Say, for example, I have a goal to do a multi-task learning workflow where I have a network that predicts both foreground/background segmentation, specific object segmentation, and bounding box regression.

Suppose that there are only labels for 1-2 of the tasks per image (i.e. one input image has labels for bounding boxes and fg/bg segmentation, but no specific object segmentation; one input image has specific objects and bounding boxes, but no fg/bg). Also, suppose that there isn’t a lot of training data to use, so I would like to utilize all of the training data even without full label coverage (especially because the feature extraction portions of the network probably benefit from mutual information).

Is there an area of research or some best practices to train an entire network end-to-end in this regard? Something involving finetuning/turning training ‘off’ for certain branches of the network related to the parts that don’t have labels for that input image?

The lecture videos from Advanced Machine Learning from ETH Zurich Autumn 2018 were previously available in the youtube link below, but they removed it. I was wondering if someone managed to save a copy and would be willing to share it? =)

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https://www.youtube.com/playlist?list=PLzn6LN6WhlN1x68-5GEzAflTBQrSoKcAJ

Say you are evaluating an AI researcher or a ML engineer in a company; what would you constitute as (realistic) high quality research or technical work from them? How can you tell a good AI practitioner from a bad one? How about a great one from good ones? What advice can you give for somebody if they wish to increase the value that they can provide to their company?

I am working on the problem of generation of plausible looking layout regions. Right now we are working by generating the region areas as rectangles, but want to extend it to polygons. Working on rectangles is easy since it can be uniquely represented using a fixed number of parameters (x and y coords of top left corner, width and height). Is there any way to represent general polygons using some fixed number of parameters?

Predicting Academic Collaboration with Logistic Regression

My capstone project for my last year of undergrad has to do with Social Network Analysis of networks formed by co-authorship in research communities. Since I have a growing interest for machine learning I wanted to see if I could apply various machine learning methods to the networks in order to gather more insight into how they work. In this post I applied a logistic regression model in order to predict future collaboration, and it seemed to be quite accurate over the test set! I would love any feedback on the post, and I hope you enjoy!

Project link: https://github.com/zli117/Evolution

Hi there, I made a CNN evolution framework for Keras. It encodes network architectures in hierarchical graphs, inspired by this paper. It also supports training multiple sessions in parallel on the same or multiple GPUs for speed. Here is an example usage on cifar10.

I am trying to read more papers and write more myself. In order to accomplish this goal I have decided to read papers on a regular basis and write small essays containing notes on the paper. I decided to start with Google’s BERT paper, which came highly recommended in a WAYR thread here.

I would love to hear any feedback you have on these notes so that I may improve my reading and writing skills.

https://stephanheijl.com/notes_on_bert.html

• Project link: https://github.com/Curt-Park/rainbow-is-all-you-need

Hi, ML redditors all around the world! I made a Pytorch RL tutorial which consists of all methods from DQN to Rainbow:

1. DQN
2. Double DQN
3. Prioritized Experience Replay
4. Dueling Networks
5. Noisy Network
6. Categorical DQN (C51)
7. N-step Learning
8. Rainbow

Every chapter contains both theoretical backgrounds and object-oriented implementation, and thanks to Colab, you can execute them and render the results without any installation even on your smartphone!

I hope it will be helpful for someone. 🙂

Cheers.

https://github.com/graykode/horovod-ansible

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Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. It’s good distributed training framework but it’s a hassle to set the environment for all nodes. So i make automation script with Ansible.

You can train with Tensorflow, Pytorch Example on AWS or On-Perm Thanks