Model Zoo Examples

Flux’s model zoo contains examples of a wide range of deep learning models and techniques. This project would involve adding new models, showing how to recreate state-of-the-art results (e.g. AlphaGo) or interesting and unusual model architectures (e.g. transformer networks).

Some experience with implementing deep learning models would be ideal for this project, but is not essential for a student willing to pick up the skills and read ML papers.

Mentors: Mike Innes

Flux.JS demos

Flux.JS enables export of Flux models to the browser. However, just porting a numerical function to JavaScript is rarely exciting on its own; you need to build an interface to give input to the model (say, via a webcam) and see output (say, by displaying an annotated image) in order to see what the model is thinking.

This project would involve creating new demos that show interesting models running in the browser. Examples could include:

The possibilities are pretty much endless here. This project will require a pretty solid handle on web technologies, and we’d expect much of the components created to be reusable between demos.

Mentors: Mike Innes, Shashi Gowda.

Model Import and Export

Sharing models with other frameworks would enables us to both export models (say to JavaScript for the browser, or TensorFlow Lite for mobile, or NNVM for optimised training) and to take advantage of the large set of trained models in the wild in Julia code.

This involves several stages, some or all of which could be tackled over the course of a project.

Mentors: Mike Innes

Benchmarks

A benchmark suite would help us to keep Julia’s performance for ML models in shape, as well as revealing opportunities for improvement. Like the model-zoo project, this would involve contributing standard models that exercise common ML use case (images, text etc) and profiles them. The project could extend to include improving performance where possible, or creating a “benchmarking CI” like Julia’s own nanosoldier.

Mentors: Mike Innes

Compiler Optimisations

Julia opens up many interesting opportunities for applying new optimisations to ML models, and exploring language design for ML. As part of this project you’d help us apply novel optimisation strategies to Julia code, with immediate benefits to Flux and other Julia users.

Possible projects could include:

Mentors: Mike Innes

Sparse GPU and ML support

While Julia supports dense GPU arrays well via CuArrays, we lack up-to-date wrappers for sparse operations. This project would involve wrapping CUDA’s sparse support, with CUSPARSE.jl as a starting point, adding them to CuArrays.jl, and perhaps demonstrating their use via a sparse machine learning model.

Mentors: Mike Innes

Making Aquiring Open-Data Easy

Goverments and Universities are releasing huge amounts of data under Open Data policies. Web portals such as:

Expose great quanities of data just wating to be used.

DataDeps.jl is a package that helps data scientists ensures that anyone running their code has all the data it needs, no matter when or where it is run. To do this it needs a registration block, which is a chunk of julia code which says where the data can be download, who created it, what terms and conditions are on its use etc. For a simple dataset that is all in one file writing this is pretty easy – copy and paste the info from the website hosting the data. When you want to dozens of datasets, some of which have dozens of files (and no easy way to download a .zip of all of them), writing this registration block is a bit more work.

DataDepsGenerators.jl exists to solve that. Give it a URL (or other identifier) for a page describing a dataset, and outputs all the code for a registration block, that you can copy and paste straight into your julia project. Right now DataDepsGenerators only supports a couple of sites: GitHub (for https://github.com/BuzzFeedNews/ and https://github.com/fivethirtyeight/data/ and others) and the UCI ML Repository. This project aims to change that by adding support for the CKAN and the OA-PMH APIs.

The CKAN and the OA-PMH APIs allow the automated extraction of metadata for a dataset. They are primarily used by goverment “data.gov.*” sites and research repositories respectively. Together they host millions of datasets, furfilling those institutions open data policies.

This project is to leverage those APIs, to allow others to leaverage those data repositories to produce easily repeatable, data driven research.

Expected Results: a series of patches to DataDepsGenerators.jl, giving it the capacity to generate a DataDeps registration block for any dataset hosted on site exposing a CRAN, or OAI-PMH API.

Recommended Skills: Familarity with web APIs and related technolgies (e.g. REST, JSON, XML (Probably not OAUTH, but if you’ve done OAUTH then your more than familar enough)). Some practice with webscraping is likely to be useful. A love of data and of doing cool things with it, is a big plus.

Mentors: Lyndon White (oxinabox)

Parameter estimation for nonlinear dynamical models

Machine learning has become a popular tool for understanding data, but scientists typically understand the world through the lens of physical laws and their resulting dynamical models. These models are generally differential equations given by physical first principles, where the constants in the equations such as chemical reaction rates and planetary masses determine the overall dynamics. The inverse problem to simulation, known as parameter estimation, is the process of utilizing data to determine these model parameters.

The purpose of this project is to utilize the growing array of statistical, optimization, and machine learning tools in the Julia ecosystem to build library functions that make it easy for scientists to perform this parameter estimation with the most high-powered and robust methodologies. Possible projects include investigating methods for Bayesian estimation of parameters via Stan.jl and Julia-based libraries like Turing.jl, or global optimization-based approaches. Novel techniques like classifying model outcomes via support vector machines and deep neural networks is can also be considered. Research and benchmarking to attempt to find the most robust methods will take place in this project.

Some work in this area can be found in DiffEqParamEstim.jl and DiffEqBayes.jl. Examples can be found in the DifferentialEquations.jl documentation.

Recommended Skills: Background knowledge of standard machine learning, statistical, or optimization techniques. It’s recommended but not required that one has basic knowledge of differential equations and DifferentialEquations.jl. Using the differential equation solver to get outputs from parameters can be learned on the job, but you should already be familiar (but not necessarily an expert) with the estimation techniques you are looking to employ.

Expected Results: Library functions for performing parameter estimation and inferring properties of differential equation solutions from parameters. Notebooks containing benchmarks determining the effectiveness of various methods and classifying when specific approaches are appropriate will be developed simultaneously.

Mentors: Chris Rackauckas

Artificial Intelligence Library Package based on Artificial Intelligence - A Modern Approach (AIMA)

AIMA is a seminal text on representation of agents to solve AI problems. Most packages available today as AI libraries tend to focus on ML only and not the architectural aspect of AI. The scope of the project is to create a library with a clean API (following AIMA) to easily allow the application of core algorithms to AI problems. The student will implement a package that brings together implementations of algorithms like depth-first search and simulated annealing, both from other Julia packages and from sample code in the AIMA book, and build sample programs to demonstrate AI applications. Starter code can be found at [AIMACore] (https://github.com/sambitdash/AIMACore.jl) along with [AIMASamples] (https://github.com/sambitdash/AIMASamples.jl).

Recommended Skills: Previous experience with AI or the ability to quickly pick up on the AI algorithms in AIMA

Expected Results: A well-documented library of functions derived from the AIMA book.

Mentors Sambit Kumar Dash.

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