Zobrazeno 1 - 10
of 7 628
pro vyhledávání: '"and, Saifuddin"'
Denoising diffusion models (DDMs) offer a flexible framework for sampling from high dimensional data distributions. DDMs generate a path of probability distributions interpolating between a reference Gaussian distribution and a data distribution by i
Externí odkaz:
http://arxiv.org/abs/2412.07877
Autor:
Zafar, Saifuddin, Ehsan, Mashaekh Tausif, Suvro, Sourav Das, Islam, Mahmudul, Hasan, Mohammad Nasim
Refractory high-entropy alloys (RHEAs) are a promising class of materials with potential applications in extreme environments, where the dominant failure mode is thermal creep. The design of these alloys, therefore, requires an understanding of how t
Externí odkaz:
http://arxiv.org/abs/2412.00588
Autor:
Ehsan, Mashaekh Tausif, Zafar, Saifuddin, Sarker, Apurba, Suvro, Sourav Das, Hasan, Mohammad Nasim
Machine learning (ML) methods have drawn significant interest in material design and discovery. Graph neural networks (GNNs), in particular, have demonstrated strong potential for predicting material properties. The present study proposes a graph-bas
Externí odkaz:
http://arxiv.org/abs/2411.13670
Automatic scene generation is an essential area of research with applications in robotics, recreation, visual representation, training and simulation, education, and more. This survey provides a comprehensive review of the current state-of-the-arts i
Externí odkaz:
http://arxiv.org/abs/2410.01816
Annealed Sequential Monte Carlo (SMC) samplers are special cases of SMC samplers where the sequence of distributions can be embedded in a smooth path of distributions. Using this underlying path of distributions and a performance model based on the v
Externí odkaz:
http://arxiv.org/abs/2408.12057
Autor:
Hossain, Tanvir, Saifuddin, Khaled Mohammed, Islam, Muhammad Ifte Khairul, Tanvir, Farhan, Akbas, Esra
Graph Neural Network (GNN) achieves great success for node-level and graph-level tasks via encoding meaningful topological structures of networks in various domains, ranging from social to biological networks. However, repeated aggregation operations
Externí odkaz:
http://arxiv.org/abs/2407.11928
Non-reversible parallel tempering (NRPT) is an effective algorithm for sampling from target distributions with complex geometry, such as those arising from posterior distributions of weakly identifiable and high-dimensional Bayesian models. In this w
Externí odkaz:
http://arxiv.org/abs/2405.11384
Predicting events such as political protests, flu epidemics, and criminal activities is crucial to proactively taking necessary measures and implementing required responses to address emerging challenges. Capturing contextual information from textual
Externí odkaz:
http://arxiv.org/abs/2404.15612
In this paper, we apply the machine learning clustering algorithm Density Based Spatial Clustering of Applications with Noise (DBSCAN) to study the membership of stars in twelve open clusters (NGC~2264, NGC~2682, NGC~2244, NGC~3293, NGC~6913, NGC~714
Externí odkaz:
http://arxiv.org/abs/2404.10477
Mental health challenges are on the rise in our modern society, and the imperative to address mental disorders, especially regarding anxiety, depression, and suicidal thoughts, underscores the need for effective interventions. This paper delves into
Externí odkaz:
http://arxiv.org/abs/2403.05568