In ML research, we often have tons of cache files to read from disk. As a project progesses, I often end up with lots of cache files which I don’t remember how they were created. My current way to keeping track of cache files and preprocessed files is by writing it down when it is created and why.

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I am wondering if anyone has a way to automate this process? I heard something like DVC (https://github.com/iterative/dvc), but it seems to be too complicated.

https://www.youtube.com/watch?v=0pMfmo8qfpQ

Now that ICCV is right around the corner, I made a trailer to hype up (and to show off its capabilities) my paper about real-time instance segmentation that I posted here a while ago. Hope you like it!

Attacking Optical Flow

TLDR: Corrupting a small patch of less than 1% of the image size lead to noisy flow estimates that extend beyond the region of the attack, even erasing the motion of objects in the scene in some cases

Anurag Ranjan, Joel Janai, Andreas Geiger, Michael J. Black

(Submitted on 22 Oct 2019)

Deep neural nets achieve state-of-the-art performance on the problem of optical flow estimation. Since optical flow is used in several safety-critical applications like self-driving cars, it is important to gain insights into the robustness of those techniques. Recently, it has been shown that adversarial attacks easily fool deep neural networks to misclassify objects. The robustness of optical flow networks to adversarial attacks, however, has not been studied so far. In this paper, we extend adversarial patch attacks to optical flow networks and show that such attacks can compromise their performance. We show that corrupting a small patch of less than 1% of the image size can significantly affect optical flow estimates. Our attacks lead to noisy flow estimates that extend significantly beyond the region of the attack, in many cases even completely erasing the motion of objects in the scene. While networks using an encoder-decoder architecture are very sensitive to these attacks, we found that networks using a spatial pyramid architecture are less affected. We analyse the success and failure of attacking both architectures by visualizing their feature maps and comparing them to classical optical flow techniques which are robust to these attacks. We also demonstrate that such attacks are practical by placing a printed pattern into real scenes.

Abs: https://arxiv.org/abs/1910.10053

Site: https://flowattack.is.tue.mpg.de

Vid: https://youtu.be/5nQ7loiPmdA

Hello, I have some basic experience with machine learning using Python, but I’ve been unable to find a guide on how to do what I’d like to do. That is, I’d like to train a model using my collection of audio files (hundreds of thousands of MP3s, WAVs, and FLACs) and output new audio files. I would greatly appreciate any help or points in the right direction!

What do you think is the best open-source model management framework and why? I’ve looked into ModelDB, Polyaxon, studio.ml, datmo, etc., but I’m not sure which one people consider the best/most robust. Have you guys used or tried any, and if so, what did you like and not like?

https://www.kaggle.com/c/data-science-bowl-2019

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Uncover new insights in early childhood education and how media can support learning outcomes.

PBS KIDS, a trusted name in early childhood education for decades, aims to gain insights into how media can help children learn important skills for success in school and life. In this challenge, you’ll use anonymous gameplay data, including knowledge of videos watched and games played, from the PBS KIDS Measure Up! app, a game-based learning tool developed as a part of the CPB-PBS Ready To Learn Initiative with funding from the U.S. Department of Education. Competitors will be challenged to predict scores on in-game assessments and create an algorithm that will lead to better-designed games and improved learning outcomes. Your solutions will aid in discovering important relationships between engagement with high-quality educational media and learning processes.

In the PBS KIDS Measure Up! app, children ages 3 to 5 learn early STEM concepts focused on length, width, capacity, and weight while going on an adventure through Treetop City, Magma Peak, and Crystal Caves. Joined by their favorite PBS KIDS characters, children can also collect rewards and unlock digital toys as they play.

https://youtu.be/QoP2Hu_RQcU

Above is an update to an ongoing “Applied-ML” project of mine.

This is a pan tilt turret equipped with an infrared depth camera that is being guided by YOLOv3 in ROS to track “Human heads”. I trained YOLO using Google OpenImages V4, and used pirobot’s code for “Robotics By Example, volume 2”, leggedrobotic’s darknet_ros, and my own headtracker node to take the 2D data for the bounding boxes from YOLO and retrieve the 3D data associated with specific depth registered RGB pixel coordinates for tracking.

The detection is much smoother in this release, although at about :12 in the video here, it jolts hard to the right in error (likely an error in lead and/or joint speed update, should be easy to resolve).

YOLO performs significantly better on the NVidia Tesla k40 that I’m using here as well, upgraded from the GTX1060 in my previous post. I’m also using a calibrated Orbbec Astra Pro instead of the Kinect 360 as well. The depth registration of the RGB as well as the stability of the detection has noticably improved.

I plan to begin the challenge of designing a rudimentary implementation of “visual dialogue” with this in an eventual upcoming upgrade. Ideally, I want this to be able to not just hold somewhat of a conversation, but be able to look around a room at objects that it’s capable of detecting, use SLAM to store their location, and interact with people and the world around it verbally and within context (an example being “what is that cat behind you doing?” and have it respond with looking for said cat, tracking and mapping it’s location, and generating a verbal response).

Stay tuned for more updates; the next will be a bit more exciting!

Link to the first release of this bot and description of the underlying technology is below:

https://www.reddit.com/r/MachineLearning/comments/dik1lr/p_my_implementation_of_object_tracking_using_an/

https://github.com/wangyi-fudan/wymlp

MLP is a bit old, however it is mature to be deployed in industry. This repo has two purposes: a minimal C++ MLP code for education and the real time performance for the industry/IoT. There are several good points:

0: It uses standard C++ code, no magic instruction. Thus is portable to most machines.

1: It use c++ templates, thus inlines everything. It works like a pre-defined static function, pure stream of float point instructions.

2: It works by SGD of 1 sample each time. Thus it enables real time learning and prediction which is useful for future industry. The training “FPS” can reach 100k for a 32-hidden,16-layer network, eg. We can learn and predict each WAV frame as it arrives.

3: It use shared hidden-hidden weights. In fact it is similar to RNN making use of marginal chaos. This reduces the size of network to the cache without loss of accuracy.

4: the activation function used is y=x/(1+|x|) which is sigmoid like. It and its gradient are fast to calculate and not easily saturated.

5: experiment shows that only a single CPU thread is needed, and more threads just not improve the speed due to memory bound.

6: for >=32 hidden units, gcc autovectorization will turn it to SSE/AVX code, which is 4X faster.

7: the float point type is a template parameter, float/double/long double are OK.

Hope you like it!

https://spectrum.ieee.org/the-human-os/biomedical/ethics/racial-bias-found-in-algorithms-that-determine-health-care-for-millions-of-patients

The algorithm was performing its task correctly — it accurately predicted future health costs for patients to determine which ones should get extra care. But it still ended up discriminating against black patients.

A really interesting read (and interview with Topcoder CEO Mike Morris and Dr. Raymond H. Mak of Harvard Medical Schooll) on a collaboration between Harvard Medical School and Topcoder on the tremendous progress that they have made in identifying Lung Cancer.

Well worth a read, and a follow up to JAMA Oncology’s groundbreaking study that revealed that AI / data science can detect cancer tumors faster, more effectively than humans (https://jamanetwork.com/journals/jamaoncology/fullarticle/2730638).

Article: https://community.turgensec.com/machine-learning-for-curing-lung-cancer/

Edit: Grammar and spelling