Could you please list most important or interesting publicataions covering application of neural networks and deep learning to 3D data, especially point clouds? I’m mostly interested in application of NNs to tasks like classification/segmentation of 3D objects but any other references are higly appreciated.

Thanks in advance.

https://youtu.be/We20YSAJZSE

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MuZero harnesses the power of AlphaZero, but without relying on an accurate environment model. This opens up planning-based reinforcement learning to entirely new domains, where such environment models aren’t available. The difference to previous work is that, instead of learning a model predicting future observations, MuZero predicts the future observations’ latent representations, and thus learns to only represent things that matter to the task!

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Abstract:

Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics governing the environment are often complex and unknown. In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. MuZero learns a model that, when applied iteratively, predicts the quantities most directly relevant to planning: the reward, the action-selection policy, and the value function. When evaluated on 57 different Atari games – the canonical video game environment for testing AI techniques, in which model-based planning approaches have historically struggled – our new algorithm achieved a new state of the art. When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.

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Authors: Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, David Silver

Graph machine learning is still a relatively new and developing area of research and brings with it a bucket load of complexities and challenges. One such challenge that both fascinates and infuriates those of us working with graph algorithms is — scalability.

I learned first-hand that when trying to apply graph machine learning techniques to identify fraudulent behaviour in the bitcoin blockchain data, scalability was the biggest roadblock. The bitcoin blockchain graph I used has millions of wallets (nodes) and billions of transactions (edges) which makes most graph machine learning methods infeasible.

An algorithm called GraphSAGE (based on the method of neighbour-sampling) offered some solid breakthroughs, but there are still mountains to climb to make scalable graph machine learning more practical.

https://medium.com/stellargraph/scalable-graph-machine-learning-a-mountain-we-can-climb-753dccc572f

There are large amount of papers which show that EfficientNet improves some CV tasks e.g. EfficientDet: Scalable and Efficient Object Detection.

But does it help much in real projects ? Do you guys have any experience with that ?

One more thing – ImageNet or COCO datasets are far away from what we have to deal with in real projects. Usually we have only small amount of images/classes, so improvements for COCO/ImageNet != improvements for real projects. What do you think ?

This is a talk from GOTO Chicago 2019 by Doug Lenat, Award-winning AI pioneer who created the landmark Machine Learning program, AM, in 1976 and CEO of Cycorp. I’ve dropped the full talk abstract below for a read before diving into the talk:

• Video
• Slides

Almost everyone who talks about Artificial Intelligence, nowadays, means training multi-level neural nets on big data. Developing and using those patterns is a lot like what our right brain hemispheres do; it enables AI’s to react quickly and – very often – adequately. But we human beings also make good use of our left brain hemisphere, which reasons more slowly, logically, and causally.

I will discuss this “other type of AI” – i.e., left brain AI, which comprises a formal representation language, a “seed” knowledge base with hand-engineered default rules of common sense and good domain-specific expert judgement written in that language, and an inference engine capable of producing hundreds-deep chains of deduction, induction, and abduction on that large knowledge base. I will describe the largest such platform, Cyc, and will demo a few commercial applications that were produced just by educating it as one might teach a new human employee.

But it is important to remember that human beings’ “super-power” is our ability to harness both types of reasoning, and I believe that the most powerful AI solutions in the coming decade will likewise be hybrids of right-brain-like “thinking fast” and left-brain-like “thinking slow”. That is the only path I see by which we will overcome the current dangerous inability of deep-learning AI’s to rationalize and explain their decisions, and will make AI’s far more trusted and – more importantly – far more trustworthy.

Anyone who understood this abstract and found it interesting should find my actual talk similarly accessible – and hopefully interesting!

Too bad I can’t create polls on Reddit…

I was talking with a data scientist friend about versioning data in ML projects (I know there are a lot of great solutions and this post is not meant to focus on any of them).

What he said really flipped the notion I had in my head that data is an integral part of data science source code.

He claimed that in most data science projects the data and artifacts (intermediate stages of data processing not including models) don’t change that much. This is to say, the source data might be changed, but it is just one file (So you can get away with not versioning it) and intermediate stages should always be determined by code so you just need to manage the code you used to create the stage and not the result (the only exception is if you have some painful or resource intensive processing where you wouldn’t want to repeat that process).

I was wondering, from people here with experience in real world projects, how versatile is your data? Do you feel it’s hard to manage the data and artifacts?

I’m confused and your input would be greatly appreciated.

I found that the word vectors loaded from en_core_web_md in spacy have some odd behavior. For instance, the words flamingo, penguin and igloo have precisely the same vectors (with nonzero values). I can’t find much on Google. Has anyone encountered this before?

• Project page: https://hyperconnect.github.io/MarioNETte/
• Paper: https://arxiv.org/abs/1911.08139
• Video: https://youtu.be/Y6HE1DtdJHg

Here’s a summary of the work. We can perform face reenactments under a few-shot or even a one-shot setting, where only a single target face image is provided. Previous approaches to face reenactments had a hard time preserving the identity of the target and tried to avoid the problem through fine-tuning or choosing a driver that does not diverge too much from the target. We tried to tackle this “identity preservation problem” through several novel components.

Instead of working with a spatial-agnostic representation of a driver or a target, we encode the style information to a spatial-information preserving representation. This allows us to maintain the details that easily get lost when utilizing spatial-agnostic representation such as those attained from AdaIN layers.

We proposed image attention blocks and feature alignment modules to attend to a specific location and warp feature-level information as well. Combining attention and flow allows us to naturally deal with multiple target images, making the proposed model to gracefully handle one-shot and few-shot settings without resorting to reductions such as sum/max/average pooling.

Another part of the contribution is the landmark transformer, where we alleviate the identity preservation problem even further. When the driver’s landmark differs a lot from that of the target, the reenacted face tends to resemble the driver’s facial characteristics. Landmark transformer disentangles the identity and expression and can be trained in an unsupervised fashion.

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Check out the video and tell us what you think. Thanks!

I imagine getting the cuda/cudnn issues right would be difficult. Not sure if there’s anything out there already.

https://www.google-colab.com

I started by cataloguing a few interesting notebooks in a github repo and want to make it more easy for others to share their work and receive comments.

If you are interested, the posts also push to twitter, linkedin, reddit and facebook

So why Google Colab? — Its is fairly easy to use with a click-and-run format and helps those with limited resources. And even though it has some issues, I am sure we are only at the start of a long-term project and that Colab would keep on improving. If you have any feedback, please let me know.