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Introduction It has been three years since the release of CLUTRR, a benchmark we created to test the reasoning capabilities of modern neural networks. The idea is simple: can models understand first-order logic, in the backdrop of kinship relations?

In my last blog post I described a method I use to keep track of my paper reading habits, using Emacs. Using the workflow, I can now: Check the latest Arxiv papers using Elfeed Score the papers using Elfeed-Score Save the papers in a local bib file, along with pdfs, using org-ref functions Maintain a paper reading tracker document in Org Mode, where the workflow automatically adds the paper to read.

Over the last couple of years I have steadily transferred most of my workflows in Emacs (more specifically, Doom Emacs). As they truly say, Emacs is not just an editor, it is an operating system.

A recurrent challenge in machine learning research is to ensure that the presented and published results are reliable, robust, and reproducible [4,5,6,7]. Reproducibility, obtaining similar results as presented in a paper using the same code and data, is necessary to verify the reliability of research findings.

GraphLog - Suite of 57 graph worlds built using first-order logic Koustuv Sinha, Shagun Sodhani, Joelle Pineau and William L. Hamilton Code | Docs | Paper | Home Page | Teaser Talk

Compositional Language Understanding with Text based Relational Reasoning Motivation Question Answering (QA) has recently gained popularity as the major domain of testing reasoning in text. The literature thus contains a deluge of Question Answering (QA) datasets to choose from.