1 citations · 2 across the 4 of their papers we have counts for
5 papers
SkillSmith: Learning to Compose Parametric Skills and Textual Knowledge
Lucio M. Dery, Benedict Aaron Tjandra, Siavash Samiei +4
Agentic systems driven by large language models (LLMs) regularly feature two key mechanisms to autonomously solve complex problems: synthesizing text-based knowledge and procedures…
T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs
Alireza Dizaji, Benedict Aaron Tjandra, Mehrab Hamidi +2
Dynamic graph learning methods have recently emerged as powerful tools for modelling relational data evolving through time. However, despite extensive benchmarking efforts, it rema…
Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification
Benedict Aaron Tjandra, Federico Barbero, Michael Bronstein
Despite the successful application of Temporal Graph Networks (TGNs) for tasks such as dynamic node classification and link prediction, they still perform poorly on the task of dyn…
Fine-Tuning Large Language Models to Appropriately Abstain with Semantic Entropy
Benedict Aaron Tjandra, Muhammed Razzak, Jannik Kossen +2
Large Language Models (LLMs) are known to hallucinate, whereby they generate plausible but inaccurate text. This phenomenon poses significant risks in critical applications, such a…
On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction
Edward Elson Kosasih, Joaquin Cabezas, Xavier Sumba +5
In order to advance large-scale graph machine learning, the Open Graph Benchmark Large Scale Challenge (OGB-LSC) was proposed at the KDD Cup 2021. The PCQM4M-LSC dataset defines a…