The top of the SQuAD 2.0 leaderboard shows a new model from the Google Language Team called ALBERT. Based on the name, it seems to be an extension of BERT. It looks like a significant improvement on SOTA, but I can’t find anything about it online. Maybe a model soon to be released?

https://rajpurkar.github.io/SQuAD-explorer/

I am training a model for searching through research papers, and I would love to test it out to see how well it does on queries from real users.

I would check to see if the results from the top search engines people use for looking up ML papers are also included in the model.

blog: https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html

also a few shot approach for generating deepfakes: https://github.com/shaoanlu/fewshot-face-translation-GAN

So I’m creating a weather forecast for a project and I need to analyze some historical data from a .json file. I want to create a list of daily summaries with the values from each dates. The .json layout looks like this:

{“city_id”:3160606,”main”:{“temp”:285.88,”temp_min”:284.26,”temp_max”:288.15,”pressure”:1015,”humidity”:79},”wind”:{“speed”:1,”deg”:166},”rain”:{“3h”:0.025},”clouds”:{“all”:80},”weather”:[{“id”:803,”main”:”Clouds”,”description”:”broken clouds”,”icon”:”04n”}],”dt”:1474588800,”dt_iso”:”2016-09-23 00:00:00 +0000 UTC”},{“city_id”:3160606,”main”:{“temp”:285.34,”temp_min”:284.26,”temp_max”:287.04,”pressure”:1015,”humidity”:82},”wind”:{“speed”:1,”deg”:166},”rain”:{“3h”:0.025},”clouds”:{“all”:80},”weather”:[{“id”:803,”main”:”Clouds”,”description”:”broken clouds”,”icon”:”04n”}],”dt”:1474592400,”dt_iso”:”2016-09-23 01:00:00 +0000 UTC”},

Any help on how I can get data from every specific day sorted in a good way?

Must share something great !

Working with Genetic Learning as replacement for back propagation and just made a complex convolution network for a CIFAR-100 dataset with 100 classes and it started training immediately. No backprop

Training in progress and no stop at 10% so I guess its working. Will be fun to see where how good it will be but anyway. Its training ! Its giving results…

Hello,

so I have a project I need to get done for University, in which I make a program, which skips Iteration steps for the Optimization of Mechanical Structures.

The Mechanical Structure is defined as follows:

You have nodes (in 3-D or 2-D) which are fully connected by beams. The Starting point is, that all beams have the same density and there are Loads, which are on some of the Nodes. Also there is an upper and lower bound for the densities of these beams (which is relevant for the conventional method)

In the Normal Process the density of the beams is tweaked Iteration by Iteration, depending on the different loads on the nodes to make the structure stiffer, also there is a certain volume fraction of the design space, which the sum of the beams has to reach.

Now I need to make a Neural Network which is capable of skipping Iterations, which are computationally expensive and I wanted to ask how I should start it.

The first file I received has 30 Nodes and 373 design variables (beams).

This is how it looks:

https://imgur.com/8p7jctT

After Optimization:

https://imgur.com/VwumM5w

I don’t know what type of network I should use, since I’m not experienced in doing my own projects.

My first ideas were:

1. Neural network, which takes the beams and the loads (in coordinates) as features encodes them and decodes them.
2. Same as 1. , but with the nodes as Features
3. CNN Encoder Decoder Network, with a matrix of NxN, N being the Nodes and the entries in the Matrix being the density of the beams, which connect the nodes, then the other channels are the loads on the node, the colume fraction and maybe the different x,y and for 3-D z values of the node.

The university can generate the data needed for training the Neural Network (Different Loads and all the iterations of the different optimizations) and the target are 3-D structures with thousands of beams. Right now we are starting of easy with 2-D structures, but I don’t know how many beams option 1. and 2. can handle, before dimensionality becomes a problem. Also I don’t know if a CNN can learn from the node matrix.

I’m not a big expert in the field, but I read a lot of material on NNs and ML (e.g. Aurelien Geron Hands on machine learning) and I am eager to make this project work, but I need a little bit of help in starting off, so I would be really grateful, if some of you guys could help me with that 🙂

I have a task and I don’t know how to tackle this. I received a set of positives and I have to find similar points from a big dataset (that I call basket). I have around 1’000 positives and around 1’000’000 points in the basket. All points are represented with 10 to 15 features. As an output, I would like to have a score for each point of the basket and this score would represent the closeness of the point to the positive set.

I first thought of using a k-nearest neighbours method on the positives but this approach presents two big drawbacks for me. First, I wouldn’t have a score associated to each point of the basket as I would only have a set of close points for each positive. Secondly, and this is the biggest drawback in my opinion, I would have to define the distance in the n-dimensional space myself while I would prefer that the method directly defines weights for each feature on the data (for instance, based on the level of information (variance) contained in each feature).

Does someone could point out to me a good approach to tackle this problem?

Thanks!

Hi,

I’ve been recently away from the research landscape, so I’m not up to date on what’s going on lately.

I know in 2017 the big hype was still Supervised Learning with bigger and bigger models.

Is there anything that might predict upcoming breakthrough in unsupervised ML or simpler supervised models (few-shot/one-shot learning) ?

What do you thing ML will look like in 5-10 years ?