6 citations · 6 across the 7 of their papers we have counts for
9 papers
Library Liberation: Competitive Performance Matmul Through Compiler-composed Nanokernels
Arun Thangamani, Md Asghar Ahmad Shahid, Adam Siemieniuk +3
The rapidly evolving landscape of AI and machine learning workloads has widened the gap between high-level domain operations and efficient hardware utilization. Achieving near-peak…
Learning big logical rules by joining small rules
Céline Hocquette, Andreas Niskanen, Rolf Morel +2
A major challenge in inductive logic programming is learning big rules. To address this challenge, we introduce an approach where we join small rules to learn big rules. We impleme…
Parallel Constraint-Driven Inductive Logic Programming
Andrew Cropper, Oghenejokpeme Orhobor, Cristian Dinu +1
Multi-core machines are ubiquitous. However, most inductive logic programming (ILP) approaches use only a single core, which severely limits their scalability. To address this limi…
Predicate Invention by Learning From Failures
Andrew Cropper, Rolf Morel
Discovering novel high-level concepts is one of the most important steps needed for human-level AI. In inductive logic programming (ILP), discovering novel high-level concepts is k…
Refinement Type Directed Search for Meta-Interpretive-Learning of Higher-Order Logic Programs
Rolf Morel
The program synthesis problem within the Inductive Logic Programming (ILP) community has typically been seen as untyped. We consider the benefits of user provided types on backgrou…
Learning logic programs by explaining their failures
Rolf Morel, Andrew Cropper
Scientists form hypotheses and experimentally test them. If a hypothesis fails (is refuted), scientists try to explain the failure to eliminate other hypotheses. The more precise t…