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Implementation of Poincaré Embeddings for Learning Hierarchical Representations(Facebook Research)
(github.com)
Corresponding blog post: https://medium.com/towards-data-science/facebook-research-just-published-an-awesome-paper-on-learning-hierarchical-representations-34e3d829ede7
Corresponding paper: https://arxiv.org/abs/1705.08039
This paper explores Poincare disk model instead of Euclidean space for ...
This is a PyTorch implementation of the NIPS-17 paper [Poincaré Embeddings for Learning Hierarchical Representations](https://papers.nips.cc/paper/7213-poincare-embeddings-for-learning-hierarchical-representations)
Abstract:
> The ability to generate natural language sequences from source code snippets can be
used for code summarization, documentation, and retrieval. Sequence-to-sequence
(seq2seq) models, adopted from neural machine translation (NMT), have achieved
state-of-the-art performance on these t...
Abstract: "A representation can be seen as a set of variables, known as features, that describe a phenomenon. Machine
learning (ML) algorithms make use of these representations to achieve the task they are designed for, such
as classification, clustering or sequential decision making. ML algorithm...
original paper https://systemerrorwang.github.io/White-box-Cartoonization/paper/06791.pdf