Zobrazeno 1 - 10
of 13 284
pro vyhledávání: '"A. Dziedzic"'
Autor:
Kowalczuk, Antoni, Dubiński, Jan, Ghomi, Atiyeh Ashari, Sui, Yi, Stein, George, Wu, Jiapeng, Cresswell, Jesse C., Boenisch, Franziska, Dziedzic, Adam
Large-scale vision models have become integral in many applications due to their unprecedented performance and versatility across downstream tasks. However, the robustness of these foundation models has primarily been explored for a single task, name
Externí odkaz:
http://arxiv.org/abs/2407.12588
Machine learning (ML) models have been shown to leak private information from their training datasets. Differential Privacy (DP), typically implemented through the differential private stochastic gradient descent algorithm (DP-SGD), has become the st
Externí odkaz:
http://arxiv.org/abs/2406.08039
The proliferation of large language models (LLMs) in the real world has come with a rise in copyright cases against companies for training their models on unlicensed data from the internet. Recent works have presented methods to identify if individua
Externí odkaz:
http://arxiv.org/abs/2406.06443
Autor:
Wang, Yihan, Lu, Yiwei, Zhang, Guojun, Boenisch, Franziska, Dziedzic, Adam, Yu, Yaoliang, Gao, Xiao-Shan
Machine unlearning provides viable solutions to revoke the effect of certain training data on pre-trained model parameters. Existing approaches provide unlearning recipes for classification and generative models. However, a category of important mach
Externí odkaz:
http://arxiv.org/abs/2406.03603
Autor:
Hintersdorf, Dominik, Struppek, Lukas, Kersting, Kristian, Dziedzic, Adam, Boenisch, Franziska
Diffusion models (DMs) produce very detailed and high-quality images. Their power results from extensive training on large amounts of data, usually scraped from the internet without proper attribution or consent from content creators. Unfortunately,
Externí odkaz:
http://arxiv.org/abs/2406.02366
Autor:
Podhajski, Marcin, Dubiński, Jan, Boenisch, Franziska, Dziedzic, Adam, Pregowska, Agnieszka, Michalak, Tomasz
Graph Neural Networks (GNNs) are recognized as potent tools for processing real-world data organized in graph structures. Especially inductive GNNs, which enable the processing of graph-structured data without relying on predefined graph structures,
Externí odkaz:
http://arxiv.org/abs/2405.12295
Autor:
Fang, Congyu, Dziedzic, Adam, Zhang, Lin, Oliva, Laura, Verma, Amol, Razak, Fahad, Papernot, Nicolas, Wang, Bo
Publikováno v:
eBioMedicine, vol. 101, p. 105006, 2024
Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data ac
Externí odkaz:
http://arxiv.org/abs/2402.00205
Autor:
Wang, Wenhao, Kaleem, Muhammad Ahmad, Dziedzic, Adam, Backes, Michael, Papernot, Nicolas, Boenisch, Franziska
Self-supervised learning (SSL) has recently received significant attention due to its ability to train high-performance encoders purely on unlabeled data-often scraped from the internet. This data can still be sensitive and empirical evidence suggest
Externí odkaz:
http://arxiv.org/abs/2401.12233
Publikováno v:
Фізика і хімія твердого тіла, Vol 18, Iss 3, Pp 302-308 (2018)
We have reported the effect of Co and Ni doping on structural and optical properties of ZnO thin films prepared by RF reactive sputtering technique. The composite targets were formed by mixing and pressing of ZnO, Mn3O4, CoO and NiO powders. The thin
Externí odkaz:
https://doaj.org/article/e80a2add620b4fbeaf995ebcd6aad951
Autor:
Franzese, Olive, Dziedzic, Adam, Choquette-Choo, Christopher A., Thomas, Mark R., Kaleem, Muhammad Ahmad, Rabanser, Stephan, Fang, Congyu, Jha, Somesh, Papernot, Nicolas, Wang, Xiao
Collaborative machine learning (ML) is widely used to enable institutions to learn better models from distributed data. While collaborative approaches to learning intuitively protect user data, they remain vulnerable to either the server, the clients
Externí odkaz:
http://arxiv.org/abs/2310.16678