, a custom-designed, pr
Externí odkaz:
http://arxiv.org/abs/2410.15697
Autor:
Serov, Yuriy, Galimov, Aidar, Smirnov, Dmitry S., Rakhlin, Maxim, Leppenen, Nikita, Klimko, Grigorii, Sorokin, Sergey, Sedova, Irina, Berezina, Daria, Salii, Yuliya, Kulagina, Marina, Zadiranov, Yuriy, Troshkov, Sergey, Shubina, Tatiana V., Toropov, Alexey
Photon entanglement is indispensable for optical quantum technologies. Measurement-based optical quantum computing and all-optical quantum networks rely on multiphoton cluster states consisting of indistinguishable entangled photons. A promising meth
Externí odkaz:
http://arxiv.org/abs/2410.02562
Autor:
Shanmugavelu, Sanjif, Taillefumier, Mathieu, Culver, Christopher, Hernandez, Oscar, Coletti, Mark, Sedova, Ada
Run to run variability in parallel programs caused by floating-point non-associativity has been known to significantly affect reproducibility in iterative algorithms, due to accumulating errors. Non-reproducibility can critically affect the efficienc
Externí odkaz:
http://arxiv.org/abs/2408.05148
One of the major aspects contributing to the striking performance of large language models (LLMs) is the vast amount of factual knowledge accumulated during pre-training. Yet, many LLMs suffer from self-inconsistency, which raises doubts about their
Externí odkaz:
http://arxiv.org/abs/2407.17125
We suggest a somewhat non-standard view on a set of curious, paradoxical from the standpoint of simple classical physics and everyday experience phenomena. There are the quantisation (discrete set of values) of the observables (e.g., energy, momentum
Externí odkaz:
http://arxiv.org/abs/2406.16686
This paper presents the first study for temporal relation extraction in a zero-shot setting focusing on biomedical text. We employ two types of prompts and five LLMs (GPT-3.5, Mixtral, Llama 2, Gemma, and PMC-LLaMA) to obtain responses about the temp
Externí odkaz:
http://arxiv.org/abs/2406.11486
Autor:
Xia, Yuxi, Sedova, Anastasiia, de Araujo, Pedro Henrique Luz, Kougia, Vasiliki, Nußbaumer, Lisa, Roth, Benjamin
Training data memorization in language models impacts model capability (generalization) and safety (privacy risk). This paper focuses on analyzing prompts' impact on detecting the memorization of 6 masked language model-based named entity recognition
Externí odkaz:
http://arxiv.org/abs/2405.03004
Autor:
Chahal, Rajni, Toomey, Michael D., Kearney, Logan T., Sedova, Ada, Damron, Joshua T., Naskar, Amit K., Roy, Santanu
Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units are in
Externí odkaz:
http://arxiv.org/abs/2404.16187
Publikováno v:
Proceedings of Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023, Turin, Italy, September 18-22, 2023
An accurate and substantial dataset is essential for training a reliable and well-performing model. However, even manually annotated datasets contain label errors, not to mention automatically labeled ones. Previous methods for label denoising have p
Externí odkaz:
http://arxiv.org/abs/2306.04502
Autor:
Sedova, Anastasiia, Roth, Benjamin
Publikováno v:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1853-1863, July 2023, Toronto, Canada
Self-supervised knowledge-graph completion (KGC) relies on estimating a scoring model over (entity, relation, entity)-tuples, for example, by embedding an initial knowledge graph. Prediction quality can be improved by calibrating the scoring model, t
Externí odkaz:
http://arxiv.org/abs/2305.06395