Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Prabhu, Ameya"'
Autor:
Roth, Karsten, Udandarao, Vishaal, Dziadzio, Sebastian, Prabhu, Ameya, Cherti, Mehdi, Vinyals, Oriol, Hénaff, Olivier, Albanie, Samuel, Bethge, Matthias, Akata, Zeynep
Multimodal foundation models serve numerous applications at the intersection of vision and language. Still, despite being pretrained on extensive data, they become outdated over time. To keep models updated, research into continual pretraining mainly
Externí odkaz:
http://arxiv.org/abs/2408.14471
Autor:
Sainz, Oscar, García-Ferrero, Iker, Jacovi, Alon, Campos, Jon Ander, Elazar, Yanai, Agirre, Eneko, Goldberg, Yoav, Chen, Wei-Lin, Chim, Jenny, Choshen, Leshem, D'Amico-Wong, Luca, Dell, Melissa, Fan, Run-Ze, Golchin, Shahriar, Li, Yucheng, Liu, Pengfei, Pahwa, Bhavish, Prabhu, Ameya, Sharma, Suryansh, Silcock, Emily, Solonko, Kateryna, Stap, David, Surdeanu, Mihai, Tseng, Yu-Min, Udandarao, Vishaal, Wang, Zengzhi, Xu, Ruijie, Yang, Jinglin
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora u
Externí odkaz:
http://arxiv.org/abs/2407.21530
Autor:
Press, Ori, Hochlehnert, Andreas, Prabhu, Ameya, Udandarao, Vishaal, Press, Ofir, Bethge, Matthias
Thousands of new scientific papers are published each month. Such information overload complicates researcher efforts to stay current with the state-of-the-art as well as to verify and correctly attribute claims. We pose the following research questi
Externí odkaz:
http://arxiv.org/abs/2407.12861
Autor:
Warnecke, Jörn, Korpi-Lagg, Maarit J., Rheinhard, Matthias, Viviani, Mariangela, Prabhu, Ameya
It has been recently shown that a small-scale dynamo (SSD) instability could be possible in solar-like low magnetic Prandtl number Pm plasmas. It has been proposed that the presence of SSD can potentially have a significant impact on the dynamics of
Externí odkaz:
http://arxiv.org/abs/2406.08967
Autor:
Gui, Zhongrui, Sun, Shuyang, Li, Runjia, Yuan, Jianhao, An, Zhaochong, Roth, Karsten, Prabhu, Ameya, Torr, Philip
Continual segmentation has not yet tackled the challenge of improving open-vocabulary segmentation models with training data for accurate segmentation across large, continually expanding vocabularies. We discover that traditional continual training r
Externí odkaz:
http://arxiv.org/abs/2404.09447
Proving geometric theorems constitutes a hallmark of visual reasoning combining both intuitive and logical skills. Therefore, automated theorem proving of Olympiad-level geometry problems is considered a notable milestone in human-level automated rea
Externí odkaz:
http://arxiv.org/abs/2404.06405
Autor:
Udandarao, Vishaal, Prabhu, Ameya, Ghosh, Adhiraj, Sharma, Yash, Torr, Philip H. S., Bibi, Adel, Albanie, Samuel, Bethge, Matthias
Web-crawled pretraining datasets underlie the impressive "zero-shot" evaluation performance of multimodal models, such as CLIP for classification/retrieval and Stable-Diffusion for image generation. However, it is unclear how meaningful the notion of
Externí odkaz:
http://arxiv.org/abs/2404.04125
Autor:
Prabhu, Ameya, Udandarao, Vishaal, Torr, Philip, Bethge, Matthias, Bibi, Adel, Albanie, Samuel
Standardized benchmarks drive progress in machine learning. However, with repeated testing, the risk of overfitting grows as algorithms over-exploit benchmark idiosyncrasies. In our work, we seek to mitigate this challenge by compiling ever-expanding
Externí odkaz:
http://arxiv.org/abs/2402.19472
Machine Learning models increasingly face data integrity challenges due to the use of large-scale training datasets drawn from the Internet. We study what model developers can do if they detect that some data was manipulated or incorrect. Such manipu
Externí odkaz:
http://arxiv.org/abs/2402.14015
Autor:
Prabhu, Ameya, Sinha, Shiven, Kumaraguru, Ponnurangam, Torr, Philip H. S., Sener, Ozan, Dokania, Puneet K.
Continual learning has primarily focused on the issue of catastrophic forgetting and the associated stability-plasticity tradeoffs. However, little attention has been paid to the efficacy of continually learned representations, as representations are
Externí odkaz:
http://arxiv.org/abs/2402.08823