10 citations · 14 across the 13 of their papers we have counts for
13 papers
Visual Tripwires: Anticipating Failure in Deep Vision Systems
Anoushka Harit, Rehan Zuberi, William Prew +1
Deep vision systems remain vulnerable to corruption, occlusion, and distribution shift despite strong benchmark performance. Existing reliability methods typically evaluate uncerta…
From News to Returns: A Granger-Causal Hypergraph Transformer on the Sphere
Anoushka Harit, Zhongtian Sun, Jongmin Yu
We propose the Causal Sphere Hypergraph Transformer (CSHT), a novel architecture for interpretable financial time-series forecasting that unifies \emph{Granger-causal hypergraph st…
Quantifying Semantic Shift in Financial NLP: Robust Metrics for Market Prediction Stability
Zhongtian Sun, Chenghao Xiao, Anoushka Harit +1
Financial news is essential for accurate market prediction, but evolving narratives across macroeconomic regimes introduce semantic and causal drift that weaken model reliability.…
EWC-Guided Diffusion Replay for Exemplar-Free Continual Learning in Medical Imaging
Anoushka Harit, William Prew, Zhongtian Sun +1
Medical imaging foundation models must adapt over time, yet full retraining is often blocked by privacy constraints and cost. We present a continual learning framework that avoids…
RicciFlowRec: A Geometric Root Cause Recommender Using Ricci Curvature on Financial Graphs
Zhongtian Sun, Anoushka Harit
We propose RicciFlowRec, a geometric recommendation framework that performs root cause attribution via Ricci curvature and flow on dynamic financial graphs. By modelling evolving i…
ManifoldMind: Dynamic Hyperbolic Reasoning for Trustworthy Recommendations
Anoushka Harit, Zhongtian Sun, Suncica Hadzidedic
We introduce ManifoldMind, a probabilistic geometric recommender system for exploratory reasoning over semantic hierarchies in hyperbolic space. Unlike prior methods with fixed cur…
GLANCE: Graph Logic Attention Network with Cluster Enhancement for Heterophilous Graph Representation Learning
Zhongtian Sun, Anoushka Harit, Alexandra Cristea +2
Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data but often struggle on heterophilous graphs, where connected nodes differ i…
Causal Spherical Hypergraph Networks for Modelling Social Uncertainty
Anoushka Harit, Zhongtian Sun
Human social behaviour is governed by complex interactions shaped by uncertainty, causality, and group dynamics. We propose Causal Spherical Hypergraph Networks (Causal-SphHN), a p…
Actionable Interpretability via Causal Hypergraphs: Unravelling Batch Size Effects in Deep Learning
Zhongtian Sun, Anoushka Harit, Pietro Lio
While the impact of batch size on generalisation is well studied in vision tasks, its causal mechanisms remain underexplored in graph and text domains. We introduce a hypergraph-ba…
Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs
Anoushka Harit, Zhongtian Sun, Jongmin Yu +1
In the fast-paced and volatile financial markets, accurately predicting stock movements based on financial news is critical for investors and analysts. Traditional models often str…
Language as a Latent Sequence: deep latent variable models for semi-supervised paraphrase generation
Jialin Yu, Alexandra I. Cristea, Anoushka Harit +4
This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequ…
INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations
Jialin Yu, Alexandra I. Cristea, Anoushka Harit +4
XAI with natural language processing aims to produce human-readable explanations as evidence for AI decision-making, which addresses explainability and transparency. However, from…
Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums
Jialin Yu, Laila Alrajhi, Anoushka Harit +3
Massive Open Online Courses (MOOCs) have become a popular choice for e-learning thanks to their great flexibility. However, due to large numbers of learners and their diverse backg…