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pro vyhledávání: '"Potts A."'
Models that rely on subword tokenization have significant drawbacks, such as sensitivity to character-level noise like spelling errors and inconsistent compression rates across different languages and scripts. While character- or byte-level models li
Externí odkaz:
http://arxiv.org/abs/2410.20771
Query Autocomplete (QAC) is a critical feature in modern search engines, facilitating user interaction by predicting search queries based on input prefixes. Despite its widespread adoption, the absence of large-scale, realistic datasets has hindered
Externí odkaz:
http://arxiv.org/abs/2411.04129
In-context learning (ICL) is a powerful technique for getting language models to perform complex tasks with no training updates. Prior work has established strong correlations between the number of in-context examples provided and the accuracy of the
Externí odkaz:
http://arxiv.org/abs/2410.16531
Semiconductor double quantum dots (DQD) coupled to superconducting microwave resonators offer a promising platform for the detection of single microwave photons. In previous works, the photodetection was studied for a monochromatic source of microwav
Externí odkaz:
http://arxiv.org/abs/2410.14278
The rapid introduction of new brand names into everyday language poses a unique challenge for e-commerce spelling correction services, which must distinguish genuine misspellings from novel brand names that use unconventional spelling. We seek to add
Externí odkaz:
http://arxiv.org/abs/2410.11655
Quantum acoustics is an emerging platform for hybrid quantum technologies enabling quantum coherent control of mechanical vibrations. High-overtone bulk acoustic resonators (HBARs) represent an attractive mechanical implementation of quantum acoustic
Externí odkaz:
http://arxiv.org/abs/2410.10272
Autor:
Potts, Jonathan R.
Aggregation-diffusion equations are foundational tools for modelling biological aggregations. Their principal use is to link the collective movement mechanisms of organisms to their emergent space use patterns in a rigorous, non-speculative way. Howe
Externí odkaz:
http://arxiv.org/abs/2409.10147
Quality pretraining data is often seen as the key to high-performance language models. However, progress in understanding pretraining data has been slow due to the costly pretraining runs required for data selection experiments. We present a framewor
Externí odkaz:
http://arxiv.org/abs/2409.05816
Autor:
Day, Matthew W., Kusyak, Kateryna, Sturm, Felix, Aranzadi, Juan I., Bretscher, Hope M., Fechner, Michael, Matsuyama, Toru, Michael, Marios H., Schulte, Benedikt F., Li, Xinyu, Hagelstein, Jesse, Herrmann, Dorothee, Kipp, Gunda, Potts, Alex M., DeStefano, Jonathan M., Hu, Chaowei, Huang, Yunfei, Taniguchi, Takashi, Watanabe, Kenji, Meier, Guido, Shin, Dongbin, Rubio, Angel, Chu, Jiun-Haw, Kennes, Dante M., Sentef, Michael A., McIver, James W.
Periodic laser driving, known as Floquet engineering, is a powerful tool to manipulate the properties of quantum materials. Using circularly polarized light, artificial magnetic fields, called Berry curvature, can be created in the photon-dressed Flo
Externí odkaz:
http://arxiv.org/abs/2409.04531
The Linear Representation Hypothesis (LRH) states that neural networks learn to encode concepts as directions in activation space, and a strong version of the LRH states that models learn only such encodings. In this paper, we present a counterexampl
Externí odkaz:
http://arxiv.org/abs/2408.10920