Knowledge graphs (KGs) are a useful source of background knowledge to (dis)prove facts of the form (s, p, o). Finding paths between s and o is the cornerstone of several fact-checking approaches. While paths are useful to (visually) explain why a given fact is true or false, it is not completely clear how to identify paths that are most relevant to a fact, encode them and weigh their importance. The goal of this paper is to present the Fact Checking via path Embedding and Aggregation (FEA) system. FEA starts by carefully collecting the paths between s and o that are most semantically related to the domain of p. However, instead of directly working with this subset of all paths, it learns vectorized path representations, aggregates them according to different strategies, and use them to finally (dis)prove a fact. We conducted a large set of experiments on a variety of KGs and found that our hybrid solution brings some benefits in terms of performance.

I finished undergrad this past spring and just got a chance to tidy up my undergraduate thesis. It's about manifold learning, which is not discussed too often here, so I thought some people might enjoy it. It's a math thesis, but it's designed to be broadly accessible (e.g. the first few chapters could serve as an introduction to kernel learning). It might also help some of the undergrads here looking for thesis topics -- there seem to be posts about this every few weeks or so. I've very open to feedback, constructive criticism, and of course let me know if you catch any typos!

There's a lot of room for improvement here, and I'm going to keep hacking at it. But I wanted to go ahead and share some preliminary results and open up discussion for feedback. You folks here on r/MachineLearning have been incredibly helpful in providing feedback, guidance, and ideas.

The researchers created a system that combines machine learning and natural language processing (NLP) algorithims with theoretical and analytical …

Machine Learning and Its Applications Part 1. (15 Mar – 2 Apr 2021). Visitor List. Peter Bartlett Simons Institute for the Theory of Computing, UC …

… continue into post-pandemic recovery efforts and has created a huge demand for advanced analytics, predictive modeling, and machine learning.

If you haven't heard, thinking computers and the tenets of machine learning are fundamentally changing almost every industry.

I'm particularly interested in the complications and societal implications that arise when machine learning is used with imperfect real-world data.

Shalini Kantayya is the documentary filmmaker behind the recent films Catching the Sun and Coded Bias, which premiered this month online. Coded Bias follows MIT researcher Joy Buolamwini as she investigates and combats the racial disparities of facial recognition for people of color, in both impact and accuracy. As it follows Buolamwini from MIT to her testimony on Capitol Hill, the film looks at the ubiquitous, but overlooked impact of algorithms on our daily lives, from policing to housing to education and shopping. Days after the film's premiere, WIRED spoke with Kantayya about the documentary, sci-fi, and Big Tech's grasping control of our lives. WIRED: People hear phrases like machine learning, artificial intelligence, recommender systems, and it's overwhelming.

Shalini Kantayya is the documentary filmmaker behind the recent films Catching the Sun and Coded Bias, which premiered this month online. Coded Bias follows MIT researcher Joy Buolamwini as she investigates and combats the racial disparities of facial recognition for people of color, in both impact and accuracy. As it follows Buolamwini from MIT to her testimony on Capitol Hill, the film looks at the ubiquitous, but overlooked impact of algorithms on our daily lives, from policing to housing to education and shopping. Days after the film's premiere, WIRED spoke with Kantayya about the documentary, sci-fi, and Big Tech's grasping control of our lives. WIRED: People hear phrases like machine learning, artificial intelligence, recommender systems, and it's overwhelming.