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Representation Learning

501 papers with code · Methodology

Representation learning is concerned with training machine learning algorithms to learn useful representations, e.g. those that are interpretable, have latent features, or can be used for transfer learning.

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Latest papers with code

Heterogeneous Network Representation Learning: Survey, Benchmark, Evaluation, and Beyond

1 Apr 2020yangji9181/HNE

Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic superclass of traditional homogeneous networks (graphs).

NETWORK EMBEDDING

10
01 Apr 2020

Look-into-Object: Self-supervised Structure Modeling for Object Recognition

31 Mar 2020JDAI-CV/LIO

Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category.

OBJECT DETECTION OBJECT RECOGNITION REPRESENTATION LEARNING

11
31 Mar 2020

DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction

31 Mar 2020jacklin18/DeepGS

Recently, with the increasing amount of affinity data available and the success of deep representation learning models on various domains, deep learning techniques have been applied to DTA prediction.

DRUG DISCOVERY REPRESENTATION LEARNING

0
31 Mar 2020

Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions

30 Mar 2020matheusgadelha/PointCloudLearningACD

In this work, we investigate the use of Approximate Convex Decompositions (ACD) as a self-supervisory signal for label-efficient learning of point cloud representations.

UNSUPERVISED REPRESENTATION LEARNING

1
30 Mar 2020

Gossip and Attend: Context-Sensitive Graph Representation Learning

30 Mar 2020zekarias-tilahun/goat

In this study we show that in-order to extract high-quality context-sensitive node representations it is not needed to rely on supplementary node features, nor to employ computationally heavy and complex models.

COMMUNITY DETECTION GRAPH REPRESENTATION LEARNING LINK PREDICTION

1
30 Mar 2020

Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds

29 Mar 2020raoyongming/PointGLR

Based on this hypothesis, we propose to learn point cloud representation by bidirectional reasoning between the local structures at different abstraction hierarchies and the global shape without human supervision.

3D OBJECT CLASSIFICATION OBJECT CLASSIFICATION UNSUPERVISED REPRESENTATION LEARNING

11
29 Mar 2020

K-Core based Temporal Graph Convolutional Network for Dynamic Graphs

22 Mar 2020jhljx/CTGCN

Graph representation learning is a fundamental task of various applications, aiming to learn low-dimensional embeddings for nodes which can preserve graph topology information.

GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING LINK PREDICTION

4
22 Mar 2020

End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning

21 Mar 2020cjy1992/detect-loc-map

Current autonomous driving systems are composed of a perception system and a decision system.

AUTONOMOUS DRIVING REPRESENTATION LEARNING

6
21 Mar 2020

NeuCrowd: Neural Sampling Network for Representation Learning with Crowdsourced Labels

21 Mar 2020crowd-data-mining/NeuCrowd

Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, small city, education, etc.

REPRESENTATION LEARNING

0
21 Mar 2020

Pre-trained Models for Natural Language Processing: A Survey

18 Mar 2020tomohideshibata/BERT-related-papers

Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era.

REPRESENTATION LEARNING

995
18 Mar 2020