Hello guys, I am trying to implement this paper. They used restricted boltzmann machines to get evolution of a system. Due to nature of the problem “activation” function is not sigmoid but tanh which is later transformed into probability. Different activation function means that gradient is different, although in the paper they wrote “standard” gradient equations. When I tried to train model with standard gradient equations I couldn’t get any meaningful results.I am aware that RBM minimizes KL divergence, but I couldn’t figure out how to implement that (how to get probability function). After trying to find what should I put for loss function when training with eg. Adam I stumbled upon some random github code which for loss function had:

L = energy(data_vec) – energy(genrated_vec)

(yes without absolute value). Well, I plugged that in and it worked like a charm. When I tried to put absolute value over loss it did not worked. I am having trouble understanding why is this correct loss for RBM and I would appreciate help 🙂

What is sota for image manipulation detection? I have not seen much work in this field happening, does anybody know the reason for that? The only recent paper have seen is from CVPR2018 but I thought there would be more work being done?

Thanks for your help!

I found this :

https://www.fujitsu.com/global/about/resources/news/press-releases/2019/1015-01.html

Looks like it was posted about a month ago, I haven’t really found much more info on it, was trying to see if it was anything groundbreaking, the details provided are slim to none. Just gives a basic overview of how neural networks seem to work, and some fancy terms for normalization/transformations/landmark detection. Thanks.

They make some bold claims:

This technology has achieved a high detection accuracy rate of 81% even with limited training data.

This technology is also more accurate than other existing technologies according to certain facial expression recognition technology benchmarks

Building a Deep Learning home server with 4x 2080 ti blower style GPUs and I am wondering what CPU to get for this machine. The delima is that I am using python Pytorch and Numpy which has a lot of support with Intels MLK packages that sabotage AMD performance. However, I am utilizing the GPUs and CUDA technology for the matrix multiplication and can get away with Numpy Openblas for my environments on the AMD machine. I’m trying to keep this project under $8000 and want the best performance. Also, I am primary using DDPG and DQN networks. What would you do?

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I’m developing a tool to use machine learning and sentiment analysis to determine the usefulness of context when parsing sarcasm.

In order to evaluate the success of the tool, I’m looking for human responses to a few examples from SARC 2.0 (https://arxiv.org/abs/1704.05579), a corpus of Reddit comments.

If you can spare a few minutes to answer this Google form (no login required), please help me out!

kaggledatasets: Collection of Kaggle Datasets ready for everyone to use.

It is a Python package which will contain several high voted kaggle datasets made available in easy to use format with special support for frameworks like Tensorflow 2.0 and PyTorch.

Every beginner in ML/DL/Data Science struggles in the initial stages of Data Loading and Pre-processing. Kaggle being the gold mine of datasets, it’s necessary to make it easy to use for everyone.

For the folks who have used tf.datasets or torchvision datasets, this is something similar to that and only for Kaggle 🙂

And hold on, support for more datasets and more types is on the way. Feel free to contribute and add datasets. Also, looking for experienced developers who can contribute to the architecture of the project or provide reviews for improvement.

GitHub: https://github.com/kaggledatasets/kaggledatasets

Hello,

As a PhD student, I read quite a lot of papers, and sometimes I make short summaries with a simple latex template to get a better understanding and have clearer idea of the paper’s contributions. For a while I stored them in private Github repo, so I tought why note share them, some people might find them helpful.

PS: Sorry for the (sometimes frequent) spelling mistakes.

Here is the Github link: https://github.com/yassouali/ML_paper_notes

Hi everyone!

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Paper: https://arxiv.org/abs/1911.07532

Github, blog

Abstract: We extend the framework of graph neural networks (GNN) to continuous time. Graph neural ordinary differential equations (GDEs) are introduced as the counterpart to GNNs where the input–output relationship is determined by a continuum of GNN layers. The GDE framework is shown to be compatible with the majority of commonly used GNN models with minimal modification to the original formulations. We evaluate the effectiveness of GDEs on both static as well as dynamic datasets: results prove their general effectiveness even in cases where the data is not generated by continuous time processes.