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pro vyhledávání: '"A. Gormley"'
In metabolomics, the study of small molecules in biological samples, data are often acquired through mass spectrometry. The resulting data contain highly correlated variables, typically with a larger number of variables than observations. Missing dat
Externí odkaz:
http://arxiv.org/abs/2410.10633
Large language models pretrained on extensive web corpora demonstrate remarkable performance across a wide range of downstream tasks. However, a growing concern is data contamination, where evaluation datasets may be contained in the pretraining corp
Externí odkaz:
http://arxiv.org/abs/2407.08716
Many tasks within NLP can be framed as sequential decision problems, ranging from sequence tagging to text generation. However, for many tasks, the standard training methods, including maximum likelihood (teacher forcing) and scheduled sampling, suff
Externí odkaz:
http://arxiv.org/abs/2406.09393
Autor:
Bertsch, Amanda, Ivgi, Maor, Alon, Uri, Berant, Jonathan, Gormley, Matthew R., Neubig, Graham
As model context lengths continue to increase, the number of demonstrations that can be provided in-context approaches the size of entire training datasets. We study the behavior of in-context learning (ICL) at this extreme scale on multiple datasets
Externí odkaz:
http://arxiv.org/abs/2405.00200
The use of hyperspectral imaging to investigate food samples has grown due to the improved performance and lower cost of spectroscopy instrumentation. Food engineers use hyperspectral images to classify the type and quality of a food sample, typicall
Externí odkaz:
http://arxiv.org/abs/2403.03349
Disease control experts inspect public health data streams daily for outliers worth investigating, like those corresponding to data quality issues or disease outbreaks. However, they can only examine a few of the thousands of maximally-tied outliers
Externí odkaz:
http://arxiv.org/abs/2401.01459
The latent position model (LPM) is a popular method used in network data analysis where nodes are assumed to be positioned in a $p$-dimensional latent space. The latent shrinkage position model (LSPM) is an extension of the LPM which automatically de
Externí odkaz:
http://arxiv.org/abs/2311.16451
Most pretrained language models rely on subword tokenization, which processes text as a sequence of subword tokens. However, different granularities of text, such as characters, subwords, and words, can contain different kinds of information. Previou
Externí odkaz:
http://arxiv.org/abs/2311.07853
Low-dimensional representation and clustering of network data are tasks of great interest across various fields. Latent position models are routinely used for this purpose by assuming that each node has a location in a low-dimensional latent space, a
Externí odkaz:
http://arxiv.org/abs/2310.03630
Minimum Bayes Risk (MBR) decoding is a method for choosing the outputs of a machine learning system based not on the output with the highest probability, but the output with the lowest risk (expected error) among multiple candidates. It is a simple b
Externí odkaz:
http://arxiv.org/abs/2310.01387