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pro vyhledávání: '"Thakkar, Janvi"'
Malicious adversaries can attack machine learning models to infer sensitive information or damage the system by launching a series of evasion attacks. Although various work addresses privacy and security concerns, they focus on individual defenses, b
Externí odkaz:
http://arxiv.org/abs/2401.10405
Machine learning models are being used in an increasing number of critical applications; thus, securing their integrity and ownership is critical. Recent studies observed that adversarial training and watermarking have a conflicting interaction. This
Externí odkaz:
http://arxiv.org/abs/2312.14260
Autor:
Thakkar, Janvi, Joshi, Devvrat
Clustering in graphs has been a well-known research problem, particularly because most Internet and social network data is in the form of graphs. Organizations widely use spectral clustering algorithms to find clustering in graph datasets. However, a
Externí odkaz:
http://arxiv.org/abs/2302.02137
Autor:
Joshi, Devvrat, Thakkar, Janvi
In today's data-driven world, the sensitivity of information has been a significant concern. With this data and additional information on the person's background, one can easily infer an individual's private data. Many differentially private iterativ
Externí odkaz:
http://arxiv.org/abs/2301.02896
The computational resources required to train a model have been increasing since the inception of deep networks. Training neural networks on massive datasets have become a challenging and time-consuming task. So, there arises a need to reduce the dat
Externí odkaz:
http://arxiv.org/abs/2209.02609
In this paper, we present novel variations of an earlier approach called homogeneous clustering algorithm for reducing dataset size. The intuition behind the approaches proposed in this paper is to partition the dataset into homogeneous clusters and
Externí odkaz:
http://arxiv.org/abs/2208.13079