More citations (microsoft#673)

* check lexsort

* check lexsort

* lexsort comment

* lexsort comment

* fix GPU identification bug

* Create README.md

* Update README.md

* Create README.md

* Create README.md

* Update README.md

* Create README.md

* Update README.md

* Update README.md

* Create README.md

* Create README.md

* Create README.md

README.md

@@ -283,7 +283,7 @@ Here is a list of models built on `Qlib`.
 - [GBDT based on LightGBM (Guolin Ke, et al. NIPS 2017)](examples/benchmarks/LightGBM/)
 - [GBDT based on Catboost (Liudmila Prokhorenkova, et al. NIPS 2018)](examples/benchmarks/CatBoost/)
 - [MLP based on pytorch](examples/benchmarks/MLP/)
-- [LSTM based on pytorch (Sepp Hochreiter, et al. Neural omputation 1997)](examples/benchmarks/LSTM/)
+- [LSTM based on pytorch (Sepp Hochreiter, et al. Neural computation 1997)](examples/benchmarks/LSTM/)
 - [GRU based on pytorch (Kyunghyun Cho, et al. 2014)](examples/benchmarks/GRU/)
 - [ALSTM based on pytorch (Yao Qin, et al. IJCAI 2017)](examples/benchmarks/ALSTM)
 - [GATs based on pytorch (Petar Velickovic, et al. 2017)](examples/benchmarks/GATs/)

examples/benchmarks/GRU/README.md

@@ -0,0 +1,2 @@
+# Gated Recurrent Unit (GRU)
+* Paper: [Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation](https://aclanthology.org/D14-1179.pdf).

examples/benchmarks/LSTM/README.md

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+# Long Short-Term Memory (LSTM)
+* Paper: [Long Short-Term Memory](https://direct.mit.edu/neco/article-abstract/9/8/1735/6109/Long-Short-Term-Memory?redirectedFrom=fulltext).

examples/benchmarks/Localformer/README.md

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+# Localformer

examples/benchmarks/MLP/README.md

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+# Multi-Layer Perceptron (MLP)

examples/benchmarks/TCN/README.md

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+# TCN
+* Code: [https://github.com/locuslab/TCN](https://github.com/locuslab/TCN)
+* Paper: [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://arxiv.org/abs/1803.01271).
+

examples/benchmarks/TabNet/README.md

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+# TabNet
+* Code: [https://github.com/dreamquark-ai/tabnet](https://github.com/dreamquark-ai/tabnet)
+* Paper: [TabNet: Attentive Interpretable Tabular Learning](https://arxiv.org/pdf/1908.07442.pdf).

examples/benchmarks/Transformer/README.md

@@ -0,0 +1,3 @@
+# Transformer
+* Code: [https://github.com/tensorflow/tensor2tensor](https://github.com/tensorflow/tensor2tensor)
+* Paper: [Attention is All you Need](https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf).