I had a trip to Quebec city for 4 days. Behind the lingering from the travel, I prepared for the meetup this week. I could not join it because of birthday dinner with my girlfriend. However, I studied the original paper seriously, and the topic involves some interesting ideas, so I want to introduce about it. Long short term memory (LSTM) To understand the paper, precedently, need to understand LSTM. I recommend chapter 10 of the deeplearning book.

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Mark who I met in machine learning study meetup had recommended me to study a research paper about discrete variational autoencoder. I have read today. As so does variational inference, it includes many mathematical equations, but what the author wants to tell was very straightforward. Two previous posts, Variational Method, Independent Component Analysis, are relevant to the following discussion. Autoencoder To understand the paper, above all, we need to know what the autoencoder is and what variational autoencoder is.

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Stochastic Hopfield net Boltzmann machine is nothing but stochastic Hopfield net1. If you did not yet read the post of the Hopfield net in the blog, just go read it. I assume the readers are familiar to it, and directly use many results we had in the post. The magic of deep learning which we have discussed a couple of times works here, too. Such as $\epsilon$-greedy off-policy algorithm2, the stochastic character of the binary units allows the machine occasionally increase its energy to escape from poor local minima.

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Bike algorithm I like riding a bike. Because my knee are not healthy now, I cannot ride a bike much. However, even little riding makes me feel good. Today it occurs to me if I am addicted at riding a bike. During riding a bike, I was thinking about the algorithm commands me. Silly me thought it is funny. Thus, I tried to make a flowchart of the algorithm. Tried to use some online service to draw it.

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Judgement Day It is the first time I did not post for 4 days. I was too busy to prepare for the meetup this week. The day before yesterday meetup topic was the reinforcement learning as I mentioned at previous post. It is not a long research paper, but includes 143 references. Ah, not my favorite. This A Brief Survey of Deep Reinforcement Learning did not explain the detail of what I am interested in.

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