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pro vyhledávání: '"Horwitz, A"'
The increasing availability of public models begs the question: can we train neural networks that use other networks as input? Such models allow us to study different aspects of a given neural network, for example, determining the categories in a mod
Externí odkaz:
http://arxiv.org/abs/2410.13569
Weight space learning aims to extract information about a neural network, such as its training dataset or generalization error. Recent approaches learn directly from model weights, but this presents many challenges as weights are high-dimensional and
Externí odkaz:
http://arxiv.org/abs/2410.10811
Model inversion and membership inference attacks aim to reconstruct and verify the data which a model was trained on. However, they are not guaranteed to find all training samples as they do not know the size of the training set. In this paper, we in
Externí odkaz:
http://arxiv.org/abs/2406.19395
Recent improvements in generative AI made synthesizing fake images easy; as they can be used to cause harm, it is crucial to develop accurate techniques to identify them. This paper introduces "Locally Aware Deepfake Detection Algorithm" (LaDeDa), th
Externí odkaz:
http://arxiv.org/abs/2406.09398
The rapid growth of neural network models shared on the internet has made model weights an important data modality. However, this information is underutilized as the weights are uninterpretable, and publicly available models are disorganized. Inspire
Externí odkaz:
http://arxiv.org/abs/2405.18432
Dataset distillation aims to compress a dataset into a much smaller one so that a model trained on the distilled dataset achieves high accuracy. Current methods frame this as maximizing the distilled classification accuracy for a budget of K distille
Externí odkaz:
http://arxiv.org/abs/2403.12040
The dominant paradigm in generative modeling consists of two steps: i) pre-training on a large-scale but unsafe dataset, ii) aligning the pre-trained model with human values via fine-tuning. This practice is considered safe, as no current method can
Externí odkaz:
http://arxiv.org/abs/2402.10208
Autor:
Bajcsy, Peter, Bhattiprolu, Sreenivas, Boerner, Katy, Cimini, Beth A, Collinson, Lucy, Ellenberg, Jan, Fiolka, Reto, Giger, Maryellen, Goscinski, Wojtek, Hartley, Matthew, Hotaling, Nathan, Horwitz, Rick, Jug, Florian, Kreshuk, Anna, Lundberg, Emma, Mathur, Aastha, Narayan, Kedar, Onami, Shuichi, Plant, Anne L., Prior, Fred, Swedlow, Jason, Taylor, Adam, Keppler, Antje
Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well
Externí odkaz:
http://arxiv.org/abs/2401.13023
Autor:
Ryan L. Boyd, Nicholas R. Morrison, Sarah D. Horwitz, Rachel Maciag, Emma Travers-Hill, Youngsuk Kim
Publikováno v:
Cogent Mental Health, Vol 3, Iss 1, Pp 1-24 (2024)
Psychological researchers are increasingly striving to enhance methodological integrity, including in qualitative methods. Although computerized text analysis tools originally emerged as a potential replacement for manual coding approaches, recent st
Externí odkaz:
https://doaj.org/article/7b3804617f6d41ca838d623c07eefb70
Autor:
Jennifer K. Lue, Efrat Luttwak, Alfredo Rivas-Delgado, Helen Irawan, Alexander Boardman, Philip C. Caron, Kevin David, Zachary Epstein-Peterson, Lorenzo Falchi, Paola Ghione, Paul Hamlin, Steven M. Horwitz, Andrew M. Intlekofer, William Johnson, Anita Kumar, Alison Moskowitz, Ariela Noy, M. Lia Palomba, Ralphael Steiner, Robert Stuver, Pallawi Torka, Santosha Vardhana, Andrew D. Zelenetz, Heiko Schoder, Brandon Imber, Joachim Yahalom, Yanming Zhang, Pallavi Galera, Ahmet Dogan, Umut Aypar, Gilles Salles
Publikováno v:
Blood Cancer Journal, Vol 14, Iss 1, Pp 1-5 (2024)
Externí odkaz:
https://doaj.org/article/1eae0d2949b448a4bc8746adc272a82a