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On Geodesic Distances and Contextual Embedding Compression for Text Classification
[article]
2021
arXiv
pre-print
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We investigate the efficacy of projecting contextual embedding data (BERT) onto a manifold, and using nonlinear dimensionality reduction techniques to compress these embeddings. ...
On one dataset, we find that despite a 12-fold dimensionality reduction, the compressed embeddings performed within 0.1% of the original BERT embeddings on a downstream classification task. ...
In addition, we would like to thank our deep learning professor Joseph Redmon for inspiring this project. ...
arXiv:2104.11295v1
fatcat:533ujtqwrrbp7i2ffuyi66ouqm
Deep learning of knowledge graph embeddings for semantic parsing of Twitter dialogs
2014
2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
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A deep neural network approach known as Deep Structured Semantic Modeling (DSSM) is used to scale the approach to learn neural embeddings for all of the concepts (pages) of Wikipedia. ...
This paper presents a novel method to learn neural knowledge graph embeddings. The embeddings are used to compute semantic relatedness in a coherence-based semantic parser. ...
For example, each word of a 40K-word vocabulary can be represented by a 10,306-dimentional vector using letter trigrams, giving a four-fold dimensionality reduction with few collisions. ...
doi:10.1109/globalsip.2014.7032187
dblp:conf/globalsip/HeckH14
fatcat:hrospi7f5ncmngv4p4bzuflhe4
Visualising Argumentation Graphs with Graph Embeddings and t-SNE
[article]
2021
arXiv
pre-print
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This paper applies t-SNE, a visualisation technique familiar from Deep Neural Network research to argumentation graphs by applying it to the output of graph embeddings generated using several different ...
It shows that such a visualisation approach can work for argumentation and show interesting structural properties of argumentation graphs, opening up paths for further research in the area. ...
However, once an embedding has been generated for complex data types such as graphs, words, or images, it becomes possible to use dimensionality reduction techniques to visualise them effectively. ...
arXiv:2107.00528v1
fatcat:wrq7ftvefbdofmfqg6eyzmdgrm
On Bi-gram Graph Attributes
2021
Computer and Information Science
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Furthermore, we showcase the different use-cases for the bi-gram graphs and how scalable it proves to be when dealing with large datasets. ...
We observe a vast domain of tools and algorithms that can be developed on top of the graph representation; creating such a graph proves to be computationally cheap, and much of the heavy lifting is achieved ...
K-core Dimensionality Reduction The K-core dimensionality reduction and noise reduction approach we proposed in section 3.2 demonstrated below shows outstanding results when used in classification-based ...
doi:10.5539/cis.v14n3p78
fatcat:s6q4z4kfbzghdhpj3hkearcbcm
Exploring Language Similarities with Dimensionality Reduction Technique
[article]
2019
arXiv
pre-print
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simple 2 dimensional plots. ...
models that can be used for other languages, so here I have explored the idea of representing several known popular languages in a lower dimension such that their similarities can be visualized using ...
For this task I chose Uniform Manifold Approximation and projection for dimensionality reduction. ...
arXiv:1902.06092v1
fatcat:xodikgzuqnglrlm5wgd6smfwnq
A New Distance Geometry Method for Constructing Word and Sentence Vectors
2020
Companion Proceedings of the Web Conference 2020
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We present a new methodology for producing low-dimensional word vectors based on distance geometry and dimensional reduction techniques. ...
We use these word vectors in order to construct sentence vectors. We evaluate their usefulness in a sentence classification task performed by a simple artificial neural network. ...
known methodologies for graph realization and dimensional reduction to NLP, and more specifically to graph embeddings for neural networks. ...
doi:10.1145/3366424.3391267
dblp:conf/www/Liberti20
fatcat:j2ov6mtpnfcgdcnijh64pym7gu
A Modified Isomap Approach to Manifold Learning in Word Spotting
[chapter]
2015
Lecture Notes in Computer Science
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In this paper, we propose a new approach to reducing the dimensionality of word image descriptors which is based on a modified version of the Isomap Manifold Learning algorithm. ...
Word spotting is an effective paradigm for indexing document images with minimal human effort. ...
For the George Washington dataset, the modified Isomap algorithm is able to achieve the same mAP values compared to no dimension reduction at an embedding dimensionality of 16. ...
doi:10.1007/978-3-319-24947-6_44
fatcat:pq4koge7yjadvdmmleelfpbhrq
Diffusion maps and coarse-graining: a unified framework for dimensionality reduction, graph partitioning, and data set parameterization
2006
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We provide evidence that nonlinear dimensionality reduction, clustering, and data set parameterization can be solved within one and the same framework. ...
We show that clustering in embedding spaces is equivalent to compressing operators. ...
Coifman for his insight and guidance, M. Maggioni and B. Nadler for contributing in the development of the diffusion framework, and Y. Keller for providing comments on the manuscript. ...
doi:10.1109/tpami.2006.184
pmid:16929727
fatcat:zwuu2xg6rvbsfovkvnsksgvgrm
Fast and Accurate Network Embeddings via Very Sparse Random Projection
[article]
2019
arXiv
pre-print
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FastRP is proposed as a scalable algorithm for network embeddings. ...
We observe that most network embedding methods consist of two components: construct a node similarity matrix and then apply dimension reduction techniques to this matrix. ...
Examining the K-nearest neighbors (KNN) of a word is a common way to measure the quality of word embeddings [22] . ...
arXiv:1908.11512v1
fatcat:j4dblin5wfabnffadp42dmt7bm
Knowledge Fusion via Embeddings from Text, Knowledge Graphs, and Images
[article]
2017
arXiv
pre-print
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Different basic fusion methods are evaluated on existing embedding approaches to show the potential of joining knowledge about certain concepts across modalities in a fused concept representation. ...
We present a baseline approach for cross-modal knowledge fusion. ...
Apart from simple concatenation we build on a set of existing methods like SVD and PCA for dimensionality reduction. ...
arXiv:1704.06084v1
fatcat:kgmi5hb4cnbsjp4ctzabqm7nzi
On Bi-gram Graph Attributes
[article]
2021
arXiv
pre-print
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Furthermore, we showcase the different use-cases for the bi-gram graphs and how scalable it proves to be when dealing with large datasets. ...
We observe a vast domain of tools and algorithms that can be developed on top of the graph representation; creating such a graph proves to be computationally cheap, and much of the heavy lifting is achieved ...
Graph Coloring POS and NER Interrelations
K-core Dimensionality Reduction The K-core dimensionality reduction and noise reduction approach we proposed in section 3.2 demonstrated below shows outstanding ...
arXiv:2107.02128v1
fatcat:bevx5liuxnehxb3lfqp2dwzgta
dg2pix: Pixel-Based Visual Analysis of Dynamic Graphs
2020
2020 Visualization in Data Science (VDS)
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The graph embedding reduces the dynamic graph to a low-dimensional representation (50-300 dimensions) and learns the similarity between graphs to capture the evolving topology of the high-dimensional data ...
The x-axis presents the temporal dimension, and the y-axis displays for each time step a graph embedding as a pixel-bar. ...
Graph Embedding In the second step, we apply dimensionality reduction methods (graph embeddings) to all generated snapshots to learn the similarity between graphs and reduce the high-dimensional data to ...
doi:10.1109/vds51726.2020.00008
fatcat:yca23ponvbfcjnmuzqyyl7mdra
Exploratory Analysis of Legal Case Citation Data Using Node Embedding
2019
ICIC Express Letters
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Suitability of node embedding for application of machine learning algorithm is demonstrated by clustering the node vectors for finding similar cases. ...
In recent years, network embedding using deep learning emerges as a promising breakthrough for analyzing networks. ...
The authors would like to thank CHRIST (Deemed to be University) for its support pertaining to this study. ...
doi:10.24507/icicel.13.10.883
fatcat:wnqvah4me5hx5eabqthqe3ftw4
Network representation learning systematic review: Ancestors and current development state
2021
Machine Learning with Applications
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2021 pre-print arXiv:2109.07583v1 fatcat:abbl3mowhrbbxpujudbfwdowpa
As part of machine learning techniques, graph embedding approaches are originally conceived for graphs constructed from feature represented datasets, like image dataset, in which links between nodes are ...
Thus, we introduce a brief history of representation learning and word representation learning ancestor of network embedding. ...
Therefore, we briefly review some of the traditional graph embedding approaches as a part of dimensionality reduction techniques. ...
doi:10.1016/j.mlwa.2021.100130
fatcat:axhg2gxkzfds3icebro6hlman4
A Deep Graph Embedding Network Model for Face Recognition
[article]
2014
arXiv
pre-print
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Secondly, many cascaded dimensionality reduction layers based on graph embedding framework are applied to GENet. Finally, a linear SVM classifier is used to classify dimension-reduced features. ...
In this paper, we propose a new deep learning network "GENet", it combines the multi-layer network architec- ture and graph embedding framework. ...
In the framework of graph embedding, we can use a unified view for understanding and explaining many of the popular dimensionality reduction algorithms, and a new dimensionality reduction algorithm called ...
arXiv:1409.7313v1
fatcat:ttz5uueycjg5xgltyexfv55l7q
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