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pro vyhledávání: '"Chen, Mei"'
The current state of machine learning scholarship in Timeseries Anomaly Detection (TAD) is plagued by the persistent use of flawed evaluation metrics, inconsistent benchmarking practices, and a lack of proper justification for the choices made in nov
Externí odkaz:
http://arxiv.org/abs/2405.02678
Temporal Action Localization (TAL) involves localizing and classifying action snippets in an untrimmed video. The emergence of large video foundation models has led RGB-only video backbones to outperform previous methods needing both RGB and optical
Externí odkaz:
http://arxiv.org/abs/2404.01282
Machine learning holds tremendous promise for transforming the fundamental practice of scientific discovery by virtue of its data-driven nature. With the ever-increasing stream of research data collection, it would be appealing to autonomously explor
Externí odkaz:
http://arxiv.org/abs/2403.01053
Autor:
Singh, Pranav, Chukkapalli, Raviteja, Chaudhari, Shravan, Chen, Luoyao, Chen, Mei, Pan, Jinqian, Smuda, Craig, Cirrone, Jacopo
Publikováno v:
Singh, P., Chukkapalli, R., Chaudhari, S. et al. Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification. Sci Rep 14, 10820 (2024)
Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time
Externí odkaz:
http://arxiv.org/abs/2311.10319
Writing strong arguments can be challenging for learners. It requires to select and arrange multiple argumentative discourse units (ADUs) in a logical and coherent way as well as to decide which ADUs to leave implicit, so called enthymemes. However,
Externí odkaz:
http://arxiv.org/abs/2310.18098
Autor:
Magooda, Ahmed, Helyar, Alec, Jackson, Kyle, Sullivan, David, Atalla, Chad, Sheng, Emily, Vann, Dan, Edgar, Richard, Palangi, Hamid, Lutz, Roman, Kong, Hongliang, Yun, Vincent, Kamal, Eslam, Zarfati, Federico, Wallach, Hanna, Bird, Sarah, Chen, Mei
We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services. Our framework for automatically measuring harms from LLMs builds on existing technical and soc
Externí odkaz:
http://arxiv.org/abs/2310.17750
Autor:
Singh, Pranav, Chen, Luoyao, Chen, Mei, Pan, Jinqian, Chukkapalli, Raviteja, Chaudhari, Shravan, Cirrone, Jacopo
The task of medical image segmentation presents unique challenges, necessitating both localized and holistic semantic understanding to accurately delineate areas of interest, such as critical tissues or aberrant features. This complexity is heightene
Externí odkaz:
http://arxiv.org/abs/2308.10488
The success of automated medical image analysis depends on large-scale and expert-annotated training sets. Unsupervised domain adaptation (UDA) has been raised as a promising approach to alleviate the burden of labeled data collection. However, they
Externí odkaz:
http://arxiv.org/abs/2307.14709
Autor:
Clarke, Christopher, Hall, Matthew, Mittal, Gaurav, Yu, Ye, Sajeev, Sandra, Mars, Jason, Chen, Mei
Classic approaches to content moderation typically apply a rule-based heuristic approach to flag content. While rules are easily customizable and intuitive for humans to interpret, they are inherently fragile and lack the flexibility or robustness ne
Externí odkaz:
http://arxiv.org/abs/2307.12935
There is a rapidly growing need for multimodal content moderation (CM) as more and more content on social media is multimodal in nature. Existing unimodal CM systems may fail to catch harmful content that crosses modalities (e.g., memes or videos), w
Externí odkaz:
http://arxiv.org/abs/2305.10547