Zobrazeno 1 - 10
of 338
pro vyhledávání: '"Thai, My T."'
Providing textual concept-based explanations for neurons in deep neural networks (DNNs) is of importance in understanding how a DNN model works. Prior works have associated concepts with neurons based on examples of concepts or a pre-defined set of c
Externí odkaz:
http://arxiv.org/abs/2406.08572
Quantum Annealing (QA) holds great potential for solving combinatorial optimization problems efficiently. However, the effectiveness of QA algorithms heavily relies on the embedding of problem instances, represented as logical graphs, into the quantu
Externí odkaz:
http://arxiv.org/abs/2406.07124
With the rapid adoption of Federated Learning (FL) as the training and tuning protocol for applications utilizing Large Language Models (LLMs), recent research highlights the need for significant modifications to FL to accommodate the large-scale of
Externí odkaz:
http://arxiv.org/abs/2403.04784
Publikováno v:
International Conference on Artificial Intelligence and Statistics (AISTATS) 2024
Multiplex influence maximization (MIM) asks us to identify a set of seed users such as to maximize the expected number of influenced users in a multiplex network. MIM has been one of central research topics, especially in nowadays social networking l
Externí odkaz:
http://arxiv.org/abs/2402.16898
Autor:
Thwal, Chu Myaet, Nguyen, Minh N. H., Tun, Ye Lin, Kim, Seong Tae, Thai, My T., Hong, Choong Seon
Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices while preserving privacy. The success of FL hinges on the efficiency of participating models and their ability t
Externí odkaz:
http://arxiv.org/abs/2401.11652
Federated Learning (FL) has garnered significant attention for its potential to protect user privacy while enhancing model training efficiency. For that reason, FL has found its use in various domains, from healthcare to industrial engineering, espec
Externí odkaz:
http://arxiv.org/abs/2311.13739
Stochastic bilevel optimization finds widespread applications in machine learning, including meta-learning, hyperparameter optimization, and neural architecture search. To extend stochastic bilevel optimization to distributed data, several decentrali
Externí odkaz:
http://arxiv.org/abs/2311.11342
Network motif identification problem aims to find topological patterns in biological networks. Identifying non-overlapping motifs is a computationally challenging problem using classical computers. Quantum computers enable solving high complexity pro
Externí odkaz:
http://arxiv.org/abs/2311.03400
Quantum annealing (QA) has emerged as a powerful technique to solve optimization problems by taking advantages of quantum physics. In QA process, a bottleneck that may prevent QA to scale up is minor embedding step in which we embed optimization prob
Externí odkaz:
http://arxiv.org/abs/2307.01843
Federated learning is a new learning paradigm for extracting knowledge from distributed data. Due to its favorable properties in preserving privacy and saving communication costs, it has been extensively studied and widely applied to numerous data an
Externí odkaz:
http://arxiv.org/abs/2306.02570