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pro vyhledávání: '"Chang, Nadine"'
In recent years, the data collected for artificial intelligence has grown to an unmanageable amount. Particularly within industrial applications, such as autonomous vehicles, model training computation budgets are being exceeded while model performan
Externí odkaz:
http://arxiv.org/abs/2409.13860
Autor:
Wang, Shihao, Yu, Zhiding, Jiang, Xiaohui, Lan, Shiyi, Shi, Min, Chang, Nadine, Kautz, Jan, Li, Ying, Alvarez, Jose M.
The advances in multimodal large language models (MLLMs) have led to growing interests in LLM-based autonomous driving agents to leverage their strong reasoning capabilities. However, capitalizing on MLLMs' strong reasoning capabilities for improved
Externí odkaz:
http://arxiv.org/abs/2405.01533
Large-scale datasets are essential to modern day deep learning. Advocates argue that understanding these methods requires dataset transparency (e.g. "dataset curation, motivation, composition, collection process, etc..."). However, almost no one has
Externí odkaz:
http://arxiv.org/abs/2306.14035
Autor:
Chang, Nadine, Yu, Zhiding, Wang, Yu-Xiong, Anandkumar, Anima, Fidler, Sanja, Alvarez, Jose M.
Training on datasets with long-tailed distributions has been challenging for major recognition tasks such as classification and detection. To deal with this challenge, image resampling is typically introduced as a simple but effective approach. Howev
Externí odkaz:
http://arxiv.org/abs/2104.05702
Autor:
Chang, Nadine, Koushik, Jayanth, Singh, Aarti, Hebert, Martial, Wang, Yu-Xiong, Tarr, Michael J.
Methods in long-tail learning focus on improving performance for data-poor (rare) classes; however, performance for such classes remains much lower than performance for more data-rich (frequent) classes. Analyzing the predictions of long-tail methods
Externí odkaz:
http://arxiv.org/abs/2008.07073
Vision science, particularly machine vision, has been revolutionized by introducing large-scale image datasets and statistical learning approaches. Yet, human neuroimaging studies of visual perception still rely on small numbers of images (around 100
Externí odkaz:
http://arxiv.org/abs/1809.01281
Autor:
Chang, Nadine
Computer vision models have proven to be tremendously capable of recognizing and detecting several real-world objects: cars, people, pets. However, the best performing classes have abundant examples in large-scale datasets today and obscured or small
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d264d51e680c4146233b1483c263763
Successful Strategy to Reduce the Use of Seclusion and Restraints on the Inpatient Psychiatric Unit.
Publikováno v:
Psychiatric Services; Dec2024, Vol. 75 Issue 12, p1292-1292, 1p
Deep learning classification models typically train poorly on classes with small numbers of examples. Motivated by the human ability to solve this task, models have been developed that transfer knowledge from classes with many examples to learn class
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b6dcddea8035172005d13d0017015abb
http://arxiv.org/abs/2008.07073
http://arxiv.org/abs/2008.07073
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