PhD Student at UMBC - Healthcare AI and Computer Vision
opencv
I remember when pjreddie released You-Only-Look-Once (YOLO) several years ago, and installing darknet (https://github.com/pjreddie/darknet). I also laughed when I read his hilarious licensing agreement LICENSE.fuck (https://github.com/pjreddie/darknet/blob/master/LICENSE.fuck). Well, Yolo continues to give back. And now in 2022 we
pytorch
Consider that you have a pair of images you want to train. In my case, it's a GAN, where I'm improving upon older work from 2020 in the facial-visible-to-thermal GAN [https://github.com/nudro/favtgan]. My paired image consists of a visible and thermal face, simultaneously
coding
I'm embarrassed to say that even at this point in my life, I still have to look up this formatting to crop an image. Once and for all, I'll post it here so that I don't forget it and also in case you'
coding
You'll eventually face a moment where you're going to have to read through an (at-first) complicated set of AI code. You might need to leverage another expert's repo in order to build an experiment. You might be in a company, and found a great
gans
This semester I'm taking a course at UMBC called Causality in Machine Learning by Dr. Osman Gani [https://osmanii.github.io/]. So far, we've studied Simpson's Paradox, Causality and the Potential Outcomes Framework, Average Treatment Effect, Ignorability, Exchangability, Identifiability, Consistency, Structural Causal Models (i.
thermal
Below is an excerpt from our paper Ordun, Catherine, Edward Raff, and Sanjay Purushotham. "The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data [https://arxiv.org/pdf/2009.10589.pdf]." arXiv preprint arXiv:2009.10589 (2020) for AAAI
gans
Introduced by Goodfellow, et al. (Goodfellow) in 2014, a GAN consists of two functions, a generator and a discriminator, which are usually neural networks but can be any differentiable function. GANs are used to map data from one space to another; typically from a generated distribution \(p_g\) to an
decision tree
Decision trees are one of the most fundamental algorithms in machine learning. Let's review how to generate one from scratch. Consider the following table about three different attributes about the ripeness of a fruit. We have density, size, and firmness. Our target is "Ripe" or "
convnet
> A paper review of Yosinski, Jason, et al. "How transferable are features in deep neural networks?." Advances in neural information processing systems. 2014. [https://proceedings.neurips.cc/paper/2014/file/375c71349b295fbe2dcdca9206f20a06-Paper.pdf] Slides available here [https://docs.google.com/presentation/d/1uZuEPV_Y3apUpZ6DGUkHwpW9m5RwF2QOfqYpKxnzRFk/edit?usp=sharing] . Summary
papers
It's been a very long time since I've last posted, and at last I've come up for some air after writing a few papers and a lot of code. One thing I've come to appreciate in my PhD studies is the frequent
gans
AutoGAN is an architecture search strategy developed by Gong, et al. in 2019 to find the most optimal Generator network architecture using reinforcement learning, trained to generate images from CIFAR-10 and STL-10. To understand how AutoGAN works requires an understanding of the basics of Neural Architecture Search (NAS). This post
object detection
Several years ago I implemented GoogLeNet on the Jetson TX2. Since then I've been dealing with object detection problems both from a data scientist side, and also team leader side. > Finally got the #Jetsontx2 [https://twitter.com/hashtag/Jetsontx2?src=hash&ref_src=twsrc%5Etfw] up