diff --git a/translate_cache/__init__.zh.json b/translate_cache/__init__.zh.json index a513003c..99aaadf5 100644 --- a/translate_cache/__init__.zh.json +++ b/translate_cache/__init__.zh.json @@ -1,55 +1,55 @@ { - "

labml.ai Annotated PyTorch Paper Implementations

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labml.ai \u5e26\u6ce8\u91ca\u7684 pyTorch \u8bba\u6587\u5b9e\u73b0

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Highlighted Research Paper PDFs

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\u91cd\u70b9\u7814\u7a76\u8bba\u6587 PDF

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Paper Implementations

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\u7eb8\u8d28\u5b9e\u73b0

\n", + "

labml.ai Annotated PyTorch Paper Implementations

\n": "

labml.ai \u5e26\u6ce8\u91ca\u7684 PyTorch \u7248\u8bba\u6587\u5b9e\u73b0

\n", + "

Highlighted Research Paper PDFs

\n": "

\u4e3b\u8981\u7814\u7a76\u8bba\u6587 PDF

\n", + "

Paper Implementations

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\u8bba\u6587\u5b9e\u73b0

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Translations

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\u7ffb\u8bd1

\n", "

English (original)

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\u82f1\u8bed\uff08\u539f\u7248\uff09

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Japanese (translated)

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\u65e5\u8bed\uff08\u5df2\u7ffb\u8bd1\uff09

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Japanese (translated)

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\u65e5\u8bed\uff08\u7ffb\u8bd1\uff09

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Chinese (translated)

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\u4e2d\u6587\uff08\u7ffb\u8bd1\uff09

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Citing LabML

\n": "

\u5f15\u7528 LabML

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Installation

\n": "

\u5b89\u88c5

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\u2728 Activations

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\u2728 \u6fc0\u6d3b

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\u2728 Activations

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\u2728 \u6fc0\u6d3b\u51fd\u6570

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\u2728 Adaptive Computation

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\u2728 \u81ea\u9002\u5e94\u8ba1\u7b97

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\u2728 Capsule Networks

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\u2728 \u80f6\u56ca\u7f51\u7edc

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\u2728 Counterfactual Regret Minimization (CFR)

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\u2728 \u53cd\u4e8b\u5b9e\u9057\u61be\u6700\u5c0f\u5316\uff08CFR\uff09

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\u2728 ConvMixer

\n": "

\u2728 \u6df7\u97f3\u5668

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\u2728 Counterfactual Regret Minimization (CFR)

\n": "

\u2728 \u865a\u62df\u9057\u61be\u6700\u5c0f\u5316\uff08CFR\uff09

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\u2728 ConvMixer

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\u2728 ConvMixer

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\u2728 Diffusion models

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\u2728 \u6269\u6563\u6a21\u578b

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\u2728 Distillation

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\u2728 \u84b8\u998f

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\u2728 Generative Adversarial Networks

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\u2728 \u751f\u6210\u5bf9\u6297\u7f51\u7edc

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\u2728 HyperNetworks - HyperLSTM

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\u2728 \u8d85\u7ea7\u7f51\u7edc-HyperLSTM

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\u2728 HyperNetworks - HyperLSTM

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\u2728 \u8d85\u7f51\u7edc-HyperLSTM

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\u2728 LSTM

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\u2728 LSTM

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\u2728 Eleuther GPT-NeoX

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\u2728 Eleuther GPT-neox

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\u2728 Normalization Layers

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\u2728 \u89c4\u8303\u5316\u5c42

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\u2728 Normalization Layers

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\u2728 \u5f52\u4e00\u5316\u5c42

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\u2728 Optimizers

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\u2728 \u4f18\u5316\u5668

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\u2728 Recurrent Highway Networks

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\u2728 \u5faa\u73af\u9ad8\u901f\u516c\u8def\u7f51\u7edc

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\u2728 Recurrent Highway Networks

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\u2728 \u5faa\u73af\u9ad8\u901f\u8def\u7f51\u7edc

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\u2728 ResNet

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\u2728 ResNet

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\u2728 Reinforcement Learning

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\u2728 \u5f3a\u5316\u5b66\u4e60

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\u2728 Language Model Sampling Techniques

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\u2728 \u8bed\u8a00\u6a21\u578b\u91c7\u6837\u6280\u672f

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\u2728 Scalable Training/Inference

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\u2728 \u53ef\u6269\u5c55\u7684\u8bad\u7ec3/\u63a8\u7406

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\u2728 Sketch RNN

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\u2728 \u7d20\u63cf RNN

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\u2728 Transformers

\n": "

\u2728 \u53d8\u5f62\u91d1\u521a

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\u2728 Scalable Training/Inference

\n": "

\u2728 \u53ef\u6269\u5c55\u8bad\u7ec3/\u63a8\u7406

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\u2728 Sketch RNN

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\u2728 Sketch RNN

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\u2728 Transformers

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\u2728 Transformers

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\u2728 Uncertainty

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\u2728 \u4e0d\u786e\u5b9a\u6027

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\u2728 U-Net

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\u2728 U-Net

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\u2728 Graph Neural Networks

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\u2728 \u56fe\u5f62\u795e\u7ecf\u7f51\u7edc

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\u2728 Graph Neural Networks

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\u2728 \u56fe\u795e\u7ecf\u7f51\u7edc

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_^_0_^_

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_^_0_^_

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If you use this for academic research, please cite it using the following BibTeX entry.

\n": "

\u5982\u679c\u60a8\u5c06\u5176\u7528\u4e8e\u5b66\u672f\u7814\u7a76\uff0c\u8bf7\u4f7f\u7528\u4ee5\u4e0b BibTeX \u6761\u76ee\u5f15\u7528\u5b83\u3002

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Solving games with incomplete information such as poker with CFR.

\n": "

\u4f7f\u7528CFR\u89e3\u51b3\u4fe1\u606f\u4e0d\u5b8c\u6574\u7684\u6e38\u620f\uff0c\u4f8b\u5982\u4f7f\u7528CFR\u7684\u6251\u514b\u3002

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This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations, and the website renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better.

\n": "

\u8fd9\u662f\u795e\u7ecf\u7f51\u7edc\u548c\u76f8\u5173\u7b97\u6cd5\u7684\u7b80\u5355 PyTorch \u5b9e\u73b0\u7684\u96c6\u5408\u3002\u8fd9\u4e9b\u5b9e\u73b0\u4e0e\u89e3\u91ca\u4e00\u8d77\u8bb0\u5f55\uff0c\u7f51\u7ad9\u5c06\u8fd9\u4e9b\u5185\u5bb9\u5448\u73b0\u4e3a\u5e76\u6392\u683c\u5f0f\u7684\u6ce8\u91ca\u3002\u6211\u4eec\u76f8\u4fe1\u8fd9\u4e9b\u5c06\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u8fd9\u4e9b\u7b97\u6cd5\u3002

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We are actively maintaining this repo and adding new implementations. _^_0_^_ for updates.

\n": "

\u6211\u4eec\u6b63\u5728\u79ef\u6781\u7ef4\u62a4\u8fd9\u4e2a\u4ed3\u5e93\u5e76\u6dfb\u52a0\u65b0\u7684\u5b9e\u73b0\u3002_^_0_^_\u4ee5\u83b7\u53d6\u66f4\u65b0\u3002

\n", + "

If you use this for academic research, please cite it using the following BibTeX entry.

\n": "

\u5982\u679c\u60a8\u5c06\u6b64\u7528\u4e8e\u5b66\u672f\u7814\u7a76\uff0c\u8bf7\u4f7f\u7528\u4ee5\u4e0b BibTeX \u6761\u76ee\u8fdb\u884c\u5f15\u7528\u3002

\n", + "

Solving games with incomplete information such as poker with CFR.

\n": "

\u4f7f\u7528 CFR \u89e3\u51b3\u8bf8\u5982\u6251\u514b\u7b49\u4e0d\u5b8c\u5168\u4fe1\u606f\u6e38\u620f

\n", + "

This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations, and the website renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better.

\n": "

\u8fd9\u662f\u4e00\u4e2a\u7528 PyTorch \u5b9e\u73b0\u5404\u79cd\u795e\u7ecf\u7f51\u7edc\u548c\u76f8\u5173\u7b97\u6cd5\u7684\u96c6\u5408\u3002\u6bcf\u4e2a\u7b97\u6cd5\u7684\u4ee3\u7801\u5b9e\u73b0\u90fd\u6709\u8be6\u7ec6\u7684\u89e3\u91ca\u8bf4\u660e\uff0c\u4e14\u5728\u7f51\u7ad9\u4e0a\u4e0e\u4ee3\u7801\u9010\u884c\u5bf9\u5e94\u3002\u6211\u4eec\u76f8\u4fe1\uff0c\u8fd9\u4e9b\u5185\u5bb9\u5c06\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u8fd9\u4e9b\u7b97\u6cd5\u3002

\n", + "

We are actively maintaining this repo and adding new implementations. _^_0_^_ for updates.

\n": "

\u6211\u4eec\u6b63\u5728\u79ef\u6781\u7ef4\u62a4\u8fd9\u4e2a\u4ed3\u5e93\u5e76\u6dfb\u52a0\u65b0\u7684\u4ee3\u7801\u5b9e\u73b0_^_0_^_\u4ee5\u83b7\u53d6\u66f4\u65b0\u3002

\n", "_^_0_^_": "_^_0_^_", - "\n": "\n", + "\n": "\n", "\n": "\n", "\n": "\n", - "\n": "\n", - "\n": "\n", - "\n": "\n", - "\n": "
  • \u5728 48GB GPU \u4e0a\u751f\u6210
  • \n", - "\n": "\n", - "\n": "\n", - "\n": "
  • \u57fa\u4e8e\u5e7f\u4e49\u4f18\u52bf\u4f30\u8ba1\u7684\u8fd1\u7aef\u7b56\u7565\u4f18\u5316
  • \n", - "\n": "\n", - "\n": "\n", - "\n": "\n", - "\n": "\n", - "labml.ai Annotated PyTorch Paper Implementations": "labml.ai \u5e26\u6ce8\u91ca\u7684 pyTorch \u8bba\u6587\u5b9e\u73b0" + "\n": "\n", + "\n": "\n", + "\n": "\n", + "\n": "
  • \u5728\u4e00\u5757 48GB GPU \u4e0a\u751f\u6210
  • \n", + "\n": "\n", + "\n": "\n", + "\n": "\n", + "\n": "\n", + "\n": "\n", + "\n": "\n", + "\n": "\n", + "labml.ai Annotated PyTorch Paper Implementations": "labml.ai \u5e26\u6ce8\u91ca\u7684 PyTorch \u7248\u8bba\u6587\u5b9e\u73b0" } \ No newline at end of file