Tuesday, December 29, 2020

The Awesome Implicit Neural Representations Highly Technical Reference Page

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

Here is a new curated page on the topic of Implicit Neural Representations aptly called Awesome Implicit Neural Representations. It is curated by Vincent Sitzmann (@vincesitzmann) and has been added to the Highly Technical Reference Page:





From the page:

A curated list of resources on implicit neural representations, inspired by awesome-computer-vision. Work-in-progress.

This list does not aim to be exhaustive, as implicit neural representations are a rapidly evolving & growing research field with hundreds of papers to date.

Instead, this list aims to list papers introducing key concepts & foundations of implicit neural representations across applications. It's a great reading list if you want to get started in this area!

For most papers, there is a short summary of the most important contributions.

Disclosure: I am an author on the following papers:

What are implicit neural representations?

Implicit Neural Representations (sometimes also referred to coordinate-based representations) are a novel way to parameterize signals of all kinds. Conventional signal representations are usually discrete - for instance, images are discrete grids of pixels, audio signals are discrete samples of amplitudes, and 3D shapes are usually parameterized as grids of voxels, point clouds, or meshes. In contrast, Implicit Neural Representations parameterize a signal as a continuous function that maps the domain of the signal (i.e., a coordinate, such as a pixel coordinate for an image) to whatever is at that coordinate (for an image, an R,G,B color). Of course, these functions are usually not analytically tractable - it is impossible to "write down" the function that parameterizes a natural image as a mathematical formula. Implicit Neural Representations thus approximate that function via a neural network.

Why are they interesting?

Implicit Neural Representations have several benefits: First, they are not coupled to spatial resolution anymore, the way, for instance, an image is coupled to the number of pixels. This is because they are continuous functions! Thus, the memory required to parameterize the signal is independent of spatial resolution, and only scales with the complexity of the underyling signal. Another corollary of this is that implicit representations have "infinite resolution" - they can be sampled at arbitrary spatial resolutions.

This is immediately useful for a number of applications, such as super-resolution, or in parameterizing signals in 3D and higher dimensions, where memory requirements grow intractably fast with spatial resolution.

However, in the future, the key promise of implicit neural representations lie in algorithms that directly operate in the space of these representations. In other words: What's the "convolutional neural network" equivalent of a neural network operating on images represented by implicit representations? Questions like these offer a path towards a class of algorithms that are independent of spatial resolution!..........





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Monday, December 21, 2020

Hardware Beyond Backpropagation: a Photonic Co-Processor for Direct Feedback Alignment

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

We presented this work at the Beyond Backpropagation workshop at NeurIPS. A great conjunction between computational hardware and algorithm! 




The scaling hypothesis motivates the expansion of models past trillions of parameters as a path towards better performance. Recent significant developments, such as GPT-3, have been driven by this conjecture. However, as models scale-up, training them efficiently with backpropagation becomes difficult. Because model, pipeline, and data parallelism distribute parameters and gradients over compute nodes, communication is challenging to orchestrate: this is a bottleneck to further scaling. In this work, we argue that alternative training methods can mitigate these issues, and can inform the design of extreme-scale training hardware. Indeed, using a synaptically asymmetric method with a parallelizable backward pass, such as Direct Feedback Alignement, communication needs are drastically reduced. We present a photonic accelerator for Direct Feedback Alignment, able to compute random projections with trillions of parameters. We demonstrate our system on benchmark tasks, using both fully-connected and graph convolutional networks. Our hardware is the first architecture-agnostic photonic co-processor for training neural networks. This is a significant step towards building scalable hardware, able to go beyond backpropagation, and opening new avenues for deep learning.



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Saturday, December 19, 2020

Diffraction-unlimited imaging based on conventional optical devices

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


Aurélien sent me an email back in October and we are now in December! Time flies.
Dear Igor,


I hope things are well.

I have been following your NuitBlanche blog for quite a few years. It would thus be great for us if you consider a recent paper of ours to appear in your blog, entitled “Diffraction-unlimited imaging based on conventional optical devices”. This paper has been published in Optics Express this year and its link is: https://www.osapublishing.org/oe/abstract.cfm?uri=oe-28-8-11243

This manuscript proposes a new imaging paradigm for objects that are too far away to be illuminated or accessed, which allows them to be resolved beyond the limit of diffraction---which is thus distinct from the microscopy setting. Our concept involves an easy-to-implement acquisition procedure where a spatial light modulator (SLM) is placed some distance from a conventional optical device. After acquisition of a sequence of images for different SLM patterns, the object is reconstructed numerically. The key novelty of our acquisition approach is to ensure that the SLM modulates light before information is lost due to diffraction.

Feel free to let us know what you think, and happy to provide more information/pictures if needed. Thanks a lot for your time and consideration!


Best regards,

Aurélien Bourquard

Thank you Aurélien


 Here is the paper's abstract:




We propose a computational paradigm where off-the-shelf optical devices can be used to image objects in a scene well beyond their native optical resolution. By design, our approach is generic, does not require active illumination, and is applicable to several types of optical devices. It only requires the placement of a spatial light modulator some distance from the optical system. In this paper, we first introduce the acquisition strategy together with the reconstruction framework. We then conduct practical experiments with a webcam that confirm that this approach can image objects with substantially enhanced spatial resolution compared to the performance of the native optical device. We finally discuss potential applications, current limitations, and future research directions.

I note that Aurélien has also published some exciting research on Differential Imaging Forensics. His co-author Nicolas has also some interesting work on Single Pixel cameras.




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Wednesday, December 09, 2020

LightOn at #NeurIPS2020

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


I posted the following on LightOn's Blog.




We live in interesting times!

A combination of post-Moore’s law era and the advent of very large ML models require all of us to think up new approaches to computing hardware and AI algorithms at the same time. LightOn is one of the few (20) companies in the world publishing in both AI and hardware venues to engage both communities into thinking how theories and workflows may eventually be transformed by the photonic technology we develop.

This year, thanks to the awesome Machine Learning team at LightOn, we have two accepted papers at NeurIPS, the AI flagship conference, and have five papers in its“Beyond Backpropagation” satellite workshop that will take place on Saturday. This is significant on many levels, not the least being that these papers have been nurtured and spearheaded by two Ph.D. students (Ruben Ohana and Julien Launay) who are doing their thesis as LightOn engineers.

Here is the list of the different papers accepted at NeurIPS this year that involved LightOn members:


And at the NeurIPS Beyond Backpropagation workshop taking place on Saturday, December 12:


  • Hardware Beyond Backpropagation: a Photonic Co-Processor for Direct Feedback Alignment, Julien Launay, Iacopo Poli, Kilian Muller, Igor Carron, Laurent Daudet, Florent Krzakala, Sylvain Gigan
  • Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures, Julien Launay, François Boniface, Iacopo Poli, Florent Krzakala (Presenter: Julien Launay).
  • Ignorance is Bliss: Adversarial Robustness by Design through Analog Computing and Synaptic Asymmetry, Alessandro Cappelli, Ruben Ohana, Julien Launay, Iacopo Poli, Florent Krzakala (Presenter: Alessandro Cappelli). We had a blog post on this recently.
  • Align, then Select: Analysing the Learning Dynamics of Feedback Alignment, Maria Refinetti, Stéphane d’Ascoli, Ruben Ohana, Sebastian Goldt paper (Presenter: Ruben Ohana).
  • How and When does Feedback Alignment Work, Stéphane d’Ascoli, Maria Refinetti, Ruben Ohana, Sebastian Goldt. paper (Presenter: Ruben Ohana)

Some of these presentations are given in French at the “Déjeuners virtuels de NeurIPS”


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