jklontz

> For example, NN-512 can exceed 48 effective FMADDs per cycle (on the 27 peak FMADD machine) with Winograd-Cook-Toom-Lavin, if the tensor is deep enough (enough channels)

Roughly how many channels do you need for this approach to be worthwhile?

Would you share this no-knead recipe?

The AWS Rekognition study I believe you are referring to was fundamentally flawed in two ways.

First, the threshold used was the default suggested for one-to-one matching (lower false reject rate, higher false accept rate). It doesn't make sense to use this threshold for search applications, and when the study was reproduced with a more appropriate (higher) threshold there were 0 mis-identifications.

Second, law enforcement use of face recognition doesn't even involve the algorithm making a lights-out identification decision. Instead, the most similar faces are presented to the user in ranked order by similarity (like a search engine). It's a tool for generating investigative leads, often preferable to publishing the face image of a wanted perpetrator on local news.

I've seen this argument made a lot, but have never found it convincing. Is there any empirical evidence to back it up? A quick Google search suggests it is a myth.

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Rank One Computing is an employee-owned small business specializing in computer vision and facial recognition algorithm development. Our technology is licensed by numerous organizations around the world, and our facial recognition SDK is increasingly found to be second to none.

We are interested in hiring a full-stack web developer to satisfy both client projects and internal R&D demands:

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30 minute phone screening followed by onsite interview.

Flexible work schedule, competitive salary commensurate with experience and equity. Come ski with us when we close the office for a powder day!

Learned something new, fixed thanks!

Also in Denver! Would be thrilled to hang out with any fellow HN folks. E-mail in profile :)

True, but unlike other commodities it's easy to take a long position on gold with ETFs like GLD which actually store the physical gold. For other commodities (like oil with ticker USO) it's not so simple to take a long position. The best you can do is trade options, with the added risk of a contango market.

Targeting FPGAs is something we've been keeping our eye on as well. Most of the pieces are in place. I think the last non-trivial part is removing any malloc()/free() calls. This should be possible as the size and lifetime of memory allocations are known at compile time and can be either moved to the stack or made global as desired.

One of the reasons I opted against Halide myself was the feeling that for this domain there ought to be enough information available to the compiler to intelligently pick tiling and vectorization parameters. For example, using Polly [0]. However in practice, manual tiling and vectorization is hard to beat.

[0] http://polly.llvm.org/

Going to take this opportunity to plug my related project [Likely](www.liblikely.org), a DSL for lowering machine learning inference algorithms.

One of the projects we've built on top of it is a static compiler for Caffe model files. This allows you to execute Caffe models _without_ a runtime dependency on Caffe libraries. Thus you can target OSes and CPUs not supported by mainline Caffe. If you have commercial interest in this capability please reach out to me.

Agreed! Instead we use a Slack channel called #todayiwill and people just enter a few bullet points when they get in.

Very exciting! I wonder how the short-term nature of the pilot program might have some inherent biases compared to the long-term study? Presumably there would be less dramatic changes in behavior / lifestyle?

Seems like the perfect opportunity to insert a plug for my project: http://liblikely.org/

Yes! And I would also add that:

> LLVM IL is more cluttered with type annotations and casts.

With LLVM moving to typeless pointers[1], this will be less true in the future than it is now.

[1] https://groups.google.com/forum/#!topic/llvm-dev/vphBegBWyyE

We published a paper on this a few years ago! Short answer: algorithms exhibit different accuracies on different races, genders, and ages.

B. F. Klare, M. J. Burge, J. C. Klontz, R. W. Vorder Bruegge and A. K. Jain, "Face Recognition Performance: Role of Demographic Information," in IEEE Transactions on Information Forensics and Security, vol. 7, no. 6, pp. 1789-1801, Dec. 2012.

http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA556941

The people I know have been notified.

Replacing my workstation with a high-end laptop removed all but the power cord for me ;)

You might be interested in [Likely](http://liblikely.org/), the programming language I've been working on. One of its core tenants is that "training == compilation". I haven't given much thought to departures from von Neumann style architectures, but with emerging/de-facto standards like OpenCL and LLVM I don't see any reason why, at least for computer vision algorithms (my domain) we can't train/compile to FPGA.

This is _not_ a post about auto-vectorization in Rust (which would have been a lot more interesting!). The provided Mandelbrot Set example is algorithmically similar to loop unrolling with a 4x unroll factor. The strategy works well in this case because neighboring locations in the Mandelbrot Set tend to require similar numbers of iterations to compute.

WebAssembly Backend RFC on the LLVM mailing list:

http://llvm.1065342.n5.nabble.com/RFC-WebAssembly-Backend-td...

GitHub: I've emailed their support desk a half dozen times over the years, and each time received a response from an engineer within minutes containing an accurate and precise answer to my question.

For reference, current DDR4 RAM to CPU is ~0.5 Tb/s [0].

[0] http://ark.intel.com/products/81059/Intel-Xeon-Processor-E5-... (see "Max Memory Bandwidth")

Yes! For those interested in a DIY project, I would add that "Permaculture: A Designer's Manual" [0] is arguably the reference for these techniques.

[0] http://www.amazon.com/Permaculture-Designers-Manual-Bill-Mol...

Your weekly is best part of my Monday morning, glad to hear it will be around in 2015 :)

OpenBR does gender & age (probably pose in 2015). YMMV though, current algorithm is very dependent on an "ID card" quality photo.

OpenBR author here, thanks for the plug!

"Liveness detection" is an active area of research within the community. I know you can enable this check on Android and it works moderately well.

The OpenBR API is designed to cater to the researchers and users of biometric algorithms, where as the OpenCV API is designed to solve a more general scope of image processing problems.

OpenCV includes implementations of several seminal face recognition algorithms (PCA, LDA, & LBP). The OpenBR algorithm is more modern/sophisticated in its combination of several well studied approaches. While I haven't explicitly compared OpenCV against OpenBR, the OpenCV algorithms can and have been expressed within the OpenBR and are known to be less accurate. Having said that, accuracy & algorithm choice are highly dependent on image quality. Our technical paper [1] provides more details.

[1] http://openbiometrics.org/publications/klontz2013open.pdf

Please feel free to ask questions on our mailing list [1] or IRC channel [2]. Our software dependencies are Qt & OpenCV, both of which compile for Andriod, so OpenBR should be able to run there also, but no one has tried yet.

[1] https://groups.google.com/forum/?fromgroups#!forum/openbr-de...

[2] irc.freenode.net #openbr

It's actually 'Primary author wanted to spend some time in academia but continue to working on the project'.