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pro vyhledávání: '"A Snell"'
A fundamental open challenge in modern LLM scaling is the lack of understanding around emergent capabilities. In particular, language model pretraining loss is known to be highly predictable as a function of compute. However, downstream capabilities
Externí odkaz:
http://arxiv.org/abs/2411.16035
A reliable deep learning system should be able to accurately express its confidence with respect to its predictions, a quality known as calibration. One of the most effective ways to produce reliable confidence estimates with a pre-trained model is b
Externí odkaz:
http://arxiv.org/abs/2410.05407
Enabling LLMs to improve their outputs by using more test-time computation is a critical step towards building generally self-improving agents that can operate on open-ended natural language. In this paper, we study the scaling of inference-time comp
Externí odkaz:
http://arxiv.org/abs/2408.03314
Autor:
Riley, Richard D, Collins, Gary S, Whittle, Rebecca, Archer, Lucinda, Snell, Kym IE, Dhiman, Paula, Kirton, Laura, Legha, Amardeep, Liu, Xiaoxuan, Denniston, Alastair, Harrell Jr, Frank E, Wynants, Laure, Martin, Glen P, Ensor, Joie
When developing a clinical prediction model, the sample size of the development dataset is a key consideration. Small sample sizes lead to greater concerns of overfitting, instability, poor performance and lack of fairness. Previous research has outl
Externí odkaz:
http://arxiv.org/abs/2407.09293
Autor:
Atkins, Carolyn, Chahid, Younes, Lister, Gregory, Tuck, Rhys, Kotlewski, Richard, Snell, Robert M., Livera, Elaine R., Faour, Mariam, Todd, Iain, Deffley, Robert, Shipley, James, Walsh, Tom, Gardstam, Johannes, Bourgenot, Cyril, White, Paul, Davies, Spencer, Tammas-Williams, Samuel
Additive manufacturing (AM; 3D printing) in aluminium using laser powder bed fusion provides a new design space for lightweight mirror production. Printing layer-by-layer enables the use of intricate lattices for mass reduction, as well as organic sh
Externí odkaz:
http://arxiv.org/abs/2407.07405
Autor:
Whittle, Rebecca, Ensor, Joie, Archer, Lucinda, Collins, Gary S., Dhiman, Paula, Denniston, Alastair, Alderman, Joseph, Legha, Amardeep, van Smeden, Maarten, Moons, Karel G., Cazier, Jean-Baptiste, Riley, Richard D., Snell, Kym I. E.
When evaluating the performance of a model for individualised risk prediction, the sample size needs to be large enough to precisely estimate the performance measures of interest. Current sample size guidance is based on precisely estimating calibrat
Externí odkaz:
http://arxiv.org/abs/2406.19673
Autor:
Marjieh, Raja, Kumar, Sreejan, Campbell, Declan, Zhang, Liyi, Bencomo, Gianluca, Snell, Jake, Griffiths, Thomas L.
Humans rely on strong inductive biases to learn from few examples and abstract useful information from sensory data. Instilling such biases in machine learning models has been shown to improve their performance on various benchmarks including few-sho
Externí odkaz:
http://arxiv.org/abs/2405.19420
Over the past several years, the misidentification of SpaceX Starlink satellites as Unidentified Aerial Phenomena (UAP) by pilots and laypersons has generated unnecessary aviation risk and confusion. The many deployment and orbital evolution strategi
Externí odkaz:
http://arxiv.org/abs/2403.08155
Autor:
Amirizaniani, Maryam, Yao, Jihan, Lavergne, Adrian, Okada, Elizabeth Snell, Chadha, Aman, Roosta, Tanya, Shah, Chirag
As Large Language Models (LLMs) become more pervasive across various users and scenarios, identifying potential issues when using these models becomes essential. Examples of such issues include: bias, inconsistencies, and hallucination. Although audi
Externí odkaz:
http://arxiv.org/abs/2402.09346
Most applications of machine learning to classification assume a closed set of balanced classes. This is at odds with the real world, where class occurrence statistics often follow a long-tailed power-law distribution and it is unlikely that all clas
Externí odkaz:
http://arxiv.org/abs/2311.14601