Add paper year

README.md

@@ -221,17 +221,17 @@ The automatic workflow may not suite the research workflow of all Quant research
 # [Quant Model Zoo](examples/benchmarks)
 
 Here is a list of models built on `Qlib`.
-- [GBDT based on LightGBM (Guolin Ke, et al.)](qlib/contrib/model/gbdt.py)
-- [GBDT based on Catboost (Liudmila Prokhorenkova, et al.)](qlib/contrib/model/catboost_model.py)
-- [GBDT based on XGBoost (Tianqi Chen, et al.)](qlib/contrib/model/xgboost.py)
+- [GBDT based on XGBoost (Tianqi Chen, et al. 2016)](qlib/contrib/model/xgboost.py)
+- [GBDT based on LightGBM (Guolin Ke, et al. 2017)](qlib/contrib/model/gbdt.py)
+- [GBDT based on Catboost (Liudmila Prokhorenkova, et al. 2017)](qlib/contrib/model/catboost_model.py)
 - [MLP based on pytorch](qlib/contrib/model/pytorch_nn.py)
-- [GRU based on pytorch (Kyunghyun Cho, et al.)](qlib/contrib/model/pytorch_gru.py)
-- [LSTM based on pytorch (Sepp Hochreiter, et al.)](qlib/contrib/model/pytorch_lstm.py)
-- [ALSTM based on pytorch (Yao Qin, et al.)](qlib/contrib/model/pytorch_alstm.py)
-- [GATs based on pytorch (Petar Velickovic, et al.)](qlib/contrib/model/pytorch_gats.py)
-- [SFM based on pytorch (Liheng Zhang, et al.)](qlib/contrib/model/pytorch_sfm.py)
-- [TFT based on tensorflow (Bryan Lim, et al.)](examples/benchmarks/TFT/tft.py)
-- [TabNet based on pytorch (Sercan O. Arik, et al.)](qlib/contrib/model/pytorch_tabnet.py)
+- [LSTM based on pytorch (Sepp Hochreiter, et al. 1997)](qlib/contrib/model/pytorch_lstm.py)
+- [GRU based on pytorch (Kyunghyun Cho, et al. 2014)](qlib/contrib/model/pytorch_gru.py)
+- [ALSTM based on pytorch (Yao Qin, et al. 2017)](qlib/contrib/model/pytorch_alstm.py)
+- [GATs based on pytorch (Petar Velickovic, et al. 2017)](qlib/contrib/model/pytorch_gats.py)
+- [SFM based on pytorch (Liheng Zhang, et al. 2017)](qlib/contrib/model/pytorch_sfm.py)
+- [TFT based on tensorflow (Bryan Lim, et al. 2019)](examples/benchmarks/TFT/tft.py)
+- [TabNet based on pytorch (Sercan O. Arik, et al. 2019)](qlib/contrib/model/pytorch_tabnet.py)
 
 Your PR of new Quant models is highly welcomed.