Description
Book Synopsis.- MRT-NAS: Boosting Training-free NAS via Manifold Regularization.
.- MSfusion: A Dynamic Model Splitting Approach for Resource Constrained Machines to Collaboratively Train Larger Models.
.- DeepCTL: Neural Branching-Time CTL Satisfiability Checking via Recursive Decision Trees.
.- MFMamba: A Hierarchical Weakly Causal Mamba with Multi-Scale Feature Fusion for Vision Tasks.
.- Characterizing trainability, expressivity and generalization of neural architecture with metrics from neural tangent kernel.
.- Unrolled Neural Adaptive Alternating Gradient Descent for NMF.
.- FedTP: Traceable Passport-based Ownership Verification for Federated Deep Neural Network Models.
.- Learning to Optimize Entropy in the Soft Actor-Critic.
.- Parallelizing Sharpness-Aware Minimization: A Semi-Asynchronous Small-Batch Approach.
.- Small transformer architectures for task switching.
.- Stochastic Covariance Regularization for Imbalanced Datasets.
.- Efficient Learning in Spiking Neural Networks - Introducing Feedback Alignment to the Reinforced Liquid State Machine.
.- Object-Centric Dreamer.
.- How Inductive Biases Affect OOD Generalization: An Investigation in Formal Language Recognition with Autoregressive Models.
.- Brain Generative Replay for Continual Learning.
.- Dynamic Ensembles Towards Out-Of-Distribution Generalization of Affect Models.
.- D2R: Dual Regularization Loss with Collaborative Adversarial Generation for Model Robustness.
.- The Power of Max Pooling Layer.
.- Firing rates and representational error in efficient spiking networks are bounded by design.
.- CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization.
.- Cascade Pre-Attention: Regulating Neuronal Activation Distributions in MetaFormer-Based Spiking Neural Networks.
.- MTL-SIMNAS: Task Similarity-Driven Neural Architecture Search for Enhanced Multi-Task Learning.
.- Towards Better Graph Anomaly Detection: A Performance-Aware Neural Architecture Search Approach.
.- Improving Stability of Parameter Sharing in Cooperative Multi-Agent Reinforcement Learning.
.- The Explainability-Performance Coefficient: A New Metric for Model Transparency.
.- GLFMamba-U: Global-Local Fused Mamba-Unet.
.- Continuous Fair SMOTE - Fairness-Aware Stream Learning from Imbalanced Data.
.- Evaluating the Impact of Data Curation on Off-Policy Reinforcement Learning.
.- Enhancing Graph Neural Networks with Mixup-Based Knowledge Distillation.
.- A Framework for Uncertainty Quantification Based on Nearest Neighbors Across Layers.
.- FedP2PAvg: A Peer-to-Peer Collaborative Framework for Federated Learning in Non-IID Scenarios.
.- Correcting the Modified Stochastic Synaptic Model of Synaptic Dynamics - Refinement of Vesicle and Neurotransmitters Functions.
.- Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient.
.- Efficient ReliefF: A low-power optimization of ReliefF for resource-constrained devices.
.- Enhancing Adversarial Robustness through Multi-Objective Representation Learning.
.- Trustworthy Learning with Noisy Labels.
.- Effect of Neuromodulation on the Brain Dynamical Repertoire.
.- Classification of large data sets by neural networks: A probabilistic viewpoint.
.- Identification and Realization of a Class of Discrete Event Systems by Neural Networks -Timed Petri Nets.
.- Dopamine-modulated Learning and Decision-making with Neuromorphic Computing.
.- A Unified Platform to Evaluate STDP Learning Rule and Synapse Model using Pattern Recognition in a Spiking Neural Network.
.- XOOD: A Self-Supervised Algorithm for Detecting Out-of-Distribution Data for Image Classification.
.- Perpetual Generation: Online Learning of Linear State-Space Models from a Single Stream.
.- Accelerating Spatiotemporal Learning with minConvRNNs.
.- Full Integer Arithmetic Online Training for Spiking Neural Networks.
.- Regularised Loss Function for Goal Recognition as a Deep Learning Task.
.- Improving Consistency Distillation with Rectified Trajectories.
.- Merging versus Separating Replay Samples in Continual Learning.
.- Signal-to-noise difference as a correlate of class learning in neural networks.
.- Catastrophic Forgetting Mitigation via Discrepancy-Weighted Experience Replay.
.- Supervised feature selection with class self-representation.
.- Complexity and Criticality in Neuro-Inspired Reservoirs.
.- A Fokker-Planck Perspective on the Flow of Information in Continuous Memory Neural Networks.
