15 citations · 28 across the 10 of their papers we have counts for
10 papers
Project Auto-World: Towards Automated Benchmarking of Neural Relational Reasoners
Anirban Das, Joanne Boisson, Irtaza Khalid +2
Reasoning about relational structures remains a significant challenge for neural models, particularly when they must systematically apply learned knowledge to problem instances tha…
Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials
Amir Masoud Nourollah, Irtaza Khalid, Stefano Leoni +1
Machine Learning Interatomic Potentials play a fundamental role in computational chemistry and materials science, enabling applications from molecular dynamics simulations to drug…
When No Paths Lead to Rome: Benchmarking Systematic Neural Relational Reasoning
Anirban Das, Irtaza Khalid, Rafael Peñaloza +1
Designing models that can learn to reason in a systematic way is an important and long-standing challenge. In recent years, a wide range of solutions have been proposed for the spe…
Large Language and Reasoning Models are Shallow Disjunctive Reasoners
Irtaza Khalid, Amir Masoud Nourollah, Steven Schockaert
Large Language Models (LLMs) have been found to struggle with systematic reasoning. Even on tasks where they appear to perform well, their performance often depends on shortcuts, r…
Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs
Zara Siddique, Irtaza Khalid, Liam D. Turner +1
We present a novel approach to bias mitigation in large language models (LLMs) by applying steering vectors to modify model activations in forward passes. We compute 8 steering vec…
Systematic Relational Reasoning With Epistemic Graph Neural Networks
Irtaza Khalid, Steven Schockaert
Developing models that can learn to reason is a notoriously challenging problem. We focus on reasoning in relational domains, where the use of Graph Neural Networks (GNNs) seems li…
Sample-efficient Model-based Reinforcement Learning for Quantum Control
Irtaza Khalid, Carrie A. Weidner, Edmond A. Jonckheere +2
We propose a model-based reinforcement learning (RL) approach for noisy time-dependent gate optimization with improved sample complexity over model-free RL. Sample complexity is th…
Analyzing and Unifying Robustness Measures for Excitation Transfer Control in Spin Networks
S. P. O'Neil, I. Khalid, A. A. Rompokos +4
Recent achievements in quantum control have resulted in advanced techniques for designing controllers for applications in quantum communication, computing, and sensing. However, th…
Statistically Characterising Robustness and Fidelity of Quantum Controls and Quantum Control Algorithms
Irtaza Khalid, Carrie A. Weidner, Edmond A. Jonckheere +2
Robustness of quantum operations or controls is important to build reliable quantum devices. The robustness-infidelity measure (RIM) is introduced to statistically quantify the…
Reinforcement Learning vs. Gradient-Based Optimisation for Robust Energy Landscape Control of Spin-1/2 Quantum Networks
I. Khalid, C. A. Weidner, E. A. Jonckheere +2
We explore the use of policy gradient methods in reinforcement learning for quantum control via energy landscape shaping of XX-Heisenberg spin chains in a model agnostic fashion. T…