Zobrazeno 1 - 10
of 1 543
pro vyhledávání: '"Katabi A"'
Autor:
Zha, Kaiwen, Yu, Lijun, Fathi, Alireza, Ross, David A., Schmid, Cordelia, Katabi, Dina, Gu, Xiuye
Image tokenization, the process of transforming raw image pixels into a compact low-dimensional latent representation, has proven crucial for scalable and efficient image generation. However, mainstream image tokenization methods generally have limit
Externí odkaz:
http://arxiv.org/abs/2412.05796
Autor:
Shenar, T., Bodensteiner, J., Sana, H., Crowther, P. A., Lennon, D. J., Abdul-Masih, M., Almeida, L. A., Backs, F., Berlanas, S. R., Bernini-Peron, M., Bestenlehner, J. M., Bowman, D. M., Bronner, V. A., Britavskiy, N., de Koter, A., de Mink, S. E., Deshmukh, K., Evans, C. J., Fabry, M., Gieles, M., Gilkis, A., González-Torà, G., Gräfener, G., Götberg, Y., Hawcroft, C., Hénault-Brunet, V., Herrero, A., Holgado, G., Janssens, S., Johnston, C., Josiek, J., Justham, S., Kalari, V. M., Katabi, Z. Z., Keszthelyi, Z., Klencki, J., Kubát, J., Kubátová, B., Langer, N., Lefever, R. R., Ludwig, B., Mackey, J., Mahy, L., Apellániz, J. Maíz, Mandel, I., Maravelias, G., Marchant, P., Menon, A., Najarro, F., Oskinova, L. M., Ovadia, A. J. G. O'Grady R., Patrick, L. R., Pauli, D., Pawlak, M., Ramachandran, V., Renzo, M., Rocha, D. F., Sander, A. A. C., Sayada, T., Schneider, F. R. N., Schootemeijer, A., Schösser, E. C., Schürmann, C., Sen, K., Shahaf, S., Simón-Díaz, S., Stoop, M., van Loon, J. Th., Toonen, S., Tramper, F., Valli, R., van Son, L. A. C., Vigna-Gómez, A., Villaseñor, J. I., Vink, J. S., Wang, C., Willcox, R.
Publikováno v:
A&A 690, A289 (2024)
Surveys in the Milky Way and Large Magellanic Cloud revealed that the majority of massive stars will interact with companions during their lives. However, knowledge of the binary properties of massive stars at low metallicity, which approaches the co
Externí odkaz:
http://arxiv.org/abs/2407.14593
Autor:
He, Hao, Li, Chao, Ganglberger, Wolfgang, Gallagher, Kaileigh, Hristov, Rumen, Ouroutzoglou, Michail, Sun, Haoqi, Sun, Jimeng, Westover, Brandon, Katabi, Dina
The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow
Externí odkaz:
http://arxiv.org/abs/2405.11739
We introduce SynCLR, a novel approach for learning visual representations exclusively from synthetic images and synthetic captions, without any real data. We synthesize a large dataset of image captions using LLMs, then use an off-the-shelf text-to-i
Externí odkaz:
http://arxiv.org/abs/2312.17742
As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Prior research has established AI's capacity to infer demographic data from
Externí odkaz:
http://arxiv.org/abs/2312.10083
Recent significant advances in text-to-image models unlock the possibility of training vision systems using synthetic images, potentially overcoming the difficulty of collecting curated data at scale. It is unclear, however, how these models behave a
Externí odkaz:
http://arxiv.org/abs/2312.04567
Unconditional generation -- the problem of modeling data distribution without relying on human-annotated labels -- is a long-standing and fundamental challenge in generative models, creating a potential of learning from large-scale unlabeled data. In
Externí odkaz:
http://arxiv.org/abs/2312.03701
Autor:
Li, Tianhong, Bhardwaj, Sangnie, Tian, Yonglong, Zhang, Han, Barber, Jarred, Katabi, Dina, Lajoie, Guillaume, Chang, Huiwen, Krishnan, Dilip
Current vision-language generative models rely on expansive corpora of paired image-text data to attain optimal performance and generalization capabilities. However, automatically collecting such data (e.g. via large-scale web scraping) leads to low
Externí odkaz:
http://arxiv.org/abs/2310.03734
Autor:
Amit Khanna, Jamie Adams, Chrystalina Antoniades, Bastiaan R. Bloem, Camille Carroll, Jesse Cedarbaum, Joshua Cosman, David T. Dexter, Marissa F. Dockendorf, Jeremy Edgerton, Laura Gaetano, Erkuden Goikoetxea, Derek Hill, Fay Horak, Elena S. Izmailova, Tairmae Kangarloo, Dina Katabi, Catherine Kopil, Michael Lindemann, Jennifer Mammen, Kenneth Marek, Kevin McFarthing, Anat Mirelman, Martijn Muller, Gennaro Pagano, M. Judith Peterschmitt, Jie Ren, Lynn Rochester, Sakshi Sardar, Andrew Siderowf, Tanya Simuni, Diane Stephenson, Christine Swanson-Fischer, John A. Wagner, Graham B. Jones
Publikováno v:
npj Parkinson's Disease, Vol 10, Iss 1, Pp 1-9 (2024)
Abstract Parkinson’s Disease is a progressive neurodegenerative disorder afflicting almost 12 million people. Increased understanding of its complex and heterogenous disease pathology, etiology and symptom manifestations has resulted in the need to
Externí odkaz:
https://doaj.org/article/618ab6ef31f24f8996de46c4c52bb9ee
We propose a novel unsupervised object localization method that allows us to explain the predictions of the model by utilizing self-supervised pre-trained models without additional finetuning. Existing unsupervised and self-supervised object localiza
Externí odkaz:
http://arxiv.org/abs/2309.04172