We benchmarked five novel edge devices:

• Nvidia Jetson Nano
• Google Coral Dev Board
• Intel Neural Compute Stick
• Raspberry Pi (upper bound reference)
• 2080ti NVIDIA GPU (lower bound reference)

We used different frameworks and models, to see which combinations perform best. In particular, we focused on performance outcomes for machine learning on the edge.

You can see the results here:

• Full article: https://tryolabs.com/blog/machine-learning-on-edge-devices-benchmark-report/
• Results only: https://tryolabs.com/blog/machine-learning-on-edge-devices-benchmark-report/#results-analysis

Edit: Added links to the hardware in question.

Hello all! I am a current student working on a Graduation project about Segways. I was asked to collect at least 100 responses in the next couple of days. Could you please help me out and fill it? There are only 12 questions and should take you about 3-4 min to complete. Your insights are very valuable to me. Thank you in advance!

https://docs.google.com/forms/d/e/1FAIpQLSe65iP2NtQqwgWU-QK_9PAvo09w5n78GUt0pqjaYKFlmtECiw/viewform?usp=sf_link

With the new release of PyTorch 1.3, they’ve added support (and libraries) to deploy PyTorch models directly to mobile devices for inference.

• Overview
• iOS support
• Android support

Hello All! I am a current student who is working on a Final Project about Segways. The requirement is to collect at least 100 survey responses in the next couple of days. Could you please help me out? There are only 12 questions and should take you about 3-4 min to complete. Your insights are very valuable to me as I don’t have any friends near me who are familiar with Segways. Thank you! The image of the segway I’m researching for is in google forms.

https://docs.google.com/forms/d/e/1FAIpQLSe65iP2NtQqwgWU-QK_9PAvo09w5n78GUt0pqjaYKFlmtECiw/viewform?usp=sf_link

For something like AlphaGo (that plays a simple board game), I understand that the neural network can output a “grid” vector the size of the board, and the largest value in the output, which is also a valid move, is the move you make*. In this case, the neural network is solving the same simple question repeatedly, “Where do I move?”. I know how to encode the answer to that question. There’s around 400 possible moves in Go, and they are fixed, so a vector of length 400 can encode every possible action.

(* Actually, AlphaGo uses the NN in a tree search. The NN does not generate moves directly.)

I don’t understand how a neural network like AlphaStar can output an answer to the much broader question “What should I do?”. The answers can be “build a building”, “kill one of your own buildings”, “build a unit”, “attack a unit”, “move 2 of your units to position”, “move 3 of your units to another positions”, “load your units into a transport”, “use one of your units special abilities”, “research a new technology”, etc.

How are the answers to such a broad question encoded? Do we know how AlphaStar does it?

I’m especially baffled by the change number of units in StarCraft. Encoding the actions 2 units can take seems significantly different than encoding the actions 3 units can take. Do they use a multi-agent setup? Is each unit running its own NN and determining its own actions individually?

Horace He looks at the data and analyzes the current state of machine learning frameworks in 2019.

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https://thegradient.pub/state-of-ml-frameworks-2019-pytorch-dominates-research-tensorflow-dominates-industry/

In addition to good training data and the right model architecture, loss functions are one of the most important parts of training an accurate machine learning model. For this post, I’d love to give developers an overview of some of the more advanced loss functions and how they can be used to improve the accuracy of models—or solve entirely new tasks.

https://heartbeat.fritz.ai/research-guide-advanced-loss-functions-for-machine-learning-models-aee68ed8a38c

Most of the major success stories in applied ML have been user-facing stuff – Facebook optimizing engagement, Bookings optimizing user-facing search, etc.

Does anyone have examples of projects that have been successfully deployed within a company? The closest thing I can think of is some companies that use face recognition for physical authentication (i.e., in lieu of key cards)

Hi all!

We (the team behind Papers With Code) have been working on a new free service: https://sotabench.com – to benchmark models directly from GitHub.

The mission of sotabench is to benchmark and map out every open source ML model. On Papers With Code we collect code implementations and results from papers, but until now we had no way of knowing if the code is sufficient to produce the claimed results. This is why we created sotabench.

We hope this is going to help practitioners and researchers alike to find high quality model implementations and gain insight into the accuracy / speed tradeoff of models out there.

We’ve implemented a couple of benchmarks for now – but we’ve made it open and free for anyone to add benchmarks and connect their repositories (including forks). If you find it useful, feel free to just go for it and add repos.

Open for suggestions and feedback!

Cheers, Robert

Like tensorflow uses Static Computation graphs. When you define your network, it prepares the whole graph data structure before running. Whereas, pytorch uses Dynamic CG, where CG is prepared dynamically when the interpreter runs the code line by line.

My questions are: 1. Is there anything conceptually wrong in the above paragraph? 2. How does Caffe2 does it?