Our team reviewed the papers accepted to NeurIPS 2020 and shortlisted the most interesting ones across different research areas. Here are the topics we cover:Natural Language Processing & Conversational AIComputer VisionReinforcement Learning & MoreTackling COVID-19 with AI & Machine LearningIf you’re interested in the remarkable keynote presentations, … [Read more...] about NeurIPS 2020: Key Research Papers in Reinforcement Learning and More
Technical Guide
How to Improve Your Supply Chain With Deep Reinforcement Learning
What has set Amazon apart from the competition in online retail? Their supply chain. In fact, this has long been one of the greatest strengths of one of their chief competitors, Walmart.Supply chains are highly complex systems consisting of hundreds if not thousands of manufacturers and logistics carriers around the world who combine resources to create the products we use … [Read more...] about How to Improve Your Supply Chain With Deep Reinforcement Learning
Graph Convolutional Networks (GCN)
In this post, we’re gonna take a close look at one of the well-known graph neural networks named Graph Convolutional Network (GCN). First, we’ll get the intuition to see how it works, then we’ll go deeper into the maths behind it.Why Graphs?Many problems are graphs in true nature. In our world, we see many data are graphs, such as molecules, social … [Read more...] about Graph Convolutional Networks (GCN)
Natural Language Processing in Production: 27 Fast Text Pre-Processing Methods
Estimates state that 70%–85% of the world’s data is text (unstructured data) [1]. New deep learning language models (transformers) have caused explosive growth in industry applications [5,6,11].This blog is not an article introducing you to Natural Language Processing. Instead, it assumes you are familiar with noise reduction and normalization of text. It covers … [Read more...] about Natural Language Processing in Production: 27 Fast Text Pre-Processing Methods
The Relationship Between Perplexity And Entropy In NLP
Perplexity is a common metric to use when evaluating language models. For example, scikit-learn’s implementation of Latent Dirichlet Allocation (a topic-modeling algorithm) includes perplexity as a built-in metric.In this post, I will define perplexity and then discuss entropy, the relation between the two, and how it arises naturally in natural language … [Read more...] about The Relationship Between Perplexity And Entropy In NLP
ECCV 2020: Some Highlights
The 2020 European Conference on Computer Vision took place online, from 23 to 28 August, and consisted of 1360 papers, divided into 104 orals, 160 spotlights and the rest of 1096 papers as posters. In addition to 45 workshops and 16 tutorials. As it is the case in recent years with ML and CV conferences, the huge number of papers can be overwhelming at times. Similar to … [Read more...] about ECCV 2020: Some Highlights
Generating Synthetic Sequential Data Using GANs
Sequential data — data that has time dependency — is very common in business, ranging from credit card transactions to medical healthcare records to stock market prices. But privacy regulations limit and dramatically slow-down access to useful data, essential to research and development. This creates a demand for highly representative, yet fully private, synthetic sequential … [Read more...] about Generating Synthetic Sequential Data Using GANs
Programming Fairness in Algorithms
Being good is easy, what is difficult is being just. ― Victor HugoWe need to defend the interests of those whom we’ve never met and never will. ― Jeffrey D. SachsNote: This article is intended for a general audience to try and elucidate the complicated nature of unfairness in machine learning algorithms. As such, I have tried to explain concepts in an accessible way … [Read more...] about Programming Fairness in Algorithms
Guide to Interpretable Machine Learning
If you can’t explain it simply, you don’t understand it well enough. — Albert EinsteinDisclaimer: This article draws and expands upon material from (1) Christoph Molnar’s excellent book on Interpretable Machine Learning which I definitely recommend to the curious reader, (2) a deep learning visualization workshop from Harvard ComputeFest 2020, as … [Read more...] about Guide to Interpretable Machine Learning
An AI Researcher’s Exploration of 200 Machine Learning Tools
To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find. The resources I used include:Full stack deep learningLF AI Foundation landscapeAI Data LandscapeVarious lists of top AI startups by the mediaResponses to my tweet and LinkedIn postPeople (friends, strangers, VCs) share … [Read more...] about An AI Researcher’s Exploration of 200 Machine Learning Tools