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Annotated Papers |
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Deep Learning is a vast field of research and understanding the nitty-gritty details of deep learning model helps an individual to become a better researcher and a Deep Learning Practitioner. With 10000+ Deep Learning paper getting published every year it becomes quite stressful to keep track of all the research.
Thus, the objective to annotate paper is to understand the important details of the paper like the Objective function, optimization technique and related topics.
1. Invariant Information Clustering for Unsupervised Image Classification and Segmentation
Tags: Entropy Maximization, Conditional Entropy Maximization, Bottleneck, Biclustering, Spatio-Temporal, Geometric Transformation, Mutual Information, Joint Entropy, Joint Distribution
1. Deep Residual Learning for Image Recognition
Tags: Highway Network, Degradation Problem, Identity Shortcut, Resnet Slides
2. [Understanding the Role of Individual Units in a Deep Neural Networks](https://github.com/Mayurji/Deep-Learning-Papers/tree/master/Investigate DNN/)
Note
All annotations for Deep Learning Papers. Some of the paper has been implemented based on compute power available.