Zobrazeno 1 - 10
of 3 795
pro vyhledávání: '"Quirke P"'
Autor:
Quirke, Jennifer, Möbius, Matthias E.
Understanding and predicting the spreading of droplets on solid surfaces is crucial in many applications such as inkjet printing, printed electronics and spray coating where the fluid is a suspension and in general non-Newtonian. However, many models
Externí odkaz:
http://arxiv.org/abs/2408.06290
Autor:
Taufeeque, Mohammad, Quirke, Philip, Li, Maximilian, Cundy, Chris, Tucker, Aaron David, Gleave, Adam, Garriga-Alonso, Adrià
How a neural network (NN) generalizes to novel situations depends on whether it has learned to select actions heuristically or via a planning process. "An investigation of model-free planning" (Guez et al. 2019) found that a recurrent NN (RNN) traine
Externí odkaz:
http://arxiv.org/abs/2407.15421
The distributional simplicity bias (DSB) posits that neural networks learn low-order moments of the data distribution first, before moving on to higher-order correlations. In this work, we present compelling new evidence for the DSB by showing that n
Externí odkaz:
http://arxiv.org/abs/2402.04362
Language Models (LMs) are increasingly used for a wide range of prediction tasks, but their training can often neglect rare edge cases, reducing their reliability. Here, we define a stringent standard of trustworthiness whereby the task algorithm and
Externí odkaz:
http://arxiv.org/abs/2402.02619
Autor:
Ivanitskiy, Michael Igorevich, Spies, Alex F., Räuker, Tilman, Corlouer, Guillaume, Mathwin, Chris, Quirke, Lucia, Rager, Can, Shah, Rusheb, Valentine, Dan, Behn, Cecilia Diniz, Inoue, Katsumi, Fung, Samy Wu
Transformer models underpin many recent advances in practical machine learning applications, yet understanding their internal behavior continues to elude researchers. Given the size and complexity of these models, forming a comprehensive picture of t
Externí odkaz:
http://arxiv.org/abs/2312.02566
Prior work has shown the existence of contextual neurons in language models, including a neuron that activates on German text. We show that this neuron exists within a broader contextual n-gram circuit: we find late layer neurons which recognize and
Externí odkaz:
http://arxiv.org/abs/2311.00863
Autor:
Quirke, Philip, Barez, Fazl
Understanding the inner workings of machine learning models like Transformers is vital for their safe and ethical use. This paper provides a comprehensive analysis of a one-layer Transformer model trained to perform n-digit integer addition. Our find
Externí odkaz:
http://arxiv.org/abs/2310.13121
Autor:
Ivanitskiy, Michael Igorevich, Shah, Rusheb, Spies, Alex F., Räuker, Tilman, Valentine, Dan, Rager, Can, Quirke, Lucia, Mathwin, Chris, Corlouer, Guillaume, Behn, Cecilia Diniz, Fung, Samy Wu
Understanding how machine learning models respond to distributional shifts is a key research challenge. Mazes serve as an excellent testbed due to varied generation algorithms offering a nuanced platform to simulate both subtle and pronounced distrib
Externí odkaz:
http://arxiv.org/abs/2309.10498
Autor:
Kanishta Srihar, Arief Gusnanto, Susan D. Richman, Nicholas P. West, Leanne Galvin, Daniel Bottomley, Gemma Hemmings, Amy Glover, Subaashini Natarajan, Rebecca Miller, Sameira Arif, Hannah Rossington, Thomas L. Dunwell, Simon C. Dailey, Gracielle Fontarum, Agnes George, Winnie Wu, Phil Quirke, Henry M. Wood
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)
Abstract Microsatellite instability (MSI) occurs across a number of cancers and is associated with different clinical characteristics when compared to microsatellite stable (MSS) cancers. As MSI cancers have different characteristics, routine MSI tes
Externí odkaz:
https://doaj.org/article/a79db419a61e40a695fcb42742c491c4
Autor:
Wagner, Sophia J., Reisenbüchler, Daniel, West, Nicholas P., Niehues, Jan Moritz, Veldhuizen, Gregory Patrick, Quirke, Philip, Grabsch, Heike I., Brandt, Piet A. van den, Hutchins, Gordon G. A., Richman, Susan D., Yuan, Tanwei, Langer, Rupert, Jenniskens, Josien Christina Anna, Offermans, Kelly, Mueller, Wolfram, Gray, Richard, Gruber, Stephen B., Greenson, Joel K., Rennert, Gad, Bonner, Joseph D., Schmolze, Daniel, James, Jacqueline A., Loughrey, Maurice B., Salto-Tellez, Manuel, Brenner, Hermann, Hoffmeister, Michael, Truhn, Daniel, Schnabel, Julia A., Boxberg, Melanie, Peng, Tingying, Kather, Jakob Nikolas
Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of microsatellite instability (MSI) in CRC has been approved in 2022. Current
Externí odkaz:
http://arxiv.org/abs/2301.09617