225 citations · 631 across the 14 of their papers we have counts for
6 papers · 1 filter
Personalized Benchmarking with the Ludwig Benchmarking Toolkit
Avanika Narayan, Piero Molino, Karan Goel +2
The rapid proliferation of machine learning models across domains and deployment settings has given rise to various communities (e.g. industry practitioners) which seek to benchmar…
Declarative Machine Learning Systems
Piero Molino, Christopher Ré
In the last years machine learning (ML) has moved from a academic endeavor to a pervasive technology adopted in almost every aspect of computing. ML-powered products are now embedd…
Meta-Graph: Few Shot Link Prediction via Meta Learning
Avishek Joey Bose, Ankit Jain, Piero Molino +1
We consider the task of few shot link prediction on graphs. The goal is to learn from a distribution over graphs so that a model is able to quickly infer missing edges in a new gra…
Ludwig: a type-based declarative deep learning toolbox
Piero Molino, Yaroslav Dudin, Sai Sumanth Miryala
In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writi…
Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models
Jiawei Zhang, Yang Wang, Piero Molino +2
Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that uti…
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks
Piero Molino, Huaixiu Zheng, Yi-Chia Wang
For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues. This paper proposes COTA, a system to improve speed…