Zobrazeno 1 - 10
of 8 210
pro vyhledávání: '"Yao Xin"'
In numerous production environments, Approximate Nearest Neighbor Search (ANNS) plays an indispensable role, particularly when dealing with massive datasets that can contain billions of entries. The necessity for rapid response times in these applica
Externí odkaz:
http://arxiv.org/abs/2410.23805
Federated learning (FL), integrating group fairness mechanisms, allows multiple clients to collaboratively train a global model that makes unbiased decisions for different populations grouped by sensitive attributes (e.g., gender and race). Due to it
Externí odkaz:
http://arxiv.org/abs/2410.06509
Publikováno v:
IEEE Transactions on Evolutionary Computation (2014)
Multiobjective evolutionary learning (MOEL) has demonstrated its advantages of training fairer machine learning models considering a predefined set of conflicting objectives, including accuracy and different fairness measures. Recent works propose to
Externí odkaz:
http://arxiv.org/abs/2409.18499
Publikováno v:
Machine Learning 2024
Recent advancements in the realm of deep generative models focus on generating samples that satisfy multiple desired properties. However, prevalent approaches optimize these property functions independently, thus omitting the trade-offs among them. I
Externí odkaz:
http://arxiv.org/abs/2407.04493
Nowadays, neural network (NN) and deep learning (DL) techniques are widely adopted in many applications, including recommender systems. Given the sparse and stochastic nature of collaborative filtering (CF) data, recent works have critically analyzed
Externí odkaz:
http://arxiv.org/abs/2407.00063
Autor:
Poyatos, Javier, Del Ser, Javier, Garcia, Salvador, Ishibuchi, Hisao, Molina, Daniel, Triguero, Isaac, Xue, Bing, Yao, Xin, Herrera, Francisco
In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems devised to cope with a single task. The recent emergence of General-Purp
Externí odkaz:
http://arxiv.org/abs/2407.08745
Real-world applications involve various discrete optimization problems. Designing a specialized optimizer for each of these problems is challenging, typically requiring significant domain knowledge and human efforts. Hence, developing general-purpose
Externí odkaz:
http://arxiv.org/abs/2405.18884
Recently, evolutionary reinforcement learning has obtained much attention in various domains. Maintaining a population of actors, evolutionary reinforcement learning utilises the collected experiences to improve the behaviour policy through efficient
Externí odkaz:
http://arxiv.org/abs/2404.14763
Fairness in machine learning (ML) has received much attention. However, existing studies have mainly focused on the distributive fairness of ML models. The other dimension of fairness, i.e., procedural fairness, has been neglected. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2404.01877
Autor:
Cui, Yiming, Yao, Xin
Mixtral, a representative sparse mixture of experts (SMoE) language model, has received significant attention due to its unique model design and superior performance. Based on Mixtral-8x7B-v0.1, in this paper, we propose Chinese-Mixtral and Chinese-M
Externí odkaz:
http://arxiv.org/abs/2403.01851