Wednesday, October 14, 2020

Weight Agnostic Neural Networks, a virtual presentation by Adam Gaier, Thursday October 15th, LightOn AI meetup #7

** Nuit Blanche is now on Twitter: @NuitBlog ** 

Ever since we started LightOn, we have been putting some emphasis on having great minds think how new algorithms are possible and how they can be enabled with our photonic chips.  We also have a regular meetup where we see how other great minds are devising new algorithms. 


Tomorrow, Thursday (October 15th) we are continuing this journey by having Adam Gaier who will talk to us about Weight Agnostic Neural Networks. The virtual meetup will start at:
  • 16:00 (UTC+2) Paris time but also 
  • 7AM PST, 
  • 10AM CST, 
  • 11PM JST. 
To have more information about connecting to the meetup, please register here: https://meetup.com/LightOn-meetup/events/273660363/



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Saturday, October 10, 2020

As The World Turns: Implementations now on ArXiv thanks to Paper with Code

** Nuit Blanche is now on Twitter: @NuitBlog ** 



It's the little things. 

In the 2000s, after featuring good work on Nuit Blanche, I was usually following through by asking authors where their codes were. This is how the implementation tag was born. Some of the answers were along the lines of: "I didn't make it available because I thought it was not worthy". But what I usually responded was that, in effect, releasing one's code had a compounding effect on the community: 
"You may not think it's worthy of release, but somehow, someone somewhere needs your code for reasons you cannot fathom"

 As a result, I made a conscious choice of featuring those papers that were actively featuring their implementations. The earliest post with featured implementations was February 28th, 2007 with a blog post featuring three different implementations of reconstruction solver for compressed sensing. Yes, implementations were already available before that, but within the compressive sensing community, it was a point in time with a collective realization that releasing one's code would bring others to reuse one's work and advance the field as a result. At some point, I started making a long list of implementation available but got swamped after a while because it became, most of the time, the default behavior (a good thing).

Five years ago, Samim Winiger started GitXiv around Machine Learning papers. I was ecstatic but the site eventually stopped working. Two years ago, the Paper with code site started around the same issue and flourished. Congratulations to Robert, Ross, Marcin, Viktor, and Ludovic on starting a vibrant community around this need for listing papers with their attendant code. Two days ago, the next logical step occurred with the featuring of codes within ArXiv, a fantastic advance for Science. Woohoo!

Congratulations to RobertRossMarcinViktor, and Ludovic on making this happen! 




My next question is: 
When are they going to get a prize for this?


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Friday, May 29, 2020

Photonic Computing for Massively Parallel AI is out and it is spectacular!




It’s been a long time brewing but we just released our first white paper on Photonic Computing for Massively Parallel AI. The document features the technology we develop at LightOn, some of its use, some testimonials, and how we see the future of computing. It is downloadable here or from our website: LightOn.ai

Enjoy!



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Friday, May 15, 2020

Tackling Reinforcement Learning with the Aurora OPU

** Nuit Blanche is now on Twitter: @NuitBlog **



Martin Graive did an internship at LightOn and decided to investigate how to use Random Projections in the context of Reinforcement Learning. He just wrote a blog post on the matter entitled "Tackling Reinforcement Learning with the Aurora OPU". The attendant GitHub repo is located here. Enjoy!




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Wednesday, April 29, 2020

3-year PhD studentship in Inverse Problems and Optical Computing, LightOn, Paris, France

** Nuit Blanche is now on Twitter: @NuitBlog **


Come and join us at LightOn, we have a 3-year PhD fellowship available for someone who can help us build our future photonic cores. Here is 


As part of the newly EU-funded ITN project “Post-Digital”, LightOn has an opening for a fully-funded 3 year Ph.D. studentship to join its R&D team, at the crossroads between Computer Science and Physics. 

The goal of this 3 year Ph.D. position is to theoretically, numerically, and experimentally investigate how optimization techniques can be used in the design of hybrid computing pipelines, including a number of photonic building blocks (“photonic cores”). In particular, the optimized networks will be used to solve large-scale physics-based inverse problems in science and engineering - for instance in medical imaging (e.g. ultrasound), or simulation problems. The candidate will first investigate how LigthOn’s current range of photonics co-processors can be integrated within task-specific networks. The candidate will then develop a computational framework for the optimization of electro-optical systems. Finally, optimized systems will be built and evaluated on experimental data. This project will be part of LightOn’s internal THEIA project, aiming at automating the design of hybrid computing architectures, including combinations of LightOn’s photonic cores and traditional silicon chips.

In the framework of the EU funded ITN Post-Digital network, this project involves collaborations and 3-month secondments with two research groups led by:
  • Daniel Brunner (Université Bourgogne Franche-Comté / FEMTO-ST Besançon), who will be the academic supervisor - The candidate will be registered as a Ph.D. student at UBFC.
  • Pieter Bienstman (IMEC, Leuven, Belgium).
The supervisor at LightOn will be Laurent Daudet, CTO - currently on leave from his position of professor of physics at Université de Paris.

Due to the EU funding source, please make sure you comply with the mobility and eligibility rule before applying. Application: Position to be filled no later than Sept 1st, 2020.

Send your application with a CV to jobs@lighton.io with [Post-Digital PhD] in the subject line. Shortlisted applicants will be asked to provide references. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 860830.

For more information: https://lighton.ai/careers/


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Tuesday, April 07, 2020

LightOn Cloud 2.0 featuring LightOn Aurora OPUs

** Nuit Blanche is now on Twitter: @NuitBlog **



At LightOn, we just launched LightOn Cloud 2.0 that feature several Aurora Optical Processing Unit for use by the Machine Learning Community. the blog post about this can be found here. You can request access to the Cloud at https://cloud.lighton.ai/

We are also having a LightOn Cloud for Research program: https://cloud.lighton.ai/lighton-research/





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Thursday, March 26, 2020

Accelerating SARS-COv2 Molecular Dynamics Studies with Optical Random Features

** Nuit Blanche is now on Twitter: @NuitBlog **



We just published a new blog post at LightOn. This time, we used LightOn's Optical Processing Unit to show how our hardware can help in speeding up global sampling studies that are using Molecular Dynamics simulations, such as in the case of metadynamics. Our engineer, Amélie Chatelain wrote a blog post about it and it is here: Accelerating SARS-COv2 Molecular Dynamics Studies with Optical Random Features

We showed that LightOn's OPU, in tandem with the NEWMA algorithm, becomes very interesting (compared to CPU implementations of Random Fourier Features and FastFood) for simulations featuring more than 4 000 atoms.
  

Because building computational hardware makes no sense if we don't have a community that lifts us, the code used to generate the plots in that blog post is publicly available at the following link: https://github.com/lightonai/newma-md.

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Saturday, March 14, 2020

Au Revoir Backprop ! Bonjour Optical Transfer Learning !

** Nuit Blanche is now on Twitter: @NuitBlog **


We recently used LightOn's Optical Processing Unit to show how our hardware fared in the context of Transfer learning. Our engineer, Luca Tommasone wrote a blog post about it and it is here: Au Revoir Backprop! Bonjour Optical Transfer Learning!

Because building computational hardware makes no sense if we don't have a community that lifts us, the code used to generate the plots in that blog post is publicly available at the following link: https://github.com/lightonai/transfer-learning-opu.




Enjoy and most importantly stay safe !





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Wednesday, January 15, 2020

Beyond Overfitting and Beyond Silicon: The double descent curve

** Nuit Blanche is now on Twitter: @NuitBlog **

We recently tried a small experiment with LightOn's Optical Processing Unit on the issue of generalization. Our engineer, Alessandro Cappelli, did the experiment and wrote a blog post on it and it is here: Beyond Overfitting and Beyond Silicon: The double descent curve 

Two days ago, Becca Willett was talking on the same subject at the Turing Institute in London.

A function space view of overparameterized neural networks Rebecca Willett.



Attendant preprint is here:

A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case by Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro
A key element of understanding the efficacy of overparameterized neural networks is characterizing how they represent functions as the number of weights in the network approaches infinity. In this paper, we characterize the norm required to realize a function f:RdR as a single hidden-layer ReLU network with an unbounded number of units (infinite width), but where the Euclidean norm of the weights is bounded, including precisely characterizing which functions can be realized with finite norm. This was settled for univariate univariate functions in Savarese et al. (2019), where it was shown that the required norm is determined by the L1-norm of the second derivative of the function. We extend the characterization to multivariate functions (i.e., networks with d input units), relating the required norm to the L1-norm of the Radon transform of a (d+1)/2-power Laplacian of the function. This characterization allows us to show that all functions in Sobolev spaces Ws,1(R)sd+1, can be represented with bounded norm, to calculate the required norm for several specific functions, and to obtain a depth separation result. These results have important implications for understanding generalization performance and the distinction between neural networks and more traditional kernel learning.


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