I just started researching text2speech and am interested in reading in some new novel approaches and what is considered as SOTA in text2speech. Will be thankful for any insights and recommendations.

In NeurIPS2018, Michael Levin’s keynote was really amazing. What were your favorite talks in ICML 2019 that you’d like to recommend? I’m particularly interested in some less task-specific and more general and inspiring ones.

I’ve posted about datasetlist.com a few months back and received lots of great feedback and ideas.

I’d like to share a new addition to the site: a list of annotation tools for building datasets at datasetlist.com/tools. I hope this saves someone from writing a new tool from scratch. 🙂 Let me know what you think! I’ll be keeping the list updated with new tools and features.

This is my first research project. I will appreciate any feedback to improve myself. The link to the report – https://github.com/akhandait/disentangled-representation-learning/blob/master/report.pdf.

I’m looking to start a new research project on the subject of pre-training models to learn some sort of context. When applied to a new environment the goal is to have the model perform better by leveraging what it knows about the context of the environment (which it can learn from other environments or outside examples).

Example: In many games (like Mario), being reset back to your original position is a bad thing because it usually means you died and had to restart.

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I realize this is a very broad topic and description, but I was wondering if anyone could point me in the direction of any similar research. I’m having trouble finding related research.

My current understanding of Alpha Zero from a high level is that it takes in the board state and outputs the probability distribution of results. I am especially confused about the anatomy of the inputs though.

Taking the example of chess, the DeepMind arxiv paper said that the network had a total of 119 spatial planes. Of these spatial planes four types stick out to me: color, repetitions, total move count, and no-progress count.

Color

The layer feels useless because it only encodes a single number repeated. Couldn’t this be optimized out because the information is already encoded within the board structure. The chess board is oriented such that the side AlphaZero is playing is on the bottom and color should be implicit with the layers that the P1 and P2 pieces exist in. Didn’t the authors also say P1 is always the player to make the move? Why do we need a color map?

Repetitions

I’m not sure what this is. Does this come from fen representation? Why does this need to be counted per time-step? I believe that past a certain point the repetitions no longer count towards a draw. As well, how are these encoded? Is it just a copy of the relevant positions that would cause a three-fold repetition?

Total Move Count and No-Progress Count

Are these encoded as integers? To my understanding, activation functions perform best near 0. So, a small difference in count would not make a difference. Would these be normalized or will this happen implicitly in the network. Also not sure what are no-progress counts.

I apologize if my questions are a waste of time.

Link to the blog post: https://ogma.ai/2019/06/ogmaneo2-and-reinforcement-learning/

Hey all,

We have finally figured out a good way of integrating reinforcement learning into our biologically plausible, fully online/incremental learning system OgmaNeo2 (implementing Sparse Predictive Hierarchies, SPH). Here we provide a few demos and some high level description, as well as links to learn more.

Included among the demos is a real-world mini-sumo robot fight, where the agents are implemented using our system. The game proceeds in episodes and automatically resets itself.

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For those who are wondering what SPH is, we included a link to a more in-depth presentation in the blog post. Here is a quick summary though:

SPH is a fully online/incremental lifelong learning system that does not use backpropagation, and is biologically plausible. It is also extremely fast, able to run in real-time on platforms such as a Raspberry Pi Zero with learning enabled. It uses a bidirectional hierarchy of very sparse encoder-decoder pairs. It is activated in two passes (although asynchronous implementation is also possible): An up-pass followed by a down-pass. All input/output occurs at the “bottom” of the hierarchy. Each encoder-decoder pair forms a layer, and each layer clocks at a slower rate than the layer directly below. We call this “exponential memory”, as it encodes information into slower and slower timescales going up the hierarchy, and decodes into faster timescales going down, allowing us to bridge exponentially large time lags with respect to the number of layers.

For reinforcement learning, we took out the original decoders which just predicted the next timestep(s) of input and replaced them with a swarm of reinforcement learning agents that all seek to locally maximize the same reward.

Let us know what you think!