Latest Research
Few-shot Calligraphy Style Learning
We introduced "Presidifussion," a novel approach to learning and replicating the unique style of calligraphy of President Xu, using a pretrained diffusion model adapted through a two-stage training process.
Certified MaxSAT Preprocessing
For SAT, this is largely a solved problem thanks to the use of proof logging, meaning that solvers emit machine-verifiable proofs of (un)satisfiability to certify correctness.
Weakly Supervised Training for Hologram Verification in Identity Documents
We propose a method to remotely verify the authenticity of Optically Variable Devices (OVDs), often referred to as ``holograms'', in identity documents.
Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond
In response, we propose ACSleuth, a novel, reconstruction deviation-guided generative framework that integrates the detection, domain adaptation, and fine-grained annotating of anomalous cells into a methodologically cohesive workflow.
Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations
Autonomous robotic systems capable of learning novel manipulation tasks are poised to transform industries from manufacturing to service automation.
A Continuous Relaxation for Discrete Bayesian Optimization
To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization.
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
We introduce the FRactional-Order graph Neural Dynamical network (FROND), a new continuous graph neural network (GNN) framework.
FairGT: A Fairness-aware Graph Transformer
The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups.
Making Better Use of Unlabelled Data in Bayesian Active Learning
Fully supervised models are predominant in Bayesian active learning.
Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT
Aims: We conduct an empirical study to evaluate the impact of SV data scarcity in emerging languages on the state-of-the-art SV prediction model and investigate potential solutions to enhance the performance.
