Dear ML community,

The paper “Tackling Climate Change with Machine Learning” was the most interesting paper i have come across since I work in the data science realm. It was created by 22 AI researchers including Andrew Ng, Yoshua Bengio, David Rolnick and others from Google, Stanford, Harvard, Deepmind, Microsoft Research etc.

Because i believe that it contains many great research works and projects which deserve more attention, i spent the last weeks and weekends to create a video summary and a blog post series, which try to give an easy to grasp overview.

Here is the video summary: https://youtu.be/pHdv4o0mfd0

And here are the parts of the blog post series:

1. Electricity Systems
2. Transportation
3. Buildings & Cities
4. Farms & Forests
5. Industry & Carbon Dioxide Removal
6. Datasets & further resources

If you want to learn more afterwards, check out the http://climatechange.ai project, which emerged from the paper, where you will find further resources, such as datasets, initiatives and talks from ICML 2019.

There will be workshops at NeurIPS 2019 (Vancouver, Canada) and AMLD 2020 (Lausanne, Switzerland) that will focus on this matter as well.

Machine Learning is not a miracle cure and cannot solve all climate change related problems. Policy makers must decide to act to drive large-scale progress. But ML is an invaluable tool which can reduce greenhouse gas emissions in many domains and sometimes even help create better policies, as the research shows.

I hope this summary will spark further ideas and maybe inspire you to do something about one of the greatest challenges we face as a planet. Let’s use the diverse talents we have to drive some progress and create a better future!

Paul

Imagine we have a training set of mp3s coupled with corresponding 3D models of rooms in which the 3D models were recorded. We train a neural net with this data. Given a new mp3, could we construct a 3D model based on sound alone?

Tensorflow has been using CUDA 10 for a while now. Since these take considerable time to compile, and not everyone has the resources to do so, I figured it wouldn’t hurt to share my latest custom builds with Python3.7, CUDA 10.1 / cuDNN 7.6 / NCCL 2.4 for Tensorflow 2.0rc2 (for which the branch finally compiles from sources without any issues).

https://github.com/davidenunes/tensorflow-wheels

The repository is mostly for wheels I’ve been using, but I also list wheels other people requested.

— this depends in my availability but feel free to make a request, if you can’t find a build that you really need.

https://www.cosmos.esa.int/web/esac-stats-workshop-2019

Discussion about his exploitation of students in his most recent course here:

https://www.reddit.com/r/MachineLearning/comments/d7ad2y/d_siraj_raval_potentially_exploiting_students/

Introducting The Github CodeSearchNet Challenge

Searching for code to reuse, call into, or to see how others handle a problem is one of the most common tasks in a software developer’s day. However, search engines for code are often frustrating and never fully understand what we want, unlike regular web search engines. We started using modern machine learning techniques to improve code search but quickly realized that we were unable to measure our progress. Unlike natural language processing with GLUE benchmarks, there is no standard dataset suitable for code search evaluation.

We collected a large dataset of functions with associated documentation written in Go, Java, JavaScript, PHP, Python, and Ruby from open source projects on GitHub. We used our TreeSitter infrastructure for this effort, and we’re also releasing our data preprocessing pipeline for others to use as a starting point in applying machine learning to code. While this data is not directly related to code search, its pairing of code with related natural language description is suitable to train models for this task. Its substantial size also makes it possible to apply high-capacity models based on modern Transformer architectures.

Our fully preprocessed CodeSearchNet Corpus is available for download on Amazon S3, including:

Six million methods overall

Two million of which have associated documentation (docstrings, JavaDoc, and more)

Metadata that indicates the original location (repository or line number, for example) where the data was found

If you have a limited amount of data, want learning to occur in real-time, want an explanation of what was learned, don’t have access to large banks of high-speed GPU machines, or you really care to model how humans learn, then in all these cases, deep learning is not the answer you want.

Does anyone have any advice on building an NLP model from a set of documents, such that you could ask “does this document discuss topic X”? I’d like synonyms and topics to be identified without supervision.

We propose Deep Conditional Target Densities (DCTD), a novel and general regression method with a clear probabilistic interpretation. DCTD models the conditional target density p(y|x) by using a neural network to directly predict the un-normalized density from the input-target pair (x, y). This model of p(y|x) is trained by minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling. Notably, our method achieves a 1.9% AP improvement over Faster-RCNN for object detection on COCO, and sets a new state-of-the-art on visual tracking when applied for bounding box regression.

arXiv: https://arxiv.org/abs/1909.12297

Project page: http://www.fregu856.com/publication/dctd/