35 citations · 56 across the 11 of their papers we have counts for
12 papers
Newclid: A User-Friendly Replacement for AlphaGeometry
Vladmir Sicca, Tianxiang Xia, Mathïs Fédérico +3
We introduce a new symbolic solver for geometry, called Newclid, which is based on AlphaGeometry. Newclid contains a symbolic solver called DDARN (derived from DDAR-Newclid), which…
VIL: Learning to Leverage Auxiliary Tasks for Multitask Learning
Rafael Kourdis, Gabriel Gordon-Hall, Philip John Gorinski
Multitask Learning is a Machine Learning paradigm that aims to train a range of (usually related) tasks with the help of a shared model. While the goal is often to improve the join…
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis
Philip John Gorinski, Matthieu Zimmer, Gerasimos Lampouras +2
The advent of large pre-trained language models in the domain of Code Synthesis has shown remarkable performance on various benchmarks, treating the problem of Code Generation in a…
Correct and Optimal: the Regular Expression Inference Challenge
Mojtaba Valizadeh, Philip John Gorinski, Ignacio Iacobacci +1
We propose regular expression inference (REI) as a challenge for code/language modelling, and the wider machine learning community. REI is a supervised machine learning (ML) and pr…
Graph Attention with Hierarchies for Multi-hop Question Answering
Yunjie He, Philip John Gorinski, Ieva Staliunaite +1
Multi-hop QA (Question Answering) is the task of finding the answer to a question across multiple documents. In recent years, a number of Deep Learning-based approaches have been p…
Relational Graph Convolutional Neural Networks for Multihop Reasoning: A Comparative Study
Ieva Staliūnaitė, Philip John Gorinski, Ignacio Iacobacci
Multihop Question Answering is a complex Natural Language Processing task that requires multiple steps of reasoning to find the correct answer to a given question. Previous researc…