Neural Information Processing

Defend from Scratch: A Diffusion-based Proactive Defense Method for Unauthorized Speech Synthesis.- Transformers As Approximations of Solomonoff Induction.- Interpreting Decision Transformer: Insights from Continuous Control Tasks.- Flexible-order Feature-interaction for Mixed Continuous and Discrete Variables with Group-level Interpretability.- Critical Feature Sifting and Dynamic Aggregation for Anomalous Audio Sequence Detection.- Parallel Interpretation Network via Semantic Visual Probe and Counterfactual Verification.- Real-Time Decentralized M2M Decision-Making via Deep Learning and Incremental Learning.- Explainable Federated Stacking Models with Encrypted Gradients for Secure Kidney Medical Imaging Diagnosis.- DDFGNN: Dual-dimensionality Fusion Graph Neural Network for Social Bot Detection.- A Motif-based Graph Convolution Network for Stock Trend Prediction.- VAGNN: Advancing the Generalization of Graph Neural Networks.- TrajAngleNet: Transformer-based Trajectory Prediction through Multi-Task Learning with Angle Prediction.- Correlation Disentangling and Spatio-Temporal Cooperative Optimizing Network for Temperature Prediction Revision.- Hierarchical Adaptive Position Encoding-based Transformer for Point Cloud Analysis.- In-context Learning for Temperature Field Reconstruction under Multiple Layouts.- Loosely coupled oscillators as a correlate of behavioral control circuits within the central complex of the fruit fly.- EL-LSTM: A Multivariate Time Series Forecasting Model Combining Spiking Neurons and Long Short-Term Memory Networks.- A Two-Stage Network for Enhanced Intracranial Artery 3D Segmentation in TOF-MRA Volume.- Independence Constrained Disentangled Representation Learning from pistemological Perspective.- Utilizing Small and Large Spectral Radii for Appropriate Reservoir Computing Design.- Noisy Deep Ensemble: Accelerating Deep Ensemble Learning via Noise Injection.- LCNet: Lightning Hierarchical Convolution for Occupancy Flow Prediction.- FedTS: Leveraging Teacher-Student Architecture in Federated Learning against Model Heterogeneity in Edge Computing Scenarios.- Physics-informed antisymmetric recurrent neural networks for solving nonlinear partial differential equations.- APS: An Adaptive Policy Switching Framework to Improve the Generalization of Branching.- Efficient Pruning and Compression Techniques for Convolutional Neural Networks to Preserve Knowledge and Optimize Performance.- Enhancing Convnets with Pruning and Symmetry-Based Filter Augmentation.- Improved Approximation Algorithms for the Cumulative Vehicle Routing Problem.