I am doing a project on automating the task to sort rocks on a conveyor belt. My initial idea was to setup a camera and train a model (using TensorFlow) on what is a small rock, big rock, quartz rock. And so far my tabletop demo is successful. My goal is to then send the coordinates of the rock to pick out to a robotic arm that will pick out the rock at a given coordinate. Lets assume that the rock picking process is trivial.

My problem is when vendors approached us, they convinced my boss that they know better (usual vendor logic) and they say that its better if we train the machine by observing an actual operator do the work. They didnt go into much detail but when i asked them how they would do that they had vague answers that made no sense, such as “its an AI that learns which rocks to pick, by wtaching the operator it will learn”

They showed us demos of games being played like racing cars around artificial tracks, super mario etc. But from my understanding, wouldnt it be better if we could actually train the algorithm to detect what is a quartz rock and how to detect it in a camera frame and then proceed to remove the rock? I think its much harder to train the algorithm with a weird camera angle watching someone do the job and then determining which rocks to pick.

Recently i saw a user post a video on here showing a generation based car racing track. (https://www.youtube.com/watch?v=wL7tSgUpy8w) Wouldnt that be a generative model and is maybe what’s causing confusion to the vendors?

Im wondering what would be your thoughts

We are writing an article for the 3DV conference. I will cite some papers which will publish at CVPR 2019. I got this information from the author’ personal website or paper GitHub repo. They also show their work’ BibTeX as CVPR paper. But CVPR Committee just only published the ids of accepted papers that means we can’t know the which papers will publish CVPR officially.

It seems like, the correct way is that cite papers as an Arxiv paper. What is your experiments and thoughts on this situation? Thanks in advance.

Exact addition, subtraction, multiplication, and division of real numbers is a challenge for machine learning models.
Neural networks can approximate complex functions. However, when extrapolating to out-of-distribution samples on arithmetic operations neural networks often fail. I would like to gather a set of applications requiring exact arithmetic of real numbers – do you have any ideas?

In regression problem, one may transform the dependent variable to log space before fitting the model. And in some cases we would want to transform the prediction back to a normal scale.

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According to this discussion, it seems right to adjust the prediction by Duan’s Smearing Estimator or a general smearing adjustment after taking exp. But in practice, I do not see a lot of example which do such adjustment. Is the effect so small that people normally neglect it?

This is because US government has placed Huawei on the “Entity List”.

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The news broke here: https://twitter.com/qian_junhui/status/1133595554905124869

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Here is Prof. Zhang’s (from Peking University) resignation letter from IEEE NANO: https://twitter.com/qian_junhui/status/1133657229561802752

With adding a consistent version system across all of the code the art of coding moved from craft to engineering – the same thing will happen to data governance: https://towardsdatascience.com/the-rise-of-dataops-from-the-ashes-of-data-governance-da3e0c3ac2c4 (full article)

Currently, data governance teams attempt to apply manual control at various points to control the consistency and quality of the data. The introduction of Data Version Control (DVC) version tracking would allow data governance and engineering teams to engineer the data together, filing bugs against data versions, applying quality control checks to the data compilers, etc.

Platforms like Palantir Foundry already treat the management of data in much the same way as versioning of code. Within data versioning platforms datasets can be versioned, branched, acted upon by versioned code to create new data sets. This enables data driven testing, where the data itself is tested in much the same way as that the code that modifies it.

There also some open source options:

• Data Version Control project is focused on data scientist users.

• Delta Lake project is a DataBricks’ version control system for data lakes with big data workloads.