You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
|[Sequence to Sequence Learning with Neural Networks](https://arxiv.org/abs/1409.3215)|[HackMD](https://hackmd.io/@photon-dodo/HyrN0wjkv)|[Rishika](https://https://github.com/rishika2110)[Khurshed](https://https://github.com/GlazeDonuts)| This paper presents a novel architecture and paradigm for Neural Machine Translation. |
6
6
|[Neural Machine Translation by jointly learning to align and translate](https://arxiv.org/abs/1409.0473)|[HackMD](https://hackmd.io/@photon-dodo/HJfefAQbP)|[Rishika](https://https://github.com/rishika2110)[Khurshed](https://https://github.com/GlazeDonuts)| This paper introduces a novel attention based approach for translation. |
7
+
|[Answer Them All! Toward Universal Visual Question Answering Models](https://arxiv.org/abs/1903.00366)|[Notion](https://phrygian-macaroni-e3b.notion.site/Answer-Them-All-RAMEN-d441cd8797984474baeba5ce4176956d)|[Aneesh](https://sites.google.com/view/aneesh-shetye/home)| This paper tries to resolve the disparity in performance of previous Visual Question Answering (VQA) architectures on synthetic and natural datasets by introducing a novel architecture. |
0 commit comments