http://www.mendel-conference.org/index.php?link=overview

A small-scale conference on Soft Computing, Evolutionary Computation, Computational Intelligence, Artificial Intelligence, Neural Networks, Deep Learning, Bayesian Methods, Fuzzy Logic, Intelligent Image Processing, Bio-Inspired Robotics…

Accepted contributions published in a journal (MENDEL) or in conference proceedings (AISC Springer – Recent Advances in Soft Computing).

(I’m one of the editors, feel free to PM me if interested).

Hey, posting our NAACL workshop paper (ESSP workshop).

In the course of working on it, I’ve been thinking a lot about “simulator” style environments for training NLP models. While there are many simulators for robot navigation and autonomous driving, there are hardly any for NLP. Seems to me there could be a lot of interesting potential there (would help in framing NLP problems as program induction, etc). If you have any thoughts on this or the paper, I’d be happy to hear!

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arXiv landing page

Understanding procedural text requires tracking entities, actions and effects as the narrative unfolds. We focus on the challenging real-world problem of action-graph extraction from material science papers, where language is highly specialized and data annotation is expensive and scarce. We propose a novel approach, Text2Quest, where procedural text is interpreted as instructions for an interactive game. A learning agent completes the game by executing the procedure correctly in a text-based simulated lab environment. The framework can complement existing approaches and enables richer forms of learning compared to static texts. We discuss potential limitations and advantages of the approach, and release a prototype proof-of-concept, hoping to encourage research in this direction.

This is a 35 minute talk by iOS tech lead at Groupon, Mourad Sidky from GOTO Copenhagen 2018.

https://youtu.be/97CPDQBUAoY?list=PLEx5khR4g7PIzxn476GK3Mkk19csZZjeH

Please give the talk abstract a read below before giving it a watch:

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Mobile devices are getting more and more powerful, with not-only advanced hardware, but also intelligent operating systems and high-performance compatible set of native frameworks. Mobile devices are capable of doing expensive on-device processing to achieve augmented reality and machine learning, without the need to communicate to any other external services. Apple exposed them in ARKit and CoreML. Both frameworks are implemented in a way to achieve correctness, best performance, energy efficiency and data privacy. Apple solves lots of problems and gave us the solutions in these frameworks. I’ll be speaking about augmented reality, step into augmented reality for iOS, then I’ll speak about machine learning, step into machine learning for iOS and then I’ll finalise with the power of cooperation between augmented reality and machine learning together.

What are people using to manage training and evaluations on k8s clusters?

Until now I started my training processes by hands, maybe with some hyperparameter search using a process pool.

But as I develop more complex models I’m looking for a solution that scales better. Kubernetes looks like a perfect fit as I’d be able to define how many cores and GPUs I assign to each model.

However I can’t find a straight-forward way to use k8s as a task runner. I’m pretty familiar with it to run reliable, long-running tasks such as serving models. But what I want now is a way to start and monitor many tasks, possibly involving multiple steps each (preproc, training, validation). The ability to prioritize and pause these tasks would be a nice-to-have.

One solution would be to simply create Jobs and to make my own dashboard and persistence logic. Or I could use a generic Job dashboard like this: https://github.com/pietervogelaar/kubernetes-job-monitor

Something else that looks promising is Kubeflow, but it looks like a lot of extra complexity.

I’m really curious to hear how other people handle this?