The latest version of mlfinlab (Github) has been released.

A package based on the book: Advances in Financial Machine Learning by Marcos Lopez de Prado.

“`pip install mlfinlab“`

This new release is our biggest to date and includes code for:

1. Sequentially Bootstrapped Ensembles (Regression & Classification)
2. Purged Cross-Validation
3. Feature Importance
4. Bet Sizing (+ EF3M)
5. ML Asset Allocation (HRP, CLA, IVP)

Documentation can be found on Read-the-Docs.

Some of us work in image synthesis / translation problems where we can’t use Amazon Mturk.

We need to show people some images and we want them to score / rank each picture or pair or panel etc

Our current approach to this is to create a stack of images or PDF and share it along with an excel etc.

Question 1: is there a tool that we can use to have users see a panel of images and rank/score from a drop down etc? I know we can write our own gui / interface but seems a bit redundant. Is there a simpler way?

Question 2: you can see how the above won’t scale to many different users . Are you aware of any web service / tool instead? (*)

• one time I did this as a Google form and send it to our specialist pool and worked out wonders but I had to hand do it. It seems that Google api has a scripting language to automate things (which means we may be able to automate part) but Google forms has restrictions in the width of images that can be shown so it affects the range of applications.

When reading the Progressive growing of gans by Karras at Nvidia they claim that they get good representations on an cifar10 unconditionally trained. I thought mode collapse for multiclass datasets was still mostly unsolved for gans. Is this partially solved with their models or am I missing something? Why is this considered such a hard problem and how does their results relate to the area?

Hi! I have been learning and working on machine learning with python for a few months alredy. Since the beginning I have been using spyder, but I find it ugly and non aesthetic at all. I really like the ide itself, just dislike its appearance. I would like to find something like sublime text but with a command terminal. Do you know any? Thanks in advance

Context: Telecommunication Networks are complex multi-vendor/technology (Huawei, Ericsson, Nokia, …) which NOC (Network Operation Center) continually monitor and respond to faults/failures on equipment & sites usually concentrating all alarms from different vendors.

Challenge: Use historical data to predict if a site will go down on the next hour (or so) based on the events their NMS (Network Management Software) are collecting from all elements – this is a time window-based analysis that chances all the time.

Anyone on the community that have faced this (or a similar problem) would like to share any thoughts / papers / links to solution/model/approach ?

Hi, I am one of the authors of this EMNLP 2019 paper.

​

We create Universal Adversarial Triggers:

Phrases that cause a specific model prediction when concatenated to 𝘢𝘯𝘺 input.

Triggers cause:

– GPT-2 to turn racist

– SQuAD models to predict “to kill american people” for 72% of “why” questions

– Text classifier accuracy 90%->1%.

​

Paper: https://arxiv.org/abs/1908.07125

Twitter: https://twitter.com/Eric_Wallace_/status/1168907518623571974

Blog: http://ericswallace.com/triggers

In CMT

I’m a beginner in machine learning and I want to classify high dimensional sequences without using an LSTM or other deep learning methods, but using something simpler, to learn more about feature extraction.

I’m trying gesture recognition using OpenPose, with a couple of gestures (wave, swipe left, swipe right, circle) of varying length. Because of OpenPose, I have 67 features (body and hands) times 3 (x, y, c). The sequences go from 4 to 43 frames, so there is a lot of variation there.

First of all, how do I google this? Time series classification is all about long, usually one dimensional sequences, while these are shorter and highly multi dimensional sequences. I don’t know if this has been done before without deep learning?

Second of all, here is what I tried:

• Padding and clipping the sequences to the same length, then using an RBF SVC from sklearn.
• Extracting mean, median, std, max, min, and other statistics to obtain a fixed representation
• Performing Fourier transformations per keypoint/feature pair, so 67*3 transforms

All three of these overfit a lot (100 to 50 train/test accuracy), the Fourier one the most because of the high amount of features. PCA does not help a lot. Are Fourier transformations even useful here because there is no real periodicity in the gestures and the transformations look weird (normally you have these peaks in frequencies, but for me the Fourier transform just looks like a choppy sine wave for most cases)

I looked at DTW but I have way too much features???

How do I classify OpenPose sequences using machine learning and not deep learning? I can’t find a lot of info on google