Recent Advances in Deep Learning: An Overview

@article{Minar2018RecentAI,
  title={Recent Advances in Deep Learning: An Overview},
  author={Matiur Rahman Minar and Jibon Naher},
  journal={ArXiv},
  year={2018},
  volume={abs/1807.08169}
}
Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. It is also one of the most popular scientific research trends now-a-days. Deep learning methods have brought revolutionary advances in computer vision and machine learning. Every now and then, new and new deep learning techniques are being born, outperforming state-of-the-art machine learning and even existing deep learning techniques. In recent years, the world has seen many major breakthroughs… 
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84 Citations
Highly Influential Citations
3
Background Citations
42
Methods Citations
16
Results Citations
1

84 Citations

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References

SHOWING 1-10 OF 167 REFERENCES

Recent advances in convolutional neural networks

Deep learning in neural networks: An overview

On the Origin of Deep Learning

This paper primarily focuses on the precedents of the models above, examining how the initial ideas are assembled to construct the early models and how these preliminary models are developed into their current forms.

Deep Learning: A Critical Appraisal

Ten concerns for deep learning are presented, and it is suggested that deep learning must be supplemented by other techniques if the authors are to reach artificial general intelligence.

An Overview of Deep-Structured Learning for Information Processing

This paper develops a classificatory scheme to analyze and summarize major work reported in the deep learning literature, and provides a taxonomy-oriented survey on the existing deep architectures, and categorize them into three types: generative, discriminative, and hybrid.

Deep Learning of Representations: Looking Forward

This paper proposes to examine some of the challenges of scaling deep learning algorithms to much larger models and datasets, reducing optimization difficulties due to ill-conditioning or local minima, designing more efficient and powerful inference and sampling procedures, and learning to disentangle the factors of variation underlying the observed data.

Deep learning in remote sensing: a review

The challenges of using deep learning for remote sensing data analysis are analyzed, the recent advances are reviewed, and resources are provided to make deep learning in remote sensing ridiculously simple to start with.

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

This paper reviews significant deep learning related models and methods that have been employed for numerous NLP tasks and provides a walk-through of their evolution.

Representation Learning: A Review and New Perspectives

Recent work in the area of unsupervised feature learning and deep learning is reviewed, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks.

Deep Learning of Representations

This chapter reviews the main motivations and ideas behind deep learning algorithms and their representation-learning components, as well as recent results, and proposes a vision of challenges and hopes on the road ahead, focusing on the questions of invariance and disentangling.
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