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labmlai
GitHub Repository: labmlai/annotated_deep_learning_paper_implementations
Path: blob/master/translate_cache/adaptive_computation/parity.ja.json
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"<h1>Parity Task</h1>\n<p>This creates data for Parity Task from the paper <a href=\"https://arxiv.org/abs/1603.08983\">Adaptive Computation Time for Recurrent Neural Networks</a>.</p>\n<p>The input of the parity task is a vector with <span translate=no>_^_0_^_</span>&#x27;s <span translate=no>_^_1_^_</span>&#x27;s and <span translate=no>_^_2_^_</span>&#x27;s. The output is the parity of <span translate=no>_^_3_^_</span>&#x27;s - one if there is an odd number of <span translate=no>_^_4_^_</span>&#x27;s and zero otherwise. The input is generated by making a random number of elements in the vector either <span translate=no>_^_5_^_</span> or <span translate=no>_^_6_^_</span>&#x27;s.</p>\n": "<h1>\u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af</h1>\n<p>\u3053\u308c\u306b\u3088\u308a\u3001\u8ad6\u6587\u300c<a href=\"https://arxiv.org/abs/1603.08983\">\u30ea\u30ab\u30ec\u30f3\u30c8\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u9069\u5fdc\u7684\u8a08\u7b97\u6642\u9593</a>\u300d\u304b\u3089\u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af\u306e\u30c7\u30fc\u30bf\u304c\u4f5c\u6210\u3055\u308c\u307e\u3059\u3002</p>\n<p>\u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af\u306e\u5165\u529b\u306f\u3001\u3068 <span translate=no>_^_2_^_</span> s <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span> \u306e\u4ed8\u3044\u305f\u30d9\u30af\u30c8\u30eb\u3067\u3001\u51fa\u529b\u306f s <span translate=no>_^_3_^_</span> \u306e\u30d1\u30ea\u30c6\u30a3\u3067\u3059\u3002s <span translate=no>_^_4_^_</span> \u306e\u6570\u304c\u5947\u6570\u306e\u5834\u5408\u306f 1\u3001\u305d\u308c\u4ee5\u5916\u306e\u5834\u5408\u306f 0 \u3067\u3059\u3002\u5165\u529b\u306f\u3001<span translate=no>_^_5_^_</span><span translate=no>_^_6_^_</span>\u30d9\u30af\u30c8\u30eb\u5185\u306e\u30e9\u30f3\u30c0\u30e0\u306a\u6570\u306e\u8981\u7d20\u3092\u307e\u305f\u306f\u306e\u3044\u305a\u308c\u304b\u306b\u3059\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u751f\u6210\u3055\u308c\u307e\u3059\u3002</p>\n",
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"<h3>Parity dataset</h3>\n": "<h3>\u30d1\u30ea\u30c6\u30a3\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8</h3>\n",
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"<p> </p>\n": "<p></p>\n",
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"<p> Generate a sample</p>\n": "<p>\u30b5\u30f3\u30d7\u30eb\u3092\u751f\u6210</p>\n",
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"<p> Size of the dataset</p>\n": "<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30b5\u30a4\u30ba</p>\n",
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"<p>Empty vector </p>\n": "<p>\u7a7a\u306e\u30d9\u30af\u30c8\u30eb</p>\n",
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"<p>Fill non-zero elements with <span translate=no>_^_0_^_</span>&#x27;s and <span translate=no>_^_1_^_</span>&#x27;s </p>\n": "<p>0 \u4ee5\u5916\u306e\u8981\u7d20\u3092\u300c\u300d\u3068 <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span>\u300c\u300d\u3067\u57cb\u3081\u308b</p>\n",
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"<p>Number of non-zero elements - a random number between <span translate=no>_^_0_^_</span> and total number of elements </p>\n": "<p>\u30bc\u30ed\u4ee5\u5916\u306e\u8981\u7d20\u306e\u6570-<span translate=no>_^_0_^_</span> \u8981\u7d20\u306e\u6570\u3068\u8981\u7d20\u6570\u306e\u9593\u306e\u30e9\u30f3\u30c0\u30e0\u306a\u6570</p>\n",
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"<p>Randomly permute the elements </p>\n": "<p>\u8981\u7d20\u3092\u30e9\u30f3\u30c0\u30e0\u306b\u4e26\u3079\u66ff\u3048\u308b</p>\n",
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"<p>The parity </p>\n": "<p>\u30d1\u30ea\u30c6\u30a3</p>\n",
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"<ul><li><span translate=no>_^_0_^_</span> is the number of samples </li>\n<li><span translate=no>_^_1_^_</span> is the number of elements in the input vector</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u30b5\u30f3\u30d7\u30eb\u6570</li>\n<li><span translate=no>_^_1_^_</span>\u306f\u5165\u529b\u30d9\u30af\u30c8\u30eb\u306e\u8981\u7d20\u6570\u3067\u3059</li></ul>\n",
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"Parity Task": "\u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af",
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"This creates data for Parity Task from the paper Adaptive Computation Time for Recurrent Neural Networks": "\u3053\u308c\u306b\u3088\u308a\u3001\u8ad6\u6587\u300c\u30ea\u30ab\u30ec\u30f3\u30c8\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u9069\u5fdc\u7684\u8a08\u7b97\u6642\u9593\u300d\u304b\u3089\u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af\u306e\u30c7\u30fc\u30bf\u304c\u4f5c\u6210\u3055\u308c\u307e\u3059\u3002"
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}
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