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awesome-free-deep-learning-papers
Free deep learning papers
https://github.com/HFTrader/awesome-free-deep-learning-papers
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- [pdf - in-the-blank templates designed to gather targeted descriptions about: people and objects, their appearances, activities, and interactions, as well as inferences about the general scene or its broader context.
- [pdf - critic, surpasses the current state-of-the-art on the Atari domain while training for half the time on a single multi-core CPU instead of a GPU. Furthermore, we show that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.
- [pdf - network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games.
- [pdf - to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one
- [pdf - level image segmentation, utilizes the supervised learning approach to map the regional feature vector to a saliency score.
- [pdf - of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with imagelevel attribute tags, their response maps over entire images have strong indication of face locations.
- [pdf - source project, and it has become widely used for machine learning research.
- [pdf - dimensional arrays efficiently. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers especially in the machine learning community and has shown steady performance improvements.
- [pdf - based probabilistic graphical modelling. To this end, we formulate mean-field approximate inference for the Conditional Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks.
- [pdf - to-use MATLAB functions, providing routines for computing linear convolutions with filter banks, feature pooling, and many more. This document provides an overview of CNNs and how they are implemented in MatConvNet and gives the technical details of each computational block in the toolbox.
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