Zobrazeno 1 - 10
of 402
pro vyhledávání: '"CHATTOPADHYAY, ANUPAM"'
Combating money laundering has become increasingly complex with the rise of cybercrime and digitalization of financial transactions. Graph-based machine learning techniques have emerged as promising tools for Anti-Money Laundering (AML) detection, ca
Externí odkaz:
http://arxiv.org/abs/2411.02926
Autor:
Velmurugan, Haemanth, Das, Arnav, Chatterjee, Turbasu, Saha, Amit, Chattopadhyay, Anupam, Chakrabarti, Amlan
Simulating quantum circuits is a computationally intensive task that relies heavily on tensor products and matrix multiplications, which can be inefficient. Recent advancements, eliminate the need for tensor products and matrix multiplications, offer
Externí odkaz:
http://arxiv.org/abs/2410.17876
The promise and proliferation of large-scale dynamic federated learning gives rise to a prominent open question - is it prudent to share data or model across nodes, if efficiency of transmission and fast knowledge transfer are the prime objectives. T
Externí odkaz:
http://arxiv.org/abs/2406.10798
In recent decades, the field of quantum computing has experienced remarkable progress. This progress is marked by the superior performance of many quantum algorithms compared to their classical counterparts, with Shor's algorithm serving as a promine
Externí odkaz:
http://arxiv.org/abs/2406.03867
This work integrates peer-to-peer federated learning tools with NS3, a widely used network simulator, to create a novel simulator designed to allow heterogeneous device experiments in federated learning. This cross-platform adaptability addresses a c
Externí odkaz:
http://arxiv.org/abs/2405.17839
Efficient quantum arithmetic circuits are commonly found in numerous quantum algorithms of practical significance. Till date, the logarithmic-depth quantum adders includes a constant coefficient k >= 2 while achieving the Toffoli-Depth of klog n + O(
Externí odkaz:
http://arxiv.org/abs/2405.02523
Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU
In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers. Among the components in this domain, activation functions hold a fundamental and indispensable role. Our research focuses on the development of act
Externí odkaz:
http://arxiv.org/abs/2404.06059
Adversarial attacks on machine learning algorithms have been a key deterrent to the adoption of AI in many real-world use cases. They significantly undermine the ability of high-performance neural networks by forcing misclassifications. These attacks
Externí odkaz:
http://arxiv.org/abs/2404.02660
Rapid progress in the design of scalable, robust quantum computing necessitates efficient quantum circuit implementation for algorithms with practical relevance. For several algorithms, arithmetic kernels, in particular, division plays an important r
Externí odkaz:
http://arxiv.org/abs/2403.01206
This paper details the privacy and security landscape in today's cloud ecosystem and identifies that there is a gap in addressing the risks introduced by machine learning models. As machine learning algorithms continue to evolve and find applications
Externí odkaz:
http://arxiv.org/abs/2402.00896