In light of recent events, Bucharest School of AI is seizing its affiliation with Siraj’s School of AI.

What started as a small community of hobbyists has grown to be one of the largest series of AI workshops in Romania, connecting students and developers in monthly events facilitated by speakers from local universities and companies.

We are encouraging other chapters across the globe to follow through and commit to “inspiring and educating” people through integrity and professionalism, features required even from community-run events.

I work in a highly specific field, where ML and Stats knowledge is important, but domain and business knowledge are also absolutely crucial to being able to do anything of substance.

A friend of mine who works in the same field, and is really good at what he does, was recently contacted by a major tech company to be an ML Solutions architect, based on his experience with some of the latest cloud platforms. The role they are hiring him for is “general” ML Solutions Architect, not tied to any specific domain: In theory he should be able to go to anyone of their clients, whether a bank, or a manufacturer, or a mobile gaming company, or a retailer, etc…and be able to advise them on how to build ML solutions in the cloud.

I’m kind of surprised by this: You might be able to port ML development skills or data engineering skills from one domain to another, but Solution Architecture is a completely different beast, and I don’t see how somebody, know matter how smart and adaptable they are, can make informed decisions about an enterprise ML architecture without deep experience in that particular domain.

What are the skills that are specific to ML Solution Architecture (as opposed to being just general ML or Data Engineering knowledge) yet are same time are not domain specific and applicable to general enterprise contexts?!?!

As a reviewer, chair and general academic, I’m beginning to notice a pattern where a paper will get rejected from NeurIPS, ICLR, ICML only to end up in PNAS later on. I won’t name specific papers, but it has happened at least on a few occasions directly in my own experience. Furthermore, the are not any substantial modifications to the work either. Has anyone else noticed this pattern as well?

PNAS is a reputable journal, but they do not have the computational/technical reviewer pools that are aware of the work going on in the AI/ML community. So I suspect that some are taking advantage of this.

Hey guys,

I’ve been working on implementing some basic deep learning architectures in C. I saw a submission a little while ago about LSTMs in C, and thought I’d share my own implementation, which includes support for GPU computation through OpenCL. Heres’ the repo:

https://github.com/siekmanj/sieknet

I’ve seen numerous guides online discussing face and facial landmark detection. Sometimes these guides also overlay an image attached to one or multiple of these landmarks. None however, promise real-time mapping of 3D models to 3D face meshes. Yet Snapchat manages to do this with nearly zero latency on mobile devices. What are some tricks to achieve similar performance?

I just thought of this as a complex, multifaceted problem. I would love and try to recreate Snapchat lenses in an open source project.

Anyone with experience with similar problems who can elaborate on some of the key components?

I’ve seen claims that my Neural Qubit paper was partly plagiarized. This is true & I apologize. I made the vid & paper in 1 week to align w/ my “2 vids/week” schedule. I hoped to inspire others to research. Moving forward, I’ll slow down & being more thoughtful about my output

What do you guys think about this?

nn_builder is a package that lets you build simple NNs, CNNs and RNNs in 1 line by reducing the amount of boilerplate code. It works with PyTorch and TensorFlow 2.0. Check it out!

https://github.com/p-christ/nn_builder

To be clearer, I assume that we only consider Supervised paradigm and Classification task (of course, if there is some literature on other paradigms and tasks, please share).

We all know that there is a plethora of adversarial attacks AND defenses on neural network. Unfortunately (or fortunately), most of the defenses have been debunked (thanks to the papers like https://arxiv.org/pdf/1802.00420.pdf), and Adversarial Training (AT) is generally the “best” defense so far (it’s NOT very effective against attacks, but it’s generally better than other fancy defenses).

However, it seems like (I can be wrong here) AT has not been compared to the defenses in a specific type, which uses the smoothness of neural network function and decision boundaries to prevent attacks from finding adversarial examples (I know there is definitely this type of defense, although I cannot recall any paper on top of my head).

So I guess my overall question is that “Are those defenses comparable to AT?”, which in turn means “Which are the best attacks against those defenses?” and “Are those attacks less effective against AT?”.

P.S: Please share some literature if possible. Thanks!

Hi,I keep bumping into the problem of doing regression/classification on data where the input data is an unordered set. For instance a set of coordinates or a set of atoms or similar, where there is no obvious ordering of the atoms/points. PointNet can be used for these kinds of problems, but does not always perform well.

See for instance this post: https://www.kaggle.com/c/champs-scalar-coupling/discussion/106468#latest-646125

I was wondering if anyone could point me in the right direction on how to use the Transformer for this…

That is extract an embedding with shape (N, ) from a set of data with shape (M, N)