I think the core reason why DeepDream don’t work on music well is that it’s learnin’ directly on it.

That’s like training a painter on paintings without letting him see any real world objects beforehand.. (or more within my taste, like training a game level designer on other game designs without letting him spend years & years in real world getting ‘useful’ information about the objects present here)

Could someone train a convolutional (or possibly recurrent) network to learn on a large dataset on bird sounds, trucks and many other sounds we hear daily (and make it learn which category it should put these to), and then try to make it imagine something on either an existing track, or rather try to adapt the videoify.py (i know it’s getting repetitive) on it, to progressively build a song ?

I have Previously Worked on many smaller projects and two major projects which is Forex Forecasting using SVR and comparing it with different kernels and Neural Network and the Other project is Denoising an image using generative adversarial network.

I am supposed to do a 6 months full time thesis project. Can you Guys help me in getting a direction to think or any idea for what to do as my project? I am interested in some computer vision application but I am open to any other topic like NLP or maybe Reinforcement learning given i can do some substantial work in the given time.

New blog post about the Importance of Decoding Algorithms in Neural Text Generation. We look into why greedy/beam search is not a good fit for free form generation. And look into 3 sampling strategies, including the most recent one (Nucleus Sampling) as well as describe the rationale behind all of them.

https://medium.com/@patry.nicolas/model-based-bpe-encodings-dd664c959a90

​

Summary : Idea to generate Byte pair encodings, not based on frequency in the dataset, but on the quality of the prediction of our model. This enables us to predict multi word tokens like “New York” and address languages that don’t use spaces to split words.

​

Author here : I’m not a researcher, and could not find any paper related to that idea, if you know about any research in that direction please let me know. Or any comments on the post.

Hey fellow redditors, please allow me to introduce you to Pywick – a high-level Pytorch training framework that aims to get you up and running quickly with state of the art neural networks for image classification and segmentation, optimizers (like SWA, AdamW), activation functions (swish/aria) etc. Does the world really need another Pytorch framework? Probably not. But I began working on this project about three years ago when no good frameworks were available and it just kept growing. So it probably doesn’t hurt to have another open-source framework out there.

The goal is to try to keep up with the state of the art in terms of models and various training enhancements so that you have something to play around with. Does SWA or Swish really guarantee you best results? Probably not… but it’s there in case you want to check it out.

Lastly, I just wanted to mention that I really can’t take credit for any of the cool stuff. I mostly assembled and cleaned up what other awesome people have already done. But that way you don’t need to do the dirty work and can just use it.

*REPOST! Thanks everyone who participated, I think this sub gave me the best response rate! I still need quite a few more responses,tho, so please fill in/share. Thanks!*

​

Hello everyone! As a master degree candidate from The Netherlands, I’m doing a research on to what extent Amazon’s e-commerce recommendations affect the purchase decisions. If you’ve purchased something from Amazon in the last 6 months, I would really appreciate 5 minutes of your time to fill in this questionnaire. Sharing is well appreciated! Thank you!

Link to the survey:

https://erasmusuniversity.eu.qualtrics.com/jfe/form/SV_0jOW3i5nxkVALT7

Are there any deep learning frameworks that support unsigned 64-bit integers?

​

I know it’s very specific, but I’m working on a chess engine (think AlphaZero stuff) and we often represent the board in chess engines with several unsigned 64-bit integers. This allows us to use extremely efficient functions to generate moves: https://www.chessprogramming.org/Move_Generation I want to do this move generation on the gpu, so the gpu tasks must support unsigned 64-bit integer and their respective manipulations (bit-shifts, etc…).

​

I looked at tensorflow and pytorch and neither seem to support unsigned 64 bit integers, unless I’m wrong?

​

Maybe there’s an easier way to do this with cuda? but I don’t know how to hook cuda up with pytorch/tensorflow efficiently. I can use either tensorflow or pytorch, and I work in python.

Blog post by google AI: https://ai.googleblog.com/2019/05/introducing-translatotron-end-to-end.html

Audio samples: https://google-research.github.io/lingvo-lab/translatotron/#conversational

Link to paper: https://arxiv.org/abs/1904.06037

​

EDIT: Again, to be clear, I’m not in any way associated with Google AI. “Introducing Translatotron …” is simply the title of the blog post.