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Varuna Jayasiri
2024-06-21 19:01:16 +05:30
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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://papers.labml.ai/paper/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://papers.labml.ai/paper/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",
"<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",
"<h3>Parity dataset</h3>\n": "<h3>\u30d1\u30ea\u30c6\u30a3\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8</h3>\n",
"<p> </p>\n": "<p></p>\n",
"<p> Generate a sample</p>\n": "<p>\u30b5\u30f3\u30d7\u30eb\u3092\u751f\u6210</p>\n",

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translate_cache/adaptive_computation/parity.si.json
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{
"<h1>Parity Task</h1>\n<p>This creates data for Parity Task from the paper <a href=\"https://papers.labml.ai/paper/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>\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf\u0d9a\u0dcf\u0dbb\u0dca\u0dba\u0dba</h1>\n<p>\u0db8\u0dd9\u0dba\u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 \u0dc0\u0dbd\u0dd2\u0db1\u0dca Parity Task \u0dc3\u0db3\u0dc4\u0dcf \u0daf\u0dad\u0dca\u0dad \u0db1\u0dd2\u0dbb\u0dca\u0db8\u0dcf\u0dab\u0dba \u0d9a\u0dbb\u0dba\u0dd2 <a href=\"https://papers.labml.ai/paper/1603.08983\">\u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0dd3 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d9a\u0dcf\u0dbd\u0dba \u0db4\u0dd4\u0db1\u0dbb\u0dcf\u0dc0\u0dbb\u0dca\u0dad\u0db1 \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd \u0dc3\u0db3\u0dc4\u0dcf</a>. </p>\n<p>\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf\u0d9a\u0dbb\u0dca\u0dad\u0dc0\u0dca\u0dba\u0dba\u0dda \u0d86\u0daf\u0dcf\u0db1\u0dba \u0dba\u0db1\u0dd4 <span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span>\u0d9c\u0dda \u0dc4\u0dcf \u0dc3\u0db8\u0d9f \u0daf\u0ddb\u0dc1\u0dd2\u0d9a\u0dba\u0d9a\u0dd2. \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1\u0dba \u0dba\u0db1\u0dd4 <span translate=no>_^_3_^_</span>\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf\u0dc0\u0dba\u0dba\u0dd2 - \u0d91\u0dc4\u0dd2 \u0db1\u0db8\u0dca \u0d91\u0d9a\u0dca \u0dba\u0db1\u0dd4 \u0d94\u0dad\u0dca\u0dad\u0dda \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0\u0d9a\u0dca \u0dc0\u0db1 <span translate=no>_^_4_^_</span>\u0d85\u0dad\u0dbb \u0dc0\u0dd9\u0db1\u0dad\u0dca \u0d86\u0d9a\u0dcf\u0dbb\u0dba\u0d9a\u0dd2\u0db1\u0dca \u0dc1\u0dd4\u0db1\u0dca\u0dba \u0dc0\u0dda. \u0d86\u0daf\u0dcf\u0db1\u0dba \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dd4 \u0dbd\u0db6\u0db1\u0dca\u0db1\u0dda \u0daf\u0ddb\u0dc1\u0dd2\u0d9a\u0dba\u0dda \u0d85\u0dc4\u0db9\u0dd4 \u0db8\u0dd6\u0dbd\u0daf\u0dca\u0dbb\u0dc0\u0dca\u0dba \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0\u0d9a\u0dca <span translate=no>_^_5_^_</span> \u0dc4\u0ddd <span translate=no>_^_6_^_</span>\u0dba.</p>\n",
"<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>\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf\u0d9a\u0dcf\u0dbb\u0dca\u0dba\u0dba</h1>\n<p>\u0db8\u0dd9\u0dba\u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 \u0dc0\u0dbd\u0dd2\u0db1\u0dca Parity Task \u0dc3\u0db3\u0dc4\u0dcf \u0daf\u0dad\u0dca\u0dad \u0db1\u0dd2\u0dbb\u0dca\u0db8\u0dcf\u0dab\u0dba \u0d9a\u0dbb\u0dba\u0dd2 <a href=\"https://arxiv.org/abs/1603.08983\">\u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0dd3 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d9a\u0dcf\u0dbd\u0dba \u0db4\u0dd4\u0db1\u0dbb\u0dcf\u0dc0\u0dbb\u0dca\u0dad\u0db1 \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd \u0dc3\u0db3\u0dc4\u0dcf</a>. </p>\n<p>\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf\u0d9a\u0dbb\u0dca\u0dad\u0dc0\u0dca\u0dba\u0dba\u0dda \u0d86\u0daf\u0dcf\u0db1\u0dba \u0dba\u0db1\u0dd4 <span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span>\u0d9c\u0dda \u0dc4\u0dcf \u0dc3\u0db8\u0d9f \u0daf\u0ddb\u0dc1\u0dd2\u0d9a\u0dba\u0d9a\u0dd2. \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1\u0dba \u0dba\u0db1\u0dd4 <span translate=no>_^_3_^_</span>\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf\u0dc0\u0dba\u0dba\u0dd2 - \u0d91\u0dc4\u0dd2 \u0db1\u0db8\u0dca \u0d91\u0d9a\u0dca \u0dba\u0db1\u0dd4 \u0d94\u0dad\u0dca\u0dad\u0dda \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0\u0d9a\u0dca \u0dc0\u0db1 <span translate=no>_^_4_^_</span>\u0d85\u0dad\u0dbb \u0dc0\u0dd9\u0db1\u0dad\u0dca \u0d86\u0d9a\u0dcf\u0dbb\u0dba\u0d9a\u0dd2\u0db1\u0dca \u0dc1\u0dd4\u0db1\u0dca\u0dba \u0dc0\u0dda. \u0d86\u0daf\u0dcf\u0db1\u0dba \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dd4 \u0dbd\u0db6\u0db1\u0dca\u0db1\u0dda \u0daf\u0ddb\u0dc1\u0dd2\u0d9a\u0dba\u0dda \u0d85\u0dc4\u0db9\u0dd4 \u0db8\u0dd6\u0dbd\u0daf\u0dca\u0dbb\u0dc0\u0dca\u0dba \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0\u0d9a\u0dca <span translate=no>_^_5_^_</span> \u0dc4\u0ddd <span translate=no>_^_6_^_</span>\u0dba.</p>\n",
"<h3>Parity dataset</h3>\n": "<h3>\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf\u0daf\u0dad\u0dca\u0dad \u0d9a\u0da7\u0dca\u0da7\u0dbd\u0dba</h3>\n",
"<p> </p>\n": "<p> </p>\n",
"<p> Generate a sample</p>\n": "<p> \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba\u0d9a\u0dca\u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",

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translate_cache/adaptive_computation/parity.zh.json
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@ -1,5 +1,5 @@
{
"<h1>Parity Task</h1>\n<p>This creates data for Parity Task from the paper <a href=\"https://papers.labml.ai/paper/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>\u5947\u5076\u6821\u9a8c\u4efb\u52a1</h1>\n<p>\u8fd9\u5c06\u4ece\u8bba\u6587\u300a<a href=\"https://papers.labml.ai/paper/1603.08983\">\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u81ea\u9002\u5e94\u8ba1\u7b97\u65f6\u95f4\u300b\u4e2d\u4e3a</a>\u5947\u5076\u6821\u9a8c\u4efb\u52a1\u521b\u5efa\u6570\u636e\u3002</p>\n<p>\u5947\u5076\u6821\u9a8c\u4efb\u52a1\u7684\u8f93\u5165\u662f\u4e00\u4e2a\u5e26\u6709<span translate=no>_^_0_^_</span>'s \u548c<span translate=no>_^_1_^_</span>'s \u7684\u5411\u91cf\u3002\u8f93\u51fa\u662f<span translate=no>_^_2_^_</span>'s \u7684<span translate=no>_^_3_^_</span>\u5947\u5076\u6821\u9a8c\u2014\u2014\u5982\u679c\u6709\uff0c\u5219\u4e3a 1\u662f\u7684\u5947\u6570<span translate=no>_^_4_^_</span>\uff0c\u5426\u5219\u4e3a\u96f6\u3002\u8f93\u5165\u662f\u901a\u8fc7\u4f7f\u77e2\u91cf\u4e2d\u7684\u968f\u673a\u6570\u91cf\u7684\u5143\u7d20\u4e3a<span translate=no>_^_5_^_</span>\u6216\u800c\u751f\u6210<span translate=no>_^_6_^_</span>\u7684\u3002</p>\n",
"<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>\u5947\u5076\u6821\u9a8c\u4efb\u52a1</h1>\n<p>\u8fd9\u5c06\u4ece\u8bba\u6587\u300a<a href=\"https://arxiv.org/abs/1603.08983\">\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u81ea\u9002\u5e94\u8ba1\u7b97\u65f6\u95f4\u300b\u4e2d\u4e3a</a>\u5947\u5076\u6821\u9a8c\u4efb\u52a1\u521b\u5efa\u6570\u636e\u3002</p>\n<p>\u5947\u5076\u6821\u9a8c\u4efb\u52a1\u7684\u8f93\u5165\u662f\u4e00\u4e2a\u5e26\u6709<span translate=no>_^_0_^_</span>'s \u548c<span translate=no>_^_1_^_</span>'s \u7684\u5411\u91cf\u3002\u8f93\u51fa\u662f<span translate=no>_^_2_^_</span>'s \u7684<span translate=no>_^_3_^_</span>\u5947\u5076\u6821\u9a8c\u2014\u2014\u5982\u679c\u6709\uff0c\u5219\u4e3a 1\u662f\u7684\u5947\u6570<span translate=no>_^_4_^_</span>\uff0c\u5426\u5219\u4e3a\u96f6\u3002\u8f93\u5165\u662f\u901a\u8fc7\u4f7f\u77e2\u91cf\u4e2d\u7684\u968f\u673a\u6570\u91cf\u7684\u5143\u7d20\u4e3a<span translate=no>_^_5_^_</span>\u6216\u800c\u751f\u6210<span translate=no>_^_6_^_</span>\u7684\u3002</p>\n",
"<h3>Parity dataset</h3>\n": "<h3>\u5947\u5076\u6821\u9a8c\u6570\u636e</h3>\n",
"<p> </p>\n": "<p></p>\n",
"<p> Generate a sample</p>\n": "<p>\u751f\u6210\u6837\u672c</p>\n",

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{
"<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: Learning to Ponder</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://papers.labml.ai/paper/2107.05407\">PonderNet: Learning to Ponder</a>.</p>\n<p>PonderNet adapts the computation based on the input. It changes the number of steps to take on a recurrent network based on the input. PonderNet learns this with end-to-end gradient descent. </p>\n": "<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: \u719f\u8003\u3059\u308b\u3053\u3068\u3092\u5b66\u3076</a></h1>\n<p>\u3053\u308c\u306f\u3001\u8ad6\u6587\u300c<a href=\"https://papers.labml.ai/paper/2107.05407\">PonderNet: \u719f\u8003\u3092\u5b66\u307c\u3046</a>\u300d<a href=\"https://pytorch.org\">\u3092PyTorch\u3067\u5b9f\u88c5\u3057\u305f\u3082\u306e\u3067\u3059</a>\u3002</p>\n<p>PonderNet \u306f\u5165\u529b\u306b\u57fa\u3065\u3044\u3066\u8a08\u7b97\u3092\u8abf\u6574\u3057\u307e\u3059\u3002\u5165\u529b\u306b\u57fa\u3065\u3044\u3066\u30ea\u30ab\u30ec\u30f3\u30c8\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3067\u5b9f\u884c\u3059\u308b\u30b9\u30c6\u30c3\u30d7\u306e\u6570\u3092\u5909\u66f4\u3057\u307e\u3059\u3002PonderNet\u306f\u3053\u308c\u3092\u7aef\u304b\u3089\u7aef\u307e\u3067\u306e\u52fe\u914d\u964d\u4e0b\u6cd5\u3067\u5b66\u7fd2\u3057\u307e\u3059</p>\u3002\n",
"<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: Learning to Ponder</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/2107.05407\">PonderNet: Learning to Ponder</a>.</p>\n<p>PonderNet adapts the computation based on the input. It changes the number of steps to take on a recurrent network based on the input. PonderNet learns this with end-to-end gradient descent. </p>\n": "<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: \u719f\u8003\u3059\u308b\u3053\u3068\u3092\u5b66\u3076</a></h1>\n<p>\u3053\u308c\u306f\u3001\u8ad6\u6587\u300c<a href=\"https://arxiv.org/abs/2107.05407\">PonderNet: \u719f\u8003\u3092\u5b66\u307c\u3046</a>\u300d<a href=\"https://pytorch.org\">\u3092PyTorch\u3067\u5b9f\u88c5\u3057\u305f\u3082\u306e\u3067\u3059</a>\u3002</p>\n<p>PonderNet \u306f\u5165\u529b\u306b\u57fa\u3065\u3044\u3066\u8a08\u7b97\u3092\u8abf\u6574\u3057\u307e\u3059\u3002\u5165\u529b\u306b\u57fa\u3065\u3044\u3066\u30ea\u30ab\u30ec\u30f3\u30c8\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3067\u5b9f\u884c\u3059\u308b\u30b9\u30c6\u30c3\u30d7\u306e\u6570\u3092\u5909\u66f4\u3057\u307e\u3059\u3002PonderNet\u306f\u3053\u308c\u3092\u7aef\u304b\u3089\u7aef\u307e\u3067\u306e\u52fe\u914d\u964d\u4e0b\u6cd5\u3067\u5b66\u7fd2\u3057\u307e\u3059</p>\u3002\n",
"PonderNet: Learning to Ponder": "PonderNet: \u719f\u8003\u3059\u308b\u3053\u3068\u3092\u5b66\u3076"
}

2
translate_cache/adaptive_computation/ponder_net/readme.si.json
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@ -1,4 +1,4 @@
{
"<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: Learning to Ponder</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://papers.labml.ai/paper/2107.05407\">PonderNet: Learning to Ponder</a>.</p>\n<p>PonderNet adapts the computation based on the input. It changes the number of steps to take on a recurrent network based on the input. PonderNet learns this with end-to-end gradient descent. </p>\n": "<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">\u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca: \u0db8\u0dd9\u0db1\u0dd9\u0dc4\u0dd2 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a></h1>\n<p>\u0db8\u0dd9\u0dba <a href=\"https://papers.labml.ai/paper/2107.05407\">PonderNet \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 <a href=\"https://pytorch.org\">PyTorch</a> \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8: \u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca \u0dc0\u0dd9\u0dad \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a> . </p>\n<p>PonderNet\u0d86\u0daf\u0dcf\u0db1\u0dba \u0db8\u0dad \u0db4\u0daf\u0db1\u0db8\u0dca\u0dc0 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0dba\u0dd2. \u0d86\u0daf\u0dcf\u0db1\u0dba \u0db8\u0dad \u0db4\u0daf\u0db1\u0db8\u0dca\u0dc0 \u0db4\u0dd4\u0db1\u0dbb\u0dcf\u0dc0\u0dbb\u0dca\u0dad\u0db1 \u0da2\u0dcf\u0dbd\u0dba\u0d9a\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9c\u0dad \u0dba\u0dd4\u0dad\u0dd4 \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1 \u0d91\u0dba \u0dc0\u0dd9\u0db1\u0dc3\u0dca \u0d9a\u0dbb\u0dba\u0dd2. \u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca \u0db8\u0dd9\u0dba \u0d89\u0d9c\u0dd9\u0db1 \u0d9c\u0db1\u0dca\u0db1\u0dda \u0d85\u0dc0\u0dc3\u0dcf\u0db1\u0dba \u0dc3\u0dd2\u0da7 \u0d85\u0dc0\u0dc3\u0dcf\u0db1\u0dba \u0daf\u0d9a\u0dca\u0dc0\u0dcf \u0dc0\u0dd6 \u0dc1\u0dca\u0dbb\u0dda\u0dab\u0dd2\u0dba\u0dda \u0dc3\u0db8\u0dca\u0db7\u0dc0\u0dba\u0d9a\u0dca \u0dc3\u0db8\u0d9f\u0dba. </p>\n",
"<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: Learning to Ponder</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/2107.05407\">PonderNet: Learning to Ponder</a>.</p>\n<p>PonderNet adapts the computation based on the input. It changes the number of steps to take on a recurrent network based on the input. PonderNet learns this with end-to-end gradient descent. </p>\n": "<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">\u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca: \u0db8\u0dd9\u0db1\u0dd9\u0dc4\u0dd2 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a></h1>\n<p>\u0db8\u0dd9\u0dba <a href=\"https://arxiv.org/abs/2107.05407\">PonderNet \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 <a href=\"https://pytorch.org\">PyTorch</a> \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8: \u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca \u0dc0\u0dd9\u0dad \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a> . </p>\n<p>PonderNet\u0d86\u0daf\u0dcf\u0db1\u0dba \u0db8\u0dad \u0db4\u0daf\u0db1\u0db8\u0dca\u0dc0 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0dba\u0dd2. \u0d86\u0daf\u0dcf\u0db1\u0dba \u0db8\u0dad \u0db4\u0daf\u0db1\u0db8\u0dca\u0dc0 \u0db4\u0dd4\u0db1\u0dbb\u0dcf\u0dc0\u0dbb\u0dca\u0dad\u0db1 \u0da2\u0dcf\u0dbd\u0dba\u0d9a\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9c\u0dad \u0dba\u0dd4\u0dad\u0dd4 \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1 \u0d91\u0dba \u0dc0\u0dd9\u0db1\u0dc3\u0dca \u0d9a\u0dbb\u0dba\u0dd2. \u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca \u0db8\u0dd9\u0dba \u0d89\u0d9c\u0dd9\u0db1 \u0d9c\u0db1\u0dca\u0db1\u0dda \u0d85\u0dc0\u0dc3\u0dcf\u0db1\u0dba \u0dc3\u0dd2\u0da7 \u0d85\u0dc0\u0dc3\u0dcf\u0db1\u0dba \u0daf\u0d9a\u0dca\u0dc0\u0dcf \u0dc0\u0dd6 \u0dc1\u0dca\u0dbb\u0dda\u0dab\u0dd2\u0dba\u0dda \u0dc3\u0db8\u0dca\u0db7\u0dc0\u0dba\u0d9a\u0dca \u0dc3\u0db8\u0d9f\u0dba. </p>\n",
"PonderNet: Learning to Ponder": "\u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca: \u0db8\u0dd9\u0db1\u0dd9\u0dc4\u0dd2 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8"
}

2
translate_cache/adaptive_computation/ponder_net/readme.zh.json
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@ -1,4 +1,4 @@
{
"<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: Learning to Ponder</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://papers.labml.ai/paper/2107.05407\">PonderNet: Learning to Ponder</a>.</p>\n<p>PonderNet adapts the computation based on the input. It changes the number of steps to take on a recurrent network based on the input. PonderNet learns this with end-to-end gradient descent. </p>\n": "<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet\uff1a\u5b66\u4f1a\u601d\u8003</a></h1>\n<p>\u8fd9\u662f <a href=\"https://pytorch.org\">P <a href=\"https://papers.labml.ai/paper/2107.05407\">onderNet\uff1a\u5b66\u4f1a\u601d\u8003</a>\u8bba\u6587\u7684 PyTorch</a> \u5b9e\u73b0\u3002</p>\n<p>PonderNet \u6839\u636e\u8f93\u5165\u8c03\u6574\u8ba1\u7b97\u3002\u5b83\u4f1a\u6839\u636e\u8f93\u5165\u66f4\u6539\u5faa\u73af\u7f51\u7edc\u4e0a\u8981\u6267\u884c\u7684\u6b65\u9aa4\u6570\u3002PonderNet \u901a\u8fc7\u7aef\u5230\u7aef\u68af\u5ea6\u4e0b\u964d\u6765\u5b66\u4e60\u8fd9\u4e00\u70b9\u3002</p>\n",
"<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: Learning to Ponder</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/2107.05407\">PonderNet: Learning to Ponder</a>.</p>\n<p>PonderNet adapts the computation based on the input. It changes the number of steps to take on a recurrent network based on the input. PonderNet learns this with end-to-end gradient descent. </p>\n": "<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet\uff1a\u5b66\u4f1a\u601d\u8003</a></h1>\n<p>\u8fd9\u662f <a href=\"https://pytorch.org\">P <a href=\"https://arxiv.org/abs/2107.05407\">onderNet\uff1a\u5b66\u4f1a\u601d\u8003</a>\u8bba\u6587\u7684 PyTorch</a> \u5b9e\u73b0\u3002</p>\n<p>PonderNet \u6839\u636e\u8f93\u5165\u8c03\u6574\u8ba1\u7b97\u3002\u5b83\u4f1a\u6839\u636e\u8f93\u5165\u66f4\u6539\u5faa\u73af\u7f51\u7edc\u4e0a\u8981\u6267\u884c\u7684\u6b65\u9aa4\u6570\u3002PonderNet \u901a\u8fc7\u7aef\u5230\u7aef\u68af\u5ea6\u4e0b\u964d\u6765\u5b66\u4e60\u8fd9\u4e00\u70b9\u3002</p>\n",
"PonderNet: Learning to Ponder": "PonderNet\uff1a\u5b66\u4f1a\u601d\u8003"
}