AI Applications

DeformableTST: a new Transformer model for time series forecasting that surpasses existing methods by reducing over-reliance on patching, enhancing performance and adaptability.

Deep learning (DL) image registration methods sometimes underperform classical methods, especially when data distribution shifts; this study reveals when each approach excels.

DDGS-CT: A novel direction-disentangled Gaussian splatting method creates realistic X-ray images from CT scans, boosting accuracy and speed for applications such as image-guided surgery and radiothera…

LLMs iteratively discover and refine interpretable dynamical systems models, achieving high accuracy and uncovering new insights; demonstrated by a novel Warfarin model.

AI-powered drug interaction prediction gets a boost! CSSE-DDI uses neural architecture search to customize subgraph selection and encoding, resulting in superior accuracy and efficiency compared to e…

CRT-Fusion: Boosting 3D object detection by fusing camera, radar, and motion information for more accurate, robust results!

CooHOI: A two-phase learning framework enables physically simulated characters to perform cooperative object transportation tasks naturally and efficiently, overcoming the limitations of existing meth…

LeapAD, a novel autonomous driving paradigm, uses a dual-process architecture mirroring human cognition to achieve continuous learning and improved adaptability. Employing a VLM for efficient scene u…

CITRUS: a novel continuous graph neural network efficiently processes multidomain data on multiple graphs, achieving superior performance in spatiotemporal forecasting.

Projected Diffusion Models (PDM) revolutionizes generative modeling by directly incorporating constraints into the sampling process, ensuring high-fidelity outputs that strictly adhere to predefined c…

A novel conjugate Bayesian two-step change point detection method for Hawkes processes, CoBay-CPD, achieves higher accuracy and efficiency by employing data augmentation for improved dynamic event mod…

Collaborative Computerized Adaptive Testing (CCAT) improves student ranking accuracy in online exams by leveraging inter-student information to enhance ranking consistency.

Coral: a novel collaborative cognitive diagnosis model uses disentangled representation learning to improve diagnostic accuracy by leveraging collaborative signals among learners with similar learning…

CLOVER: A closed-loop visuomotor framework using generative visual plans & feedback mechanisms achieves state-of-the-art results in long-horizon robotic manipulation tasks.

ClavaDDPM synthesizes multi-relational data using cluster-guided diffusion models, efficiently capturing long-range dependencies and outperforming existing methods.

Chimera: a novel 2D state space model effectively captures complex multivariate time series dependencies, achieving superior forecasting, classification, and anomaly detection.

CausalStock: A novel framework for accurate news-driven multi-stock movement prediction, using lag-dependent causal discovery and LLMs for enhanced noise reduction and explainability.

CaPaint: a novel causal spatio-temporal prediction framework that uses causal reasoning and diffusion inpainting to boost model accuracy and generalizability, especially in data-scarce settings.

CALANet: Cheap All-Layer Aggregation boosts real-time HAR accuracy by efficiently aggregating features from all layers, achieving state-of-the-art performance on seven benchmark datasets.

BricksRL: A low-cost, open-source platform democratizes robotics and reinforcement learning research using LEGO, enabling accessible real-world experiments.