Saturday, December 8th 2018

Vote for panel discussion topics: https://presemo.helsinki.fi/mloss2018/

8:25 – 8:30 AM Opening remarks Workshop organizers
8:30 – 9:00 AM Invited talk Gina Helfrich, NumFOCUS
9:00 – 9:30 AM Invited talk Christoph Hertzberg, Eigen3
9:30 – 10:00 AM Invited talk Joaquin Vanschoren, OpenML
10:00 – 10:05 AM Poster spotlight Sherpa: Hyperparameter Optimization for Machine Learning Models
10:05 – 10:10 AM Poster spotlight How to iNNvestigate neural network’s predictions!
10:10 – 10:15 AM Poster spotlight mlpack open-source machine learning library and community
10:15 – 10:20 AM Poster spotlight Stochastic optimization library: SGDLibrary
10:20 – 10:25 AM Poster spotlight Baseline: Strong, Extensible, Reproducible, Deep Learning Baselines for NLP
10:25 AM – 11:20 AM Poster McTorch, a manifold optimization library for deep learning
10:25 AM – 11:20 AM Poster Tensorflex: Tensorflow bindings for the Elixir programming language
10:25 AM – 11:20 AM Poster Open Source Machine Learning Software Development in CERN(High-Energy Physics): lessons and exchange of experience
10:25 AM – 11:20 AM Poster Accelerating Machine Learning Research with MI-Prometheus
10:25 AM – 11:20 AM Poster Gravity: A Mathematical Modeling Language for Optimization and Machine Learning
10:25 AM – 11:20 AM Poster skpro: A domain-agnostic modelling framework for probabilistic supervised learning
10:25 AM – 11:20 AM Poster xpandas - python data containers for structured types and structured machine learning tasks
10:25 AM – 11:20 AM Poster Machine Learning at Microsoft with ML.NET
10:25 AM – 11:20 AM Poster Open Fabric for Deep Learning Models
10:25 AM – 11:20 AM Poster Towards Reproducible and Reusable Deep Learning Systems Research Artifacts
10:25 AM – 11:20 AM Poster PyLissom: A tool for modeling computational maps of the visual cortex in PyTorch
10:25 AM – 11:20 AM Poster Salad: A Toolbox for Semi-supervised Adaptive Learning Across Domains
10:25 AM – 11:20 AM Poster Why every GBM speed benchmark is wrong
11:20 AM – 11:40 PM Contributed talk Building, growing and sustaining ML communities
11:40 AM – 12:00 PM Contributed talk PyMC’s Big Adventure: Lessons Learned from the Development of Open-source Software for Probabilistic Programming
12:00 PM – 2:00 PM Lunch (on your own)  
2:00 – 2:30 PM Invited talk James Hensman, GPFlow
2:30 – 3:00 PM Invited talk Mara Averick, tidyverse
3:00 – 3:30 PM Afternoon coffee break  
3:30 – 3:50 PM Demo DeepPavlov: An Open Source Library for Conversational AI
3:50 – 4:10 PM Demo MXFusion: A Modular Deep Probabilistic Programming Library
4:10 – 4:30 PM Demo Flow: Open Source Reinforcement Learning for Traffic Control
4:30 – 4:50 PM Demo Reproducing Machine Learning Research on Binder
4:50 – 5:30 PM Panel discussion  
5:30 – 5:35 PM Closing remarks Workshop organizers

This schedule can also be found at NIPS website.