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pro vyhledávání: '"Y Lan"'
Publikováno v:
3D Printing in Medicine, Vol 8, Iss 1, Pp 1-8 (2022)
Abstract The growing use of 3D printing in the biomedical sciences demonstrates its utility for a wide range of research and healthcare applications, including its potential implementation in the discipline of breath analysis to overcome current limi
Externí odkaz:
https://doaj.org/article/b409d1dfb9ac4193adf3e845d44b1d95
Autor:
Y Lan Pham, Jonathan Beauchamp
Publikováno v:
Molecules, Vol 26, Iss 18, p 5514 (2021)
The detection of chemical compounds in exhaled human breath presents an opportunity to determine physiological state, diagnose disease or assess environmental exposure. Recent advancements in metabolomics research have led to improved capabilities to
Externí odkaz:
https://doaj.org/article/7eb96809909844d1bdaeecf1e93450a0
Many cognitive approaches to well-being, such as recognizing and reframing unhelpful thoughts, have received considerable empirical support over the past decades, yet still lack truly widespread adoption in self-help format. A barrier to that adoptio
Externí odkaz:
http://arxiv.org/abs/2307.02768
Autor:
Behrooz, Morteza, Ngan, William, Lane, Joshua, Morse, Giuliano, Babcock, Benjamin, Shuster, Kurt, Komeili, Mojtaba, Chen, Moya, Kambadur, Melanie, Boureau, Y-Lan, Weston, Jason
Publicly deploying research chatbots is a nuanced topic involving necessary risk-benefit analyses. While there have recently been frequent discussions on whether it is responsible to deploy such models, there has been far less focus on the interactio
Externí odkaz:
http://arxiv.org/abs/2306.04765
Autor:
Xu, Jing, Ju, Da, Lane, Joshua, Komeili, Mojtaba, Smith, Eric Michael, Ung, Megan, Behrooz, Morteza, Ngan, William, Moritz, Rashel, Sukhbaatar, Sainbayar, Boureau, Y-Lan, Weston, Jason, Shuster, Kurt
We present BlenderBot 3x, an update on the conversational model BlenderBot 3, which is now trained using organic conversation and feedback data from participating users of the system in order to improve both its skills and safety. We are publicly rel
Externí odkaz:
http://arxiv.org/abs/2306.04707
Frozen models trained to mimic static datasets can never improve their performance. Models that can employ internet-retrieval for up-to-date information and obtain feedback from humans during deployment provide the promise of both adapting to new inf
Externí odkaz:
http://arxiv.org/abs/2208.03270
Autor:
Shuster, Kurt, Xu, Jing, Komeili, Mojtaba, Ju, Da, Smith, Eric Michael, Roller, Stephen, Ung, Megan, Chen, Moya, Arora, Kushal, Lane, Joshua, Behrooz, Morteza, Ngan, William, Poff, Spencer, Goyal, Naman, Szlam, Arthur, Boureau, Y-Lan, Kambadur, Melanie, Weston, Jason
We present BlenderBot 3, a 175B parameter dialogue model capable of open-domain conversation with access to the internet and a long-term memory, and having been trained on a large number of user defined tasks. We release both the model weights and co
Externí odkaz:
http://arxiv.org/abs/2208.03188
The promise of interaction between intelligent conversational agents and humans is that models can learn from such feedback in order to improve. Unfortunately, such exchanges in the wild will not always involve human utterances that are benign or of
Externí odkaz:
http://arxiv.org/abs/2208.03295
Autor:
Smith, Eric Michael, Hsu, Orion, Qian, Rebecca, Roller, Stephen, Boureau, Y-Lan, Weston, Jason
At the heart of improving conversational AI is the open problem of how to evaluate conversations. Issues with automatic metrics are well known (Liu et al., 2016, arXiv:1603.08023), with human evaluations still considered the gold standard. Unfortunat
Externí odkaz:
http://arxiv.org/abs/2201.04723
Current open-domain conversational models can easily be made to talk in inadequate ways. Online learning from conversational feedback given by the conversation partner is a promising avenue for a model to improve and adapt, so as to generate fewer of
Externí odkaz:
http://arxiv.org/abs/2110.07518