<jupyter_start><jupyter_text>Topic: NMT Re-Ranking and Paraphrase Detection Using TreeLSTM-based Pointer Network and Attention Mechanism on Dependency Trees  NMT Re-RankingInstead of solely relying on an integrated search for the best translation, we may introduce a second decoding pass in which the best translation is chosen from the set of the most likely candidates generated by a traditional decoder. This allows more features or alternate decision rules. - [Reranking (Statmt)](http://www.statmt.org/survey/Topic/Reranking)- [Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models (<NAME> et al.)](https://arxiv.org/abs/2009.13267)- [Neural Machine Translation: A Review and Survey (<NAME>)](https://arxiv.org/pdf/1912.02047.pdf)- [A Comparable Study on Model Averaging, Ensembling and Reranking in NMT (Y. Liu et al.)](https://www.semanticscholar.org/paper/A-Comparable-Study-on-Model-Averaging%2C-Ensembling-Liu-Zhou/1df3667e81e37fd6fcb9485514622b9e78fa8161?sort=relevance&page=2)   Study Resources- [Unsupervised Sub-tree Alignment for Tree-to-Tree Translation (paper)](https://www.jair.org/index.php/jair/article/view/10850/25893)- [PyTorch for Deep Learning - Full Course (FreeCodeCamp Video)](https://www.youtube.com/watch?v=GIsg-ZUy0MY&t=205s)- [Jovian.ai  Notebooks](https://jovian.ai/aakashns/03-logistic-regression)- [Understanding LSTMs (Colah)](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)- [Attention Mechanism (Wikidocs 딥 러닝을 이용한 자연어 처리 입문)](https://wikidocs.net/22893)- [BLEU (Wikidocs)](https://wikidocs.net/31695)- [TreeLSTM (Slideshare)](https://www.slideshare.net/tuvistavie/tree-lstm)- [TreeLSTM pytorch implementation](https://github.com/dasguptar/treelstm.pytorch/blob/master/treelstm/dataset.py)- [Dependency vs Constituency Parsing](https://www.baeldung.com/cs/constituency-vs-dependency-parsing)- [Pointer Networks 논문 리뷰 (티스토리)](https://ropiens.tistory.com/57)  Dependency Tree-LSTM Structure       Activation/Loss Functions- [Why Sigmoid?](https://stats.stackexchange.com/questions/162988/why-sigmoid-function-instead-of-anything-else/318209318209)- [Why Softmax?](https://stackoverflow.com/questions/17187507/why-use-softmax-as-opposed-to-standard-normalization)- [Cross Entropy](https://machinelearningmastery.com/cross-entropy-for-machine-learning) Uncategorized- [NYU Drive (ms9144)](https://drive.google.com/drive/folders/1zhXsGz1C9AikH6t1ADj0IosTQGaVn9kV?usp=sharing)- [Neural Networks, Manifolds, and Topology (colah)](http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/)- [How to make a C/C++ extension to Python](https://medium.com/@matthiasbitzer94/how-to-extend-python-with-c-c-code-aa205417b2aa)- [Git 서브모듈](https://ohgyun.com/711)- [deeplearningtheory.com (physics-based interpretations)](https://deeplearningtheory.com/PDLT.pdf)- [Graph neural networks: A review of methods and applications (Elsevier paper)](https://arxiv.org/ftp/arxiv/papers/1812/1812.08434.pdf)- [Boyd CVXOPT (book, slides)](https://web.stanford.edu/~boyd/cvxbook/)  Some Combinatorial Problems $$\begin{align*}    \mathbf{u}_i &= A \mathbf{c}_i & \; i = 1, \ldots, n \\n    \mathbf{v} &= B \mathbf{q} \\n    \mathbf{p} &= \text{softmax}_i(\mathbf{v}^T \mathbf{u}_i) \\n    \mathbf{z} &= \sum_i p_i \mathbf{c}_i \end{align*}$$<jupyter_code>#<jupyter_output><empty_output>
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