Suppose you have a problem that you like to tackle with machine learning and use the resulting system in a real-life project. I like to share my simple pathway for such purpose, in order to provide a basic guide to beginners and keep these things as a reminder to myself. These rules are tricky since even-thought … Continue reading Short guide to deploy Machine Learning
Selfies are everywhere. With different fun masks, poses and filters, it goes crazy. When we coincide with any of these selfies, we automatically give an intuitive score regarding the quality and beauty of the selfie. However, it is not really possible to describe what makes a beautiful selfie. There are some obvious attributes but they are not fully prescribed. With … Continue reading Selfai: A Method for Understanding Beauty in Selfies
Paper: https://arxiv.org/pdf/1611.03530v1.pdf This paper states the following phrase. Traditional machine learning frameworks (VC dimensions, Rademacher complexity etc.) trying to explain how learning occurs are not very explanatory for the success of deep learning models and we need more understanding looking from different perspectives. They rely on following empirical observations; Deep networks are able to learn … Continue reading Paper review - Understanding Deep Learning Requires Rethinking Generalization
A crucial problem in a real DL system design is to capture test data distribution with the trained model which only sees the training data distribution. Therefore, it is always important to find a good data splitting scheme which at least gives the right measures to such divergence. It is always a waste to spend … Continue reading Important Nuances to Train Deep Learning Models.
Please ping me if you know something more. Multi-view Face Detection Using Deep Convolutional Neural Network Train face classifier with face (> 0.5 overlap) and background (<0.5 overlap) images. Compute heatmap over test image scaled to different sizes with sliding window Apply NMS . Computation intensive, especially for CPU. http://arxiv.org/abs/1502.02766 From Facial Parts Responses … Continue reading Face Detection by Literature
A successful AI agent should communicate. It is all about language. It should understand and explain itself in words in order to communicate us. All of these spark with the "meaning" of words which the atomic part of human-wise communication. This is one of the fundamental problems of Natural Language Processing (NLP). "meaning" is described … Continue reading Why do we need better word representations ?
<Please let me know if there are more works comparable to these below.> R-CNN minus R http://arxiv.org/pdf/1506.06981.pdf FasterRCNN (Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks) Keywords: RCNN, RoI pooling, object proposals, ImageNet 2015 winner. PASCAL VOC2007: 73.2% PASCAL VOC2012: 70.4% ImageNet Val2 set: 45.4% MAP Model agnostic State of art with … Continue reading Object Detection Literature
As we witness the golden age of AI underpinned by deep learning, there are many different tools and frameworks continuously proposed. Sometimes it is even hard to catch up what is going on. You choose one over another then you see a new library and you go for it. However, it seems the exact choice is oblivious to anyone. According … Continue reading Comparison of Deep Learning Libraries After Years of Use
paper: http://arxiv.org/pdf/1511.07543v3.pdf code : https://github.com/yixuanli/convergent_learning This paper is an interesting work which tries to explain similarities and differences between representation learned by different networks in the same architecture. To the extend of their experiments, they train 4 different AlexNet and compare the units of these networks by correlation and mutual information analysis. They asks following … Continue reading Paper review: CONVERGENT LEARNING: DO DIFFERENT NEURAL NETWORKS LEARN THE SAME REPRESENTATIONS?
paper: http://arxiv.org/abs/1511.06422 code: https://github.com/yobibyte/yobiblog/blob/master/posts/all-you-need-is-a-good-init.md This work proposes yet another way to initialize your network, namely LUV (Layer-sequential Unit-variance) targeting especially deep networks. The idea relies on lately served Orthogonal initialization and fine-tuning the weights by the data to have variance of 1 for each layer output. The scheme follows three stages; Initialize weights by unit … Continue reading Paper review: ALL YOU NEED IS A GOOD INIT