[mlpack] Fwd: Essential Deep Learning Modules in GSoC '19

[Rajiv Vaidyanathan]

Hi Marcus,

I did some more research about the Graph Neural Networks. Since I'm new to
GNNs, I am not sure about how long it will take to implement all the
necessary layers. Hence, I thought I'll work on something which I'm already
familiar about, which is convolutional neural networks. I'm extremely
interested in working on methods used for object detection. Object
detection algorithms such as R-CNN, YOLO, etc. have become very popular due
to their speed and accuracy. I feel that it would be a great addition to
the MLPack library.

I want to implement the following networks along with tests and
documentation:
1. R-CNN and it's variants such as fast RCNN, Faster RCNN and Mask-R-CNN
2. YOLO

For their implementation, the following have to be implemented:
1. ROI pooling
2. Region Proposal Network
3. Techniques: non-max suppression, intersection over union and anchor boxes

Please let me know what you think about this. If you are fine with this
idea, I'll do more research and make a brief proposal as to what needs be
precisely done with respect to the MLPack code along with a rough timeline.

Thanks and regards,
Rajiv
ᐧ

On Sat, 2 Mar 2019 at 03:23, Marcus Edel <marcus.edel at fu-berlin.de> wrote:

> Hello Rajiv,
>
> thanks again for the contributions. Implementing Graph Neural Networks is
> quite
> a challenge especially timewise but if you are up for that; we should make
> sure
> the timeline is reasonable including the milestones. Everything needs to be
> tests and stable at the end of the summer which often takes a lot of time,
> but
> as I said if you are up for the challenge this could be an interesting
> project
> for sure.
>
> Let us know what you think.
>
> Thanks,
> Marcus
>
> On 28. Feb 2019, at 19:14, Rajiv Vaidyanathan <
> rajiv.vaidyanathan4 at gmail.com> wrote:
>
> Dear Marcus, Mikhail and Shikhar,
>
> I am N Rajiv Vaidyanathan and I'm interested in the topic "Essential Deep
> Learning Modules" in GSoC 19.
>
> Over the past month, I'm trying to get an understanding of the MLPack
> codebase by making contributions. As of now, I have implemented SPSA
> optimiser, Dice Loss function and currently working on Dense blocks.
>
> I recently read a paper on Graph Neural Networks and found it to be
> fascinating as it works very well on non-Euclidian spaces such as social
> networks and 3D images. I am interested in working on implementing this
> network along with tests and documentation. I'm also interested in
> improving the overall documentation of ann.
>
> Please let me know what you think :)
>
> Thanks and regards,
> Rajiv
> ᐧ
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>
> ᐧ
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