Posters

HHD-GP leverages Helmholtz-Hodge decomposition within Gaussian Processes to learn physically meaningful components of dynamical systems, enhancing prediction accuracy and interpretability.

HGDL: Heterogeneous Graph Label Distribution Learning, a new framework that leverages graph topology and content to enhance label distribution prediction.

HiCS-FL: A novel federated learning client sampling method that leverages data heterogeneity for faster, more efficient global model training in non-IID settings.

HEPrune accelerates private deep learning training 16x by integrating encrypted data pruning, achieving this speedup with minimal accuracy loss.

HENASY, a novel egocentric video-language model, uses a compositional approach to assemble scene entities for improved interpretability and performance.

HEALNet: a novel multimodal fusion network achieving state-of-the-art performance on biomedical survival analysis by effectively integrating heterogeneous data while handling missing modalities.

HDR-GS: 1000x faster HDR novel view synthesis via Gaussian splatting!

HC-GAE: A novel hierarchical graph autoencoder combats over-smoothing by using hard node assignment to create isolated subgraphs, improving graph representation learning for classification.

HAWK: a novel framework leveraging interactive VLMs and motion modality achieves state-of-the-art performance in open-world video anomaly understanding, generating descriptions and answering questions…

Boost self-supervised visual learning: This paper introduces theoretical insights and practical recommendations to significantly improve SSL’s efficiency and reduce data needs.

Unlocking latent community structures from multiple correlated networks is now possible with greater precision, as this research pinpoints the information-theoretic threshold for exact recovery, even …

TextHarmony: a unified multimodal model harmonizes visual text comprehension & generation, achieving improved performance across benchmarks with minimal parameter increase.

DiMoP3D: Predicting diverse, physically realistic human motions in 3D scenes by harmonizing stochasticity and determinism.

HardCore: Fast generation of hard, realistic UNSAT problems for improved SAT solver runtime prediction.

Happy: a novel debiased learning framework, excels at continually discovering new categories from unlabeled data while retaining knowledge of previously learned ones, overcoming existing bias issues a…

This paper enhances learnware dock systems by using model outputs to improve heterogeneous learnware management, enabling effective task handling even without perfectly matched models.

Hamiltonian Generative Flows (HGFs) revolutionize generative modeling by leveraging Hamiltonian dynamics, offering enhanced score matching and generative capabilities.

Hamiltonian Monte Carlo struggles with ReLU neural networks: high rejection rates hinder Bayesian deep learning.

Accelerate Bayesian inference in linear mixed-effects models by efficiently marginalizing random effects using fast linear algebra, enabling faster and more accurate posterior estimations.

Hamba: a novel graph-guided framework for single-view 3D hand reconstruction, significantly outperforms existing methods by efficiently modeling spatial relationships between joints using a fraction o…