Pruning methods for RBF networks by Tarannum Shakir.

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Unsupervised generative variational continual learning by Liu, Guimeng

“…There are mainly three categories in this field: replay methods, regularization-based methods, and parameter isolation methods. …”
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Discovering thematic objects in image collections and videos by Katsaggelos, Aggelos K., Yuan, Junsong, Zhao, Gangqiang, Fu, Yun, Li, Zhu, Wu, Ying

“…Experiments on challenging image and video data sets and comparisons with existing methods validate the effectiveness of our method.…”
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Accurate and Fast Approximate Graph Mining at Scale by Arpaci-Dusseau, Anna

“…Second, we propose two techniques to deal with the “needle-in-the-hay” problem, eager-verify and hybrid sampling. Our eager-verify method drastically improves sampling hit rate by pruning unpromising candidates as early as possible. …”
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Refining learning models in grammatical inference by Wang, Xiangrui

“…We introduce the use of recurrent neural networks (RNNs) and present a pruning learning method to avoid the exponential space costs. …”
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Embedded computing techniques for remote mobile video surveillance systems by Nirmala Ramakrishnan

“…Next, a low-complexity GME method (called sparse-GME) is proposed by employing a minimum number of well-distributed sparse corner features. …”
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Efficient techniques for subgraph mining and query processing by Lin, Wenqing

“…Based on our analysis, we develop a GPU-based solution that (i) drastically differs from existing CPU-based methods, and (ii) exploits the strengths of GPUs in terms of parallelism while mitigating their limitations in terms of the computation power per GPU core. …”
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Efficient algorithms for subgraph counting and enumeration on large graphs by Wang, Kaixin

“…It determines the weights of edges in a data-driven fashion, using a novel method based on reinforcement learning. We conduct extensive experi- ments to verify that our technique can produce estimates with smaller errors while often running faster compared with existing algorithms. …”
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