Machine Learning

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

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.

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.

Researchers devised a novel method to accelerate neural network training by guiding the optimization process toward a Simplex Equiangular Tight Frame, exploiting the Neural Collapse phenomenon to enha…

GTG, a novel conditional generative modeling approach, leverages diffusion models to generate high-scoring design trajectories for offline model-based optimization, outperforming existing methods on b…

GUIDE: Real-time human-shaped AI agents achieve up to 30% higher success rates using continuous human feedback, boosted by a parallel training model that mimics human input for continued improvement.

Generative Trajectory Augmentation (GTA) significantly boosts offline reinforcement learning by generating high-reward trajectories using a conditional diffusion model, enhancing algorithm performance…

Group-and-Shuffle (GS) matrices enable efficient structured orthogonal parametrization, improving parameter and computational efficiency in orthogonal fine-tuning for deep learning.

Generative world models enhance multi-agent decision-making by simulating trial-and-error learning, improving answer accuracy and explainability.

Deep neural networks surprisingly exhibit universal convergence in input salience, aligning more closely as model capacity increases, revealing valuable insights into model behavior and improving deep…

GraphMETRO tackles complex graph distribution shifts by using a Mixture-of-Experts model to decompose shifts into interpretable components, achieving state-of-the-art results.

GraphCroc, a novel graph autoencoder, leverages cross-correlation to accurately reconstruct complex graph structures, outperforming self-correlation-based methods.

Boost GNN performance and overcome oversmoothing with Cluster-Normalize-Activate (CNA) modules: a simple yet highly effective plug-and-play solution!

Deep graph neural networks often suffer from oversmoothing; this paper reveals a non-oversmoothing phase controllable by weight variance, enabling deep, expressive models.

GNeuralFlow unveils systemic interactions in irregularly sampled time series by learning a directed acyclic graph representing conditional dependencies, achieving superior performance in classificatio…

GRAPHEDX, a novel neural network, accurately estimates graph edit distance with varying operation costs, outperforming existing methods.

GDPO: A novel method optimizes graph diffusion models for any objective using reinforcement learning, achieving state-of-the-art performance in diverse graph generation tasks.