Update README.md

Author: slackerjerry

README.md

@@ -141,13 +141,8 @@ views of the funding agencies.
 
 The experiments in this work benefit from the following open-source codes:
 * Ramsauer, Hubert, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Thomas Adler, Lukas Gruber et al. "Hopfield networks is all you need." arXiv preprint arXiv:2008.02217 (2020). https://github.com/ml-jku/hopfield-layers
-* Martins, Andre, and Ramon Astudillo. "From softmax to sparsemax: A sparse model of attention and multi-label classification." In International conference on machine learning, pp. 1614-1623. PMLR, 2016. [add sparsemax source code]
-* Correia, Gonçalo M., Vlad Niculae, and André FT Martins. "Adaptively sparse transformers." arXiv preprint arXiv:1909.00015 (2019). https://github.com/deep-spin/entmax
+* Martins, Andre, and Ramon Astudillo. "From softmax to sparsemax: A sparse model of attention and multi-label classification." In International conference on machine learning, pp. 1614-1623. PMLR, 2016. https://github.com/KrisKorrel/sparsemax-pytorch
+* Correia, Gonçalo M., Vlad Niculae, and André FT Martins. "Adaptively sparse transformers." arXiv preprint arXiv:1909.00015 (2019). https://github.com/deep-spin/entmax & https://github.com/prajjwal1/adaptive_transformer
 * Ilse, Maximilian, Jakub Tomczak, and Max Welling. "Attention-based deep multiple instance learning." In International conference on machine learning, pp. 2127-2136. PMLR, 2018. https://github.com/AMLab-Amsterdam/AttentionDeepMIL
 * Zhang, Yunhao, and Junchi Yan. "Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting." In The Eleventh International Conference on Learning Representations. 2022. https://github.com/Thinklab-SJTU/Crossformer
 * Millidge, Beren, Tommaso Salvatori, Yuhang Song, Thomas Lukasiewicz, and Rafal Bogacz. "Universal hopfield networks: A general framework for single-shot associative memory models." In International Conference on Machine Learning, pp. 15561-15583. PMLR, 2022. https://github.com/BerenMillidge/Theory_Associative_Memory
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-### We appreciate the following works for their valuable code and data:
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-[https://github.com/KrisKorrel/sparsemax-pytorch/blob/master/sparsemax.py] <br>
-[https://github.com/prajjwal1/adaptive_transformer/blob/master/models/entmax.py] <br>