Get FREE domain for 1st year and build your brand new site. Classification is the task to assign probability to different categories based on how an object like image and text is likely to match the category. Intense research has been done in this domain. We have presented Research Papers in this domain using Machine Learning approaches. Following are the research papers that you must go through to understand Classification in Machine Learning:.
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Inspecting gradient magnitudes in context can be a powerful tool to see when recurrent units use short-term or long-term contextual understanding. Andreas Madsen. However, the practical problem of memorization still poses a challenge. As such, developing new recurrent units that are better at memorization continues to be an active field of research. To compare a recurrent unit against its alternatives, both past and recent papers, such as the Nested LSTM paper by Monzi et al. These comparisons often measure accuracy or cross entropy loss on standard problems such as Penn Treebank , Chinese Poetry Generation, or text8 , where the task is to predict the next character given existing input.
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A key limitation of the prior works is the lack of a systematic design optimization framework of RNN model and hardware implementations, especially when the block size or compression ratio should be jointly optimized with RNN type, layer size, etc. ADMM-based training provides an effective means to deal with the structure requirement in weight matrices, thereby enhancing accuracy and training speed. Hardware design: At hardware level, we propose a systematic design framework and hardware optimization using HLS, to achieve alternative designs for RNNs, and to limit the design range and accelerate the design exploration. This paper focuses on block-circulant matrix-based RNN implementations and aim to mitigate these limitations with target application as Automatic Speech Recognition. They did not provide a systematic method to perform design optimization.
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