Výsledky vyhledávání - "Savitha Ramasamy"

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Unsupervised Few-Shot Continual Learning for Remote Sensing Image Scene Classification

Autor: Ma'sum, Muhammad Anwar, Pratama, Mahardhika, Savitha, Ramasamy, Liu, Lin, Habibullah, Kowalczyk, Ryszard

A continual learning (CL) model is desired for remote sensing image analysis because of varying camera parameters, spectral ranges, resolutions, etc. There exist some recent initiatives to develop CL techniques in this domain but they still depend on

Externí odkaz: http://arxiv.org/abs/2406.18574

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Cross-Domain Continual Learning via CLAMP

Autor: Weng, Weiwei, Pratama, Mahardhika, Zhang, Jie, Chen, Chen, Yee, Edward Yapp Kien, Savitha, Ramasamy

Artificial neural networks, celebrated for their human-like cognitive learning abilities, often encounter the well-known catastrophic forgetting (CF) problem, where the neural networks lose the proficiency in previously acquired knowledge. Despite nu

Externí odkaz: http://arxiv.org/abs/2405.07142

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Class-incremental Learning for Time Series: Benchmark and Evaluation

Autor: Qiao, Zhongzheng, Pham, Quang, Cao, Zhen, Le, Hoang H, Suganthan, P. N., Jiang, Xudong, Savitha, Ramasamy

Real-world environments are inherently non-stationary, frequently introducing new classes over time. This is especially common in time series classification, such as the emergence of new disease classification in healthcare or the addition of new act

Externí odkaz: http://arxiv.org/abs/2402.12035

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Robust Continual Learning through a Comprehensively Progressive Bayesian Neural Network

Autor: Yang, Guo, Wong, Cheryl Sze Yin, Savitha, Ramasamy

This work proposes a comprehensively progressive Bayesian neural network for robust continual learning of a sequence of tasks. A Bayesian neural network is progressively pruned and grown such that there are sufficient network resources to represent a

Externí odkaz: http://arxiv.org/abs/2202.13369

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Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

Autor: Oviedo, Felipe, Ren, Zekun, Sun, Shijing, Settens, Charlie, Liu, Zhe, Hartono, Noor Titan Putri, Savitha, Ramasamy, DeCost, Brian L., Tian, Siyu I. P., Romano, Giuseppe, Kusne, Aaron Gilad, Buonassisi, Tonio

X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-enabled approach to predict crystallographic dimensionality and space gr

Externí odkaz: http://arxiv.org/abs/1811.08425

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Graph Convolutional Neural Networks for Polymers Property Prediction

Autor: Zeng, Minggang, Kumar, Jatin Nitin, Zeng, Zeng, Savitha, Ramasamy, Chandrasekhar, Vijay Ramaseshan, Hippalgaonkar, Kedar

A fast and accurate predictive tool for polymer properties is demanding and will pave the way to iterative inverse design. In this work, we apply graph convolutional neural networks (GCNN) to predict the dielectric constant and energy bandgap of poly

Externí odkaz: http://arxiv.org/abs/1811.06231

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Akademický článek

Online RBM: Growing Restricted Boltzmann Machine on the fly for unsupervised representation

Autor: Savitha, Ramasamy, Ambikapathi, ArulMurugan, Rajaraman, Kanagasabai

Publikováno v: In Applied Soft Computing Journal July 2020 92

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Akademický článek

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