A bunch of website builders claim that they can use AI to make a great website for you. Personally, I feel like they are actually using some simpler algorithms rather than ML and are using the term ‘Artificial Intelligence’ to make it sound all fancy and advanced.

To what extent are the claims of these websites true?

A few days ago was published a paper on the topic “What does it mean to understand a neural network?”.

https://arxiv.org/abs/1907.06374

This article was not discussed on reddit.

While some trash like “Decomposing latent space to generate custom anime girls” was discussed very active

What’s wrong with you?

Hello all,

My data points consist of 71 attributes, each numeric between 0 and 1, the target class is binary and my belief is the relationship is non linear. In addition, the attributes have a 1D spacial relation to each other, this may or may not be important.

I would like to have a model to be able to classify each tuple, but equally as important to me is a way to understand which attributes are the most weightful in the classification.

I’m thinking I should use an autoencoder for the second part, and to use the output from that as an input for a model, such as another NN.

Does that sound right, or am I leaning too heavily in the direction of NNs because of how my data looks?

Thank you all very much in advance.

Danny Lange discusses the role of intelligence in biological evolution and learning. He demonstrates why a game engine is the perfect virtual biodome for AI’s evolution.
You will recognize how the scale and speed of simulations is changing the game of AI while learning about new developments in reinforcement learning.

• Video
• Slides

The author shortly demonstrate how you can easily make use of DVC tool to effectively manage your ML workflow: Effective Management of your Machine Learning Laboratory

An example project (from the article) in the author’s personal GitHub

The following common ML workflow issues are dealt with in the article:

• how to connect versions of source codes and versions of large data files?
• how to recover model from weeks earlier without retraining it?
• how to run only model inferencing using a model that I built weeks ago?
• how to keep a track of model parameters of various ML experiments?

I am having trouble understanding a section from the MobileNetV2 paper.

In particular, section 3.2 Linear Bottlenecks, authors talk about how “it is easy to see that in general if a result of a layer transformation ReLU(Bx) has a non-zero volume S, the points mapped to interior S are obtained via a linear transformation B of the input, thus indicating that the part of the input space corresponding to the full dimensional output is limited to a linear transformation.” Is there a simpler way of explaining this?

Can I check my understanding, that this volume S is the volume created by the output tensor of ReLU(Bx), which each “pixel value” is a multi-dimensional vector and a point in the subspace, and all of these points form a volume? And if so, it is not clear to me why the interior points have any relevance to the argument.

I’m not sure this is the right place for this, but hopefully someone can point me in the right direction if not. I run a site called Fantasy Critic. It’s basically fantasy football for video games, where you “draft” a “team” of video games and compete for the best review scores. On the site, I maintain a list of “master games” that players can select from. You can see that here:

https://www.fantasycritic.games/games

I have a stat that I calculate, called “Hype Factor”, that attempts to calculate how excited people are for a game. Right now, it’s a fairly simple calculation based on how frequently a game is drafted and how early in the draft the game is taken. However, I think my calculation is a little flawed and I’m wondering if machine learning would be a better choice here.

What I’m looking to do is take the various stats that I can gather from the site and use those to predict the critic score (from OpenCritic.com) that the game will get. I have a sample dataset here:

https://www.dropbox.com/s/6vc3dwltbncm8ev/FantasyCriticMasterGameData.csv?dl=0

So there’s a lot complexites that would go into doing something like this I’m sure. One of my concerns is that I may not have enough data here to be able to generate a model. I’m open to any thoughts or advice that people have, and I can expand on anything that’s unclear. Additionally, if anyone feels like they have a decent shot at making this work, I’m open to paying someone to help me with this.

Thanks!

code: https://github.com/junsukchoe/ADL

video: https://youtu.be/azevl9_w2BE

paper: http://openaccess.thecvf.com/content_CVPR_2019/papers/Choe_Attention-Based_Dropout_Layer_for_Weakly_Supervised_Object_Localization_CVPR_2019_paper.pdf

: We are glad to share our Tensorflow and Pytorch implementations for “Attention-based Dropout Layer for Weakly Supervised Object Localization. CVPR 2019.” Although this repository is still in progress, we believe that the current version can also help researchers and practitioners in this field. More detailed instructions will be released soon. Contributions and stars are welcome!

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Abstract

Weakly Supervised Object Localization (WSOL) techniques learn the object location only using image-level labels, without location annotations. A common limitation for these techniques is that they cover only the most discriminative part of the object, not the entire object. To address this problem, we propose an Attention-based Dropout Layer (ADL), which utilizes the self-attention mechanism to process the feature maps of the model. The proposed method is composed of two key components: 1) hiding the most discriminative part from the model for capturing the integral extent of object, and 2) highlighting the informative region for improving the recognition power of the model. Based on extensive experiments, we demonstrate that the proposed method is effective to improve the accuracy of WSOL, achieving a new state-of-the-art localization accuracy in CUB-200-2011 dataset. We also show that the proposed method is much more efficient in terms of both parameter and computation overheads than existing techniques.