Category Archives: Machine Learning

Compare Among Popular Machine Reading Comprehension Datasets

This is a quick guide for people who newly joined the Machine Reading Comprehension army. Here I’ll give you some advice about which dataset to start with. ABOUT MRC Teaching machines to read is a non-negligible part of ‘True AI’, people are making progress since the renaissance of deep learning, however, we’re not even close, the state-of-the-art models […]

Also posted in Deep Learning, Machine Reading Comprehension, NLP | Tagged , , , , , , , | 2 Responses

Deep Neural Network Framework in CUDA

Hey guys, long time no see! I’m happy to show you the project I’m working on recently. I transplanted my convolutional neural network implementation into GPU environment, and made a deep neural network framework in nVidia CUDA. Although it is not completely finished, most of the functions are available for use. You are more than welcome […]

Also posted in Twaddle | Tagged , | 3 Responses

Recurrent Neural Networks II — LSTM

In my previous post, I introduced the basic ideas of Recurrent Neural Networks, as the 2nd post of RNNs, we’ll focus on long short-term memory method. LONG SHORT TERM MEMORY One of the very famous problems of RNNs is the vanishing gradient, the problem is that the influence of a given input on the hidden layer, […]

Also posted in NLP, OpenCV | Tagged , , , , , , , , | 6 Responses

Recurrent Neural Networks I

RNNs Recurrent neural networks are very famous recently, they play as important roles as convolutional neural networks. RNNs can use their internal memory to process arbitrary sequences of inputs, so not only images, they work well on speech recognition and natural language processing tasks. There are several type of RNNs, as the beginning, we focus our attention on […]

Also posted in NLP | Tagged , , , | 6 Responses

Named-Entity Recognition using Deep Learning

NAMED ENTITY RECOGNITION In Natural Language Processing, named-entity recognition is a task of information extraction that seeks to locate and classify elements in text into pre-defined categories. The following graph is stolen from Maluuba Website, it perfectly demonstrates what does NER do.  

Also posted in NLP | Tagged , , , , , | 4 Responses

Convolutional Neural Networks III

Hey, I’m recently working on my new version of CNN, the updates are as follows: Support 3-channels images; Add Dropout; In conv layers, one can use either 3-channel conv kernels or single-chanel conv kernels (that is to say, whether share weights). Now I’ve finished most of the works, and I’m debugging the code, hope I can release it […]

Posted in Machine Learning | Tagged | 53 Responses

Sparse Coding

INTRODUCTION Sparse coding is one of the very famous unsupervised methods in this decade, it is a dictionary learning process, which target is to find a dictionary that we can use a linear combination of vectors in this dictionary to represent any training input vector. For better capture structures and patterns inherent in the input vectors, […]

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Restricted Boltzmann Machine

WHAT IS RBM Restricted Boltzmann Machine is one of the special cases of Boltzmann Machine, which restricted all visible-visible connections and hidden-hidden connections, which makes for each hidden unit, it connects to all visible units, and for each visible unit, it connects to all hidden units. Following is a figure which shows the model of RBM.

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Clustering by fast search and find of density peaks

This post is about a new cluster algorithm published by Alex Rodriguez and Alessandro Laio in the latest Science magazine. The method is short and efficient, I implemented it using about only 100 lines of cpp code. BASIC METHOD There are two leading criteria in this method: Local Density and Minimum Distance with higher density.  Rho above is the […]

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Denoising Autoencoder

I chose “Dropped out auto-encoder” as my final project topic in the last semester deep learning course, it was simply dropping out units in regular sparse auto-encoder, and furthermore, in stacked sparse auto-encoder, both in visible layer and hidden layer. It does not work well on auto-encoders, except can be used in fine-tune process of stacked sparse […]

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