deep-neural-networks

Documents about deep-neural-networks

• Persistence-Augmented Neural Networks#2588arXiv CS
• Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks#13084arXiv CS
• Information Plane Analysis of Binary Neural Networks#155317arXiv CS
• Criticality and Saturation in Orthogonal Neural Networks#168333arXiv CS
• Storm Surge Modeling, Bias Correction, Graph Neural Networks, Graph Convolution Networks#124056arXiv CS
• Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories#124039arXiv CS
• On Halting vs Converging in Recurrent Graph Neural Networks#141491arXiv CS
• TabSurv: Adapting Modern Tabular Neural Networks to Survival Analysis#155279arXiv CS
• Dynamic Treatment on Networks#168332arXiv CS
• Inter-Layer Hessian Analysis of Neural Networks with DAG Architectures#10345arXiv CS
• Beyond the Laplacian: Doubly Stochastic Matrices for Graph Neural Networks#19023arXiv CS
• Complexity of Linear Regions in Self-supervised Deep ReLU Networks#138947arXiv CS
• Neural Weight Norm = Kolmogorov Complexity#175251arXiv CS
• A Nonlinear Separation Principle: Applications to Neural Networks, Control and Learning#19003arXiv CS
• Fraud Detection in Cryptocurrency Markets with Spatio-Temporal Graph Neural Networks#138926arXiv CS
• Physical Foundation Models: Fixed hardware implementations of large-scale neural networks#146948arXiv CS
• Quantum Interval Bound Propagation for Certified Training of Quantum Neural Networks#151724arXiv CS
• Towards Metric-Faithful Neural Graph Matching#168326arXiv CS
• Possibilistic Predictive Uncertainty for Deep Learning#151751arXiv CS
• Introducing Echo Networks for Computational Neuroevolution#2612arXiv CS
• Extreme bandits#138933arXiv CS
• Spectral bandits#141508arXiv CS
• Attractor FCM#146944arXiv CS
• Exact Certification of Neural Networks and Partition Aggregation Ensembles against Label Poisoning#10375arXiv CS
• Randomized Neural Networks for Integro-Differential Equations with Application to Neutron Transport#14042arXiv CS
• How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations#18997arXiv CS
• Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle#120515arXiv CS
• Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs#138952arXiv CS
• ANTIC: Adaptive Neural Temporal In-situ Compressor#5944arXiv CS
• Modern Structure-Aware Simplicial Spatiotemporal Neural Network#31299arXiv CS
• Revisiting Neural Activation Coverage for Uncertainty Estimation#134572arXiv CS
• Quality-Driven Selective Mutation for Deep Learning#134539arXiv CS
• Deep Kernel Learning for Stratifying Glaucoma Trajectories#151732arXiv CS
• Physics-Informed Neural Networks for Biological $2\mathrm{D}{+}t$ Reaction-Diffusion Systems#120504arXiv CS
• Weisfeiler Lehman Test on Combinatorial Complexes: Generalized Expressive Power of Topological Neural Networks#151729arXiv CS
• Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories#155285arXiv CS
• Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory#178882arXiv CS
• Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks#120510arXiv CS
• ImageNet classification with deep convolutional neural networks#6257OpenAlex
• Conformalized Super Learner#134569arXiv CS
• Pliable rejection sampling#134571arXiv CS
• Hierarchical Behaviour Spaces#138928arXiv CS
• Cost-Aware Learning#146934arXiv CS
• Conditional Diffusion Sampling#155272arXiv CS
• Tempered Guided Diffusion#155309arXiv CS
• Recursive Agent Optimization#168308arXiv CS
• Deep learning in neural networks: An overview#6396OpenAlex
• Neural architectures for resolving references in program code#14010arXiv CS
• Relaxation-Informed Training of Neural Network Surrogate Models#134530arXiv CS
• Fixed-Point Neural Optimal Transport without Implicit Differentiation#175274arXiv CS