no code implementations • NAACL (SIGMORPHON) 2022 • Akhilesh Kakolu Ramarao, Yulia Zinova, Kevin Tang, Ruben van de Vijver
This paper presents the submission by the HeiMorph team to the SIGMORPHON 2022 task 2 of Morphological Acquisition Trajectories.
no code implementations • LTEDI (ACL) 2022 • Harrison Santiago, Joshua Martin, Sarah Moeller, Kevin Tang
To overcome the scarcity, we employ a combination of rule-based filters and data augmentation that generate a corpus balanced between habitual and non-habitual instances.
no code implementations • 3 Apr 2024 • Jeffy Yu, Maximilian Huber, Kevin Tang
This paper investigates the ethical implications of aligning Large Language Models (LLMs) with financial optimization, through the case study of GreedLlama, a model fine-tuned to prioritize economically beneficial outcomes.
no code implementations • 4 Dec 2023 • YuChao Gu, Yipin Zhou, Bichen Wu, Licheng Yu, Jia-Wei Liu, Rui Zhao, Jay Zhangjie Wu, David Junhao Zhang, Mike Zheng Shou, Kevin Tang
In contrast to previous methods that rely on dense correspondences, we introduce the VideoSwap framework that exploits semantic point correspondences, inspired by our observation that only a small number of semantic points are necessary to align the subject's motion trajectory and modify its shape.
no code implementations • 26 Apr 2022 • Harrison Santiago, Joshua Martin, Sarah Moeller, Kevin Tang
To overcome the scarcity, we employ a combination of rule-based filters and data augmentation that generate a corpus balanced between habitual and non-habitual instances.
1 code implementation • 12 Oct 2020 • Keto D. Zhang, WeiKang Zheng, Thomas de Jaeger, Benjamin E. Stahl, Thomas G. Brink, Xuhui Han, Daniel Kasen, Ken J. Shen, Kevin Tang, Alexei V. Filippenko
In the two cases we consider, 35\% of SNe Ia are considered to be asymmetric in Case 1, and all SNe Ia are asymmetric in Case 2.
High Energy Astrophysical Phenomena Cosmology and Nongalactic Astrophysics
no code implementations • 29 May 2020 • Kevin Tang, Jason A. Shaw
Extending this interpretation, these results suggest that predictability is closely linked to prosodic prominence, and that the lexical representation of a word includes phonetic details associated with its average prosodic prominence in discourse.
no code implementations • ICLR 2019 • Amir Feghahati, Christian R. Shelton, Michael J. Pazzani, Kevin Tang
The second question asks, "Why did you not choose answer B over A?"
1 code implementation • CVPR 2017 • Linjie Yang, Kevin Tang, Jianchao Yang, Li-Jia Li
The goal is to densely detect visual concepts (e. g., objects, object parts, and interactions between them) from images, labeling each with a short descriptive phrase.
no code implementations • ICCV 2015 • Kevin Tang, Manohar Paluri, Li Fei-Fei, Rob Fergus, Lubomir Bourdev
With the widespread availability of cellphones and cameras that have GPS capabilities, it is common for images being uploaded to the Internet today to have GPS coordinates associated with them.
no code implementations • ICCV 2015 • Vignesh Ramanathan, Kevin Tang, Greg Mori, Li Fei-Fei
In this paper, we propose to learn temporal embeddings of video frames for complex video analysis.
no code implementations • CVPR 2014 • Kevin Tang, Armand Joulin, Li-Jia Li, Li Fei-Fei
In this paper, we tackle the problem of co-localization in real-world images.
no code implementations • CVPR 2013 • Kevin Tang, Rahul Sukthankar, Jay Yagnik, Li Fei-Fei
Second, we ensure that CRANE is robust to label noise, both in terms of tagged videos that fail to contain the concept as well as occasional negative videos that do.
no code implementations • NeurIPS 2012 • Kevin Tang, Vignesh Ramanathan, Li Fei-Fei, Daphne Koller
In this paper, we tackle the problem of adapting object detectors learned from images to work well on videos.