Zobrazeno 1 - 10
of 1 318
pro vyhledávání: '"Fox, Geoffrey"'
Deep learning has proven very promising for interpreting MRI in brain tumor diagnosis. However, deep learning models suffer from a scarcity of brain MRI datasets for effective training. Self-supervised learning (SSL) models provide data-efficient and
Externí odkaz:
http://arxiv.org/abs/2411.12874
Autor:
Toledo-Marin, J. Quetzalcoatl, Gonzalez, Sebastian, Jia, Hao, Lu, Ian, Sogutlu, Deniz, Abhishek, Abhishek, Gay, Colin, Paquet, Eric, Melko, Roger, Fox, Geoffrey C., Swiatlowski, Maximilian, Fedorko, Wojciech
Particle collisions at accelerators such as the Large Hadron Collider, recorded and analyzed by experiments such as ATLAS and CMS, enable exquisite measurements of the Standard Model and searches for new phenomena. Simulations of collision events at
Externí odkaz:
http://arxiv.org/abs/2410.22870
This research is part of a systematic study of scientific time series. In the last three years, hundreds of papers and over fifty new deep-learning models have been described for time series models. These mainly focus on the key aspect of time depend
Externí odkaz:
http://arxiv.org/abs/2410.15218
Redshift prediction is a fundamental task in astronomy, essential for understanding the expansion of the universe and determining the distances of astronomical objects. Accurate redshift prediction plays a crucial role in advancing our knowledge of t
Externí odkaz:
http://arxiv.org/abs/2409.01825
Autor:
Sun, Xiaoxiang, Fox, Geoffrey
3D object detection is critical for autonomous driving, leveraging deep learning techniques to interpret LiDAR data. The PointPillars architecture is a prominent model in this field, distinguished by its efficient use of LiDAR data. This study provid
Externí odkaz:
http://arxiv.org/abs/2409.00673
Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities remains a crucial and enduring objective aimed at reducing casualties. This multifaceted challenge has recently gained attention within the deep lear
Externí odkaz:
http://arxiv.org/abs/2408.11990
Autor:
Bajracharya, Pradeep, Toledo-Marín, Javier Quetzalcóatl, Fox, Geoffrey, Jha, Shantenu, Wang, Linwei
High-performance scientific simulations, important for comprehension of complex systems, encounter computational challenges especially when exploring extensive parameter spaces. There has been an increasing interest in developing deep neural networks
Externí odkaz:
http://arxiv.org/abs/2407.07674
Earthquake nowcasting has been proposed as a means of tracking the change in large earthquake potential in a seismically active area. The method was developed using observable seismic data, in which probabilities of future large earthquakes can be co
Externí odkaz:
http://arxiv.org/abs/2406.09471
The pursuit of understanding fundamental particle interactions has reached unparalleled precision levels. Particle physics detectors play a crucial role in generating low-level object signatures that encode collision physics. However, simulating thes
Externí odkaz:
http://arxiv.org/abs/2406.12898
Autor:
Sarker, Arup Kumar, Alsaadi, Aymen, Perera, Niranda, Staylor, Mills, von Laszewski, Gregor, Turilli, Matteo, Kilic, Ozgur Ozan, Titov, Mikhail, Merzky, Andre, Jha, Shantenu, Fox, Geoffrey
Managing and preparing complex data for deep learning, a prevalent approach in large-scale data science can be challenging. Data transfer for model training also presents difficulties, impacting scientific fields like genomics, climate modeling, and
Externí odkaz:
http://arxiv.org/abs/2403.15721