Zobrazeno 1 - 10
of 100 382
pro vyhledávání: '"So Jimmy"'
Autor:
Mortensen Michael Bau, Casella Francesco, Düzgün Özgül, Glehen Olivier, Hewett Peter, Hübner Martin, Jørgensen Magnus Skov, Königsrainer Alfred, Marin Miguel, Pocard Marc, Rezniczek Günther, So Jimmy, Fristrup Claus Wilki
Publikováno v:
Pleura and Peritoneum, Vol 8, Iss 4, Pp 141-146 (2023)
To monitor the results of PIPAC directed therapy based on data from the International Society for the Study of the Pleura and Peritoneum (ISSPP) PIPAC database.
Externí odkaz:
https://doaj.org/article/5f629c73bbb440afa1f2f0680ff09b5d
Autor:
Sgarbura Olivia, Eveno Clarisse, Alyami Mohammad, Bakrin Naoual, Guiral Delia Cortes, Ceelen Wim, Delgadillo Xavier, Dellinger Thanh, Di Giorgio Andrea, Kefleyesus Amaniel, Khomiakov Vladimir, Mortensen Michael Bau, Murphy Jamie, Pocard Marc, Reymond Marc, Robella Manuela, Rovers Koen P., So Jimmy, Somashekhar S.P., Tempfer Clemens, Van der Speeten Kurt, Villeneuve Laurent, Yong Wei Peng, Hübner Martin
Publikováno v:
Pleura and Peritoneum, Vol 7, Iss 1, Pp 1-7 (2022)
Safe implementation and thorough evaluation of new treatments require prospective data monitoring and standardization of treatments. Pressurized intraperitoneal aerosol chemotherapy (PIPAC) is a promising alternative for the treatment of patients wit
Externí odkaz:
https://doaj.org/article/b41c28edb3694892b82a637b777e2390
Autor:
Pradeep, Ronak, Lee, Daniel, Mousavi, Ali, Pound, Jeff, Sang, Yisi, Lin, Jimmy, Ilyas, Ihab, Potdar, Saloni, Arefiyan, Mostafa, Li, Yunyao
The rapid advancement of Large Language Models (LLMs) and conversational assistants necessitates dynamic, scalable, and configurable conversational datasets for training and evaluation. These datasets must accommodate diverse user interaction modes,
Externí odkaz:
http://arxiv.org/abs/2408.05948
Adaptive experimentation can significantly improve statistical power, but standard algorithms overlook important practical issues including batched and delayed feedback, personalization, non-stationarity, multiple objectives, and constraints. To addr
Externí odkaz:
http://arxiv.org/abs/2408.04570
Innovations across science and industry are evaluated using randomized trials (a.k.a. A/B tests). While simple and robust, such static designs are inefficient or infeasible for testing many hypotheses. Adaptive designs can greatly improve statistical
Externí odkaz:
http://arxiv.org/abs/2408.04531
In this study, we couple a population dynamics model with a model for optimal foraging to study the interdependence between individual-level cost-benefits and population-scale dynamics. Specifically, we study the logistic growth model, which provides
Externí odkaz:
http://arxiv.org/abs/2408.02552
Autor:
Yang, Jheng-Hong, Lin, Jimmy
Vision--Language Models (VLMs) have demonstrated success across diverse applications, yet their potential to assist in relevance judgments remains uncertain. This paper assesses the relevance estimation capabilities of VLMs, including CLIP, LLaVA, an
Externí odkaz:
http://arxiv.org/abs/2408.01363
Autor:
Jaipersaud, Brandon, Zhang, Paul, Ba, Jimmy, Petersen, Andrew, Zhang, Lisa, Zhang, Michael R.
Publikováno v:
In: Artificial Intelligence in Education. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham
We propose and evaluate a question-answering system that uses decomposed prompting to classify and answer student questions on a course discussion board. Our system uses a large language model (LLM) to classify questions into one of four types: conce
Externí odkaz:
http://arxiv.org/abs/2407.21170
Conventional nonlinear RNNs are not naturally parallelizable across the sequence length, whereas transformers and linear RNNs are. Lim et al. [2024] therefore tackle parallelized evaluation of nonlinear RNNs by posing it as a fixed point problem, sol
Externí odkaz:
http://arxiv.org/abs/2407.19115
"Honeywords" have emerged as a promising defense mechanism for detecting data breaches and foiling offline dictionary attacks (ODA) by deceiving attackers with false passwords. In this paper, we propose PassFilter, a novel deep learning (DL) based at
Externí odkaz:
http://arxiv.org/abs/2407.16964