Keeping up with the latest research is important for the data scientist so let's work on this together. Each week or two, we will choose one or more articles to read and meet up to discuss them. This group is open to data scientists of any experience level and speciality but I expect the core group will be relatively small. I hope you are as excited about data science, machine learning, and statistics research as I am, and I look forward to meeting you!
This is a variety of the Silicon Valley Data Science Journal Club and we will be closely following the pages that they read.
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Causal inference and the data-fusion problem
The proliferation of Big Data systems means that there is an increasing amount of data available to Data Scientists but relatively little of it is collected in a controlled fashion, instead it is purely observational.data data-fusion causal-inference journal-club datascience
Towards Neural Theorem Proving at Scale
Theorem provers and logic programming languages are one of the highlights of "classical" AI. They represent knowledge as a series of facts in predicate form and queries are connected to the facts by applying rules. The main issue with these rule-based systems becomes evident upon...discovery data neural-theorem journal-club ldsjc datascience
Hierarchical Neural Story Generation
In this session we’ll discuss the paper ‘Hierarchical Neural Story Generation’ which uses Transformer-based neural networks to generate writing prompts and stories, trained on posts from reddit.com/r/writingprompts.data natural-language-processing neural-network machine-learning
CoordConv: Addressing an Intriguing Failing of Convolutional Neural Networks
At this meetup, we'll be having a look at a relatively new addition to the deep-learning toolkit: CoordConv. The approach works by adding the coordinates of pixels and feature maps to the network layers.data coord-conv convolution-neural-network cnn neural-network
Machine Learning on Encrypted Data
Recent progress on homomorphic encryption allows us to leverage it for machine learning. There are two options: either applying a trained model to encrypted data or to encrypt your model and send it to the owner of the data for inference.data python data-encryption datascience encryption machine-learning
Building a Knowledge Base Question Answering Pipeline
In this session, we will discuss a semantic parsing approach to knowledge base question answering and the challenges of building a question answering pipeline.pipeline nlp natural-language-processing data
London Data Science Journal Club
Going deeper and deeper is sometimes not the only way for convolutional neural networks (CNNs) to learn better representations. As a general successor to ResNet, DenseNets brings together patterns that have worked well in various architectures and obtains state-of-the-art results across common...data data-science
Easy life in NLP: only 100 examples to train a classifier with Transfer Learning
At this month's London Data Science Journal Club, we'll be looking at two papers to discuss NLP, neural network architectures, model pre-training and fine-tuning techniques alongside with the questions of semantics: what are the models actually learning? Meaning or syntax? Don't miss...data transfer-learning nlp data-science bigdata
A Unified Approach to Interpreting Model Predictions
In many cases, understanding how models make their predictions is as important as the accuracy of their predictions. As models get more complex, various tools such as LIME and DeepLift have been developed to help users interpret the computations of their models.lime data-predictions data-models deeplift data databases datascience
Causal Effect Inference with Deep Latent-Variable Models
Causal inference has seen a resurgence of interest from the machine learning community. This interest has arisen alongside some controversial criticism of deep learning, with recent developments in the field having been likened to “alchemy”  or mere “curve fitting” .causality bigdata deep-learning machine-learning