Zobrazeno 1 - 10
of 1 104
pro vyhledávání: '"68T01"'
Autor:
Salehi, Pegah, Sheshkal, Sajad Amouei, Thambawita, Vajira, Gautam, Sushant, Sabet, Saeed S., Johansen, Dag, Riegler, Michael A., Halvorsen, Pål
This paper examines the integration of real-time talking-head generation for interviewer training, focusing on overcoming challenges in Audio Feature Extraction (AFE), which often introduces latency and limits responsiveness in real-time applications
Externí odkaz:
http://arxiv.org/abs/2411.13209
Federated learning (FL) is an emerging paradigm for training machine learning models across distributed clients. Traditionally, in FL settings, a central server assigns training efforts (or strategies) to clients. However, from a market-oriented pers
Externí odkaz:
http://arxiv.org/abs/2411.11793
Publikováno v:
DIGITAL HEALTH. 2024;10
Objectives: Our research adopts computational techniques to analyze disease outbreaks weekly over a large geographic area while maintaining local-level analysis by incorporating relevant high-spatial resolution cultural and environmental datasets. Th
Externí odkaz:
http://arxiv.org/abs/2411.06436
Autor:
Prabhune, Sonal, Berndt, Donald J.
Knowing that the generative capabilities of large language models (LLM) are sometimes hampered by tendencies to hallucinate or create non-factual responses, researchers have increasingly focused on methods to ground generated outputs in factual data.
Externí odkaz:
http://arxiv.org/abs/2411.11895
Autor:
Chen, Weijie, McMillan, Alan
This paper introduces an efficient sub-model ensemble framework aimed at enhancing the interpretability of medical deep learning models, thus increasing their clinical applicability. By generating uncertainty maps, this framework enables end-users to
Externí odkaz:
http://arxiv.org/abs/2411.05324
Autor:
Hossain, Soaad, Rasalingam, James, Waheed, Arhum, Awil, Fatah, Kandiah, Rachel, Ahmed, Syed Ishtiaque
With the growing interest in using AI and machine learning (ML) in medicine, there is an increasing number of literature covering the application and ethics of using AI and ML in areas of medicine such as clinical psychiatry. The problem is that ther
Externí odkaz:
http://arxiv.org/abs/2411.05856
This paper addresses the challenges of predicting bioprocess performance, particularly in monoclonal antibody (mAb) production, where conventional statistical methods often fall short due to time-series data's complexity and high dimensionality. We p
Externí odkaz:
http://arxiv.org/abs/2411.01404
In many industrial applications, it is common that the graph embeddings generated from training GNNs are used in an ensemble model where the embeddings are combined with other tabular features (e.g., original node or edge features) in a downstream ML
Externí odkaz:
http://arxiv.org/abs/2411.00287
Image labeling is a critical bottleneck in the development of computer vision technologies, often constraining the potential of machine learning models due to the time-intensive nature of manual annotations. This work introduces a novel approach that
Externí odkaz:
http://arxiv.org/abs/2410.24116
Constrained reinforcement learning has achieved promising progress in safety-critical fields where both rewards and constraints are considered. However, constrained reinforcement learning methods face challenges in striking the right balance between
Externí odkaz:
http://arxiv.org/abs/2410.20786