Zobrazeno 1 - 10
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pro vyhledávání: '"Dubrawski, A."'
Recent advancements in machine learning have accelerated its widespread adoption across various real-world applications. However, in safety-critical domains, the deployment of machine learning models is riddled with challenges due to their complexity
Externí odkaz:
http://arxiv.org/abs/2408.00986
Efficient intravascular access in trauma and critical care significantly impacts patient outcomes. However, the availability of skilled medical personnel in austere environments is often limited. Autonomous robotic ultrasound systems can aid in needl
Externí odkaz:
http://arxiv.org/abs/2407.21273
Publikováno v:
International Conference on Machine Learning, 2024
In supervised learning, understanding an input's proximity to the training data can help a model decide whether it has sufficient evidence for reaching a reliable prediction. While powerful probabilistic models such as Gaussian Processes naturally ha
Externí odkaz:
http://arxiv.org/abs/2406.10775
In the real world, data is often noisy, affecting not only the quality of features but also the accuracy of labels. Current research on mitigating label errors stems primarily from advances in deep learning, and a gap exists in exploring interpretabl
Externí odkaz:
http://arxiv.org/abs/2405.17672
We introduce MOMENT, a family of open-source foundation models for general-purpose time series analysis. Pre-training large models on time series data is challenging due to (1) the absence of a large and cohesive public time series repository, and (2
Externí odkaz:
http://arxiv.org/abs/2402.03885
Signal quality assessment (SQA) is required for monitoring the reliability of data acquisition systems, especially in AI-driven Predictive Maintenance (PMx) application contexts. SQA is vital for addressing "silent failures" of data acquisition hardw
Externí odkaz:
http://arxiv.org/abs/2402.00803
Autor:
Goel, Raghavv, Morales, Cecilia, Singh, Manpreet, Dubrawski, Artur, Galeotti, John, Choset, Howie
Segmenting a moving needle in ultrasound images is challenging due to the presence of artifacts, noise, and needle occlusion. This task becomes even more demanding in scenarios where data availability is limited. In this paper, we present a novel app
Externí odkaz:
http://arxiv.org/abs/2312.01239
Forecasting healthcare time series is crucial for early detection of adverse outcomes and for patient monitoring. Forecasting, however, can be difficult in practice due to noisy and intermittent data. The challenges are often exacerbated by change po
Externí odkaz:
http://arxiv.org/abs/2309.13135
Autor:
Goswami, Mononito, Sanil, Vedant, Choudhry, Arjun, Srinivasan, Arvind, Udompanyawit, Chalisa, Dubrawski, Artur
Machine learning (ML) models are only as good as the data they are trained on. But recent studies have found datasets widely used to train and evaluate ML models, e.g. ImageNet, to have pervasive labeling errors. Erroneous labels on the train set hur
Externí odkaz:
http://arxiv.org/abs/2306.09467
Autor:
Olivares, Kin G., Luo, David, Challu, Cristian, La Vattiata, Stefania, Mergenthaler, Max, Dubrawski, Artur
Large collections of time series data are often organized into hierarchies with different levels of aggregation; examples include product and geographical groupings. Probabilistic coherent forecasting is tasked to produce forecasts consistent across
Externí odkaz:
http://arxiv.org/abs/2305.07089