I just wrote an article (quite long) about how we’ve build a semantic similarity index alongside the ElasticSearch and used both to provide smarter search results.

Tools used:

• Tensorflow and Simple Tensorflow Serving
• Pre-trained Universal Sentence Encoder
• Docker
• Python 3
• FAISS by Facebook
• ElasticSearch
• gRPC for the API

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Link to the article: https://blog.onebar.io/building-a-semantic-search-engine-using-open-source-components-e15af5ed7885

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I hope, this would be helpful as a practical “how-to” for anyone, trying to build a semantic search engine for their project.

https://www.eventbrite.com/e/pytorch-summer-hackathon-in-menlo-park-registration-63756668913

The eventbrite website will give you a welcome message, but you have to wait until your application is reviewed by Facebook staff and give you a confirmation message. Space is limited and not all applicants will be admitted into the hackathon.

Btw, to anyone who got in, the official website send via email is officially up now (it was down earlier this week), you can use it to find teammates.

I have written a slightly sarcastic but mostly research focused blog post on issues with neural dialog models:
https://medium.com/@richardcsaky/neural-chatbots-are-dumb-65b6b40e9bd4
I also present a simple improvement (from my ACL paper this year), through a demo and some nice visualizations.
Check it out if it spiked your interest!

I’ll be teaching a tutorial on GAN with tensor flow. While I’m well versed in the math. I really want to make it easy enough for undergrads that may never have heard about GAN (let’s assume they know Python/Keras and some ML).

Do you have any favorite tutorials out there, either blog post or video.

Just wonder what are the best practices or role models for research management as a group? Research group activities include properly selecting research direction, balancing risk and feasibility, converting research findings into product, optimizing organizational structures, and performance measure etc., but these activities could easily go wild and out-of-control in practice due to the volatile nature of research. Which organization demonstrated most successful research management in history, and are there any key patterns a research group needs to follow in order to reach higher productivity?

I have made a small experiment to convert each network layer gradient norm into a tone in order to synthesize sound from it during training, there are some samples here: http://blog.christianperone.com/2019/08/listening-to-the-neural-network-gradient-norms-during-training/, if someone is interested.

I have an idea that solves a frustration I got while working on various machine learning projects which I think is quite common. I’m curious what others think before I start building the solution. I’m curious to hear all your suggestions and feedback as I want to create a tool that can be beneficial for as many people in ML as possible. First let me describe the problem and then propose my solution.

During several machine learning endeavors, we often quickly ran into scaling and operation issues: frequently it felt as though keeping (preprocessing) pipelines working correctly for different datasets, optimizing hyperparameters (cost-) effectively and managing tests and deployments was taking much more time than the actual development of the individual pieces. Lots of glue code and extremely precise documentation was necessary to keep experiments easily reproducible. Although individual processes were often relatively simple to grasp, properly managing them to work in concert (in various ways) was extremely time-consuming and tedious.

As a solution to this problem I came up with an idea for an application: what if you could manage the different components of the pipelines (let’s call them modules) and hook them up in a GUI similar to Apple’s Quartz Composer. Several commonly used modules can directly be used (so even without any coding experience!), but the user can also write their own Python scripts that can be interacted with using the application. A simple associated Python library aims to create some consistency in input and outputs of Python scripts which enables them to be used in the GUI. The application would also enable users to easily manage deployments of training sessions, tests and inference endpoints on various cloud providers, local compute, or other computers over SSH. Hyperparameter tuning can also be done through modules. Basically the entire process from raw data to usable models for inference can be streamlined in visual pipelines.

I have seen similar tools, but they are often not as extensive as my idea, and sometimes suffer from vendor lock-in issues (such as with Google AutoML). If I’d build this application I might even do so open source, which would also speed up development.

As said, I’m extremely curious what y’all think. I am interested to hear suggestions, comments or similar ideas. Thanks a lot in advance!

Hello I want to create a neural network for the game Ult. Tic tac Toe. It is my first neural network that I will create. Is my approach good? I want to have 90 inputs to the layer(81 representing the sub boards and 9 the global boards, -1 for occupied by O, 0 for empty and 1 for occupied by X). I want to include one or two hidden layers with 40 nodes each(Sigmoid function). The output layer has 1 output node ranging from [-1,1] representing 1 that X will win and -1 that O will win.

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I want to play the neural network against itself. I want to have a dataset of 50 games. Then I want to interpolate each board state with the probability of winning. For example X won in 20 rounds, initial board has target output of 0 the next states 0.05, 0.1,0.15……, then backpropagate. What do you think?

Hi,

I recently got a paper accepted at ICCV as an oral presentation. I’m super stoked about this but unfortunately struggling to find money to pay for my trip.

I was a post-doc when I worked on the project and submitted the work. I have now left to industry. My present company won’t pay for me to attend for reasons. The previous grant I was employed on is “running low” on its travel budget so they will not assist. If I were still a post-doc, I don’t think this would be an issue..

My ex-boss said she would look for some money, maybe her discretionary fund but I’m not hopeful. This leaves with me self-funding the entire trip, which is looking around $2000.

Are there any avenues I can explore for help? Have others been in this situation and managed to salvage some money?

Thanks!