Can machines dream while listening to music? Is it possible to turn music into images in a meaningful way? deepsing was born to materialize our idea of translating audio to images inspired by Futurama Holophoner. In this way, deepsing is able to autonomously generate visual stories which convey the emotions expressed in songs. The process of such music-to-image translation poses unique challenges, mainly due to the unstable mapping between the different modalities involved in this process. To overcome these limitations, deepsing employs a trainable cross-modal translation method, leading to a deep learning method for generating sentiment-aware visual stories.

We have implemented a front-end to our method at https://www.deepsing.com You can find an example of a purely machine-generated visual story using our method at https://deepsing.com/engine/9C0xGB73Uuc/5dfbcd1ec9e5f7311d8a9fcf Note that the version available at https://www.deepsing.com is currently lacking many essential features, but demonstrates the basic concept of our idea! Also, note that song lyrics are NOT used in this process, since the proposed method currently works based SOLELY on the sentiment induced by the audio!

Furthermore, you can find more information in our preprint https://arxiv.org/abs/1912.05654, while we have also released the code of our method at https://github.com/deepsing-ai/deepsing Feel free to hack with us and share your opinions with us!

Hi everyone! I handpicked 10 must-read articles of last month so that you won’t miss anything. Interesting contributions for socially and environmentally responsible Machine Learning, Yoshua Bengio and François Chollet talking about real intelligence, and there is even a Christmas song generated by GPT-2!

This right here

Hello everyone,

I’m new using reddit but seems to be a very interesting to place to ask questions.

Basically I was doing a research process during the first year of my PhD, and now I have some interesting results that I would like to send to some conference. I was doing my research in the use of a new reduced precision numerical data type to train deep learning models (CNN, RNN) without accuracy penalties. The numerical data format is not implemented on hardware right now but probably during this year some processors will implement it.

Any advice about some conference to send my paper ? The idea is send it to some conference having the deadline between January-March.

Thanks for your help.

Not sure if this is the best place to ask this, but let’s say I have a ginormous cross reference matrix file, where the first row and column represent existing nodes. So then any cells they have in common represent new relationship nodes. What’s the best way to incorporating these new nodes into a graph? Assuming I don’t really want to create these new nodes.

The reason is because I’d like to keep the graph condense and maintainable, and creating all of the relationship nodes can make the graph unruly and hard to look at. Is there some structure I can store the data in, like a lookup table of sort for property graphs/Neo4j?

Well it’s actually really basic library which I just made for fun.

I tested it with MNIST_Digits (acc = 0.95). {784,250,10 with sigmoid and 0.3 learning rate}

I’ve attached demo of XOR gate training.

check out library on github

Any feedback from you guys are highly appreciated !

P.s. I’m new at both GitHub and Machine Learning so bear with me.

In the early 2010s, I actively used Hadoop / Hive and HBase for large-scale data processing. Since then, I’ve been somewhat out of the loop, except for using Spark infrequently. I am now wondering what would be the best open source software for storing a very large image dataset (100s of terabytes if not multiple petabytes) on commodity hardware. The reason I post this here is that the objective will be to run ML algorithms over subsets of the images in this dataset. Thus, it would be desirable to execute ML code in situ, if possible. For my purposes, it’s also safe to assume that writes are fairly infrequent.

https://www.youtube.com/channel/UCHB9VepY6kYvZjj0Bgxnpbw/videos

This YouTube channel deserves more attention IMO. I don’t know who it is but it started pretty recently and features frequent updates with mid-level overviews of recent ML research papers. I like having it on in the evening as a healthier alternative to the random crap* I binge on YouTube when I get off work.

*as in: longplays of 90s video games and pensioners eating watermelons ¯_(ツ)_/¯

Hello folks,

I want to implement a handwritten digit detector that works in Realtime. I want to draw boxes over the video stream to indicate the user that the digits are detected. Training and Detection is made utilizing a GPU.

Due to the fact I am relatively new to tensorflow and CNNs I investigated some nets and tried to train them with MNIST and some other labeled images I made myself.

Until now I used: SVM, LeNet-5, R-CNN and yolov2

From what I have read so far, I think yolov2 or yolov3 would be an appropriate neural network for the task. Because they are very fast in detection. But there are so many layers and it seems to be very complex.

Do I need to choose such a complex CNN? Originally it was intended for 3D object detection with many classes and I only use it for 2D digits (only 10 classes).

Like I already said, I am new to the topic, so be nice…^^

In segmentation there is usually a lack of datasets, so I want to use more than one. For example, let’s say I want to segment four classes, cars, buses, bikes, and background. background anything other than the vehicles.

I have three datasets, two of them have ground truths for cars, busses, and bikes but the last one doesn’t label the bikes and just ignore it. I want to use all datasets, is there a trick where I get away with this. I’m using softmax and use logic nor to get the background. It worked using only the first 2 datasets but I want to add the last dataset. I wrote this question and I didn’t get an answer in StackOverflow. Thank you

I have been looking at using Graph Neural Networks as a classifier. The example here: https://towardsdatascience.com/hands-on-graph-neural-networks-with-pytorch-pytorch-geometric-359487e221a8 was a good intro for me – provided with lots of small graphs (Recsys 2015 yoochoose challenge data) can you make a prediction on what they will buy. This seems to get good results (I am unsure how it was appropriate to use a variant of GraphSage though, the documentation recommends it to be used on very large graphs – is there any suggestions as to why this was ok here?).

However, what if I want to go a step further and generate new graphs? How could this be accomplished? One generative graph approach, GraphGAN, is designed to be trained on 1 very large graph, as opposed to lots of smaller ones. Is there work that looks at doing what I am hoping to accomplish?

Thanks