Real NVP uses affine coupling layers to create invertible transformations that support exact density estimation, sampling, and latent inference without approximations.
Resnet in Resnet: Generalizing Residual Architectures
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
Residual networks (ResNets) have recently achieved state-of-the-art on challenging computer vision tasks. We introduce Resnet in Resnet (RiR): a deep dual-stream architecture that generalizes ResNets and standard CNNs and is easily implemented with no computational overhead. RiR consistently improves performance over ResNets, outperforms architectures with similar amounts of augmentation on CIFAR-10, and establishes a new state-of-the-art on CIFAR-100.
citation-role summary
background 1
baseline 1
citation-polarity summary
representative citing papers
A vector-quantized autoencoder learns minimal control codebooks for forward invariance in sampled-data control, achieving 157x reduction over grid baselines on a 12D quadrotor model.
Models arbitrary AI models as DAGs and solves split-learning model partitioning via min s-t cut / max-flow equivalence, plus a low-complexity block-wise variant, with hardware experiments showing up to 13x faster decisions and 39% lower delay.
An LLM-guided framework simulates physiological trajectories to provide interpretable early warnings for sepsis, achieving AUC scores of 0.861-0.903 on MIMIC-IV and eICU data.
A Transformer-based generative model builds an environment-aware channel knowledge base that is injected into JSCC encoders and decoders, achieving 10^{-3} level channel estimation error and outperforming benchmarks in semantic communication performance.
citing papers explorer
-
Density estimation using Real NVP
Real NVP uses affine coupling layers to create invertible transformations that support exact density estimation, sampling, and latent inference without approximations.
-
Minimal Information Control Invariance via Vector Quantization
A vector-quantized autoencoder learns minimal control codebooks for forward invariance in sampled-data control, achieving 157x reduction over grid baselines on a 12D quadrotor model.
-
Fast AI Model Partition for Split Learning over Edge Networks
Models arbitrary AI models as DAGs and solves split-learning model partitioning via min s-t cut / max-flow equivalence, plus a low-complexity block-wise variant, with hardware experiments showing up to 13x faster decisions and 39% lower delay.
-
Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics
An LLM-guided framework simulates physiological trajectories to provide interpretable early warnings for sepsis, achieving AUC scores of 0.861-0.903 on MIMIC-IV and eICU data.
-
Generative Channel Knowledge Base With Environmental Information for Joint Source-Channel Coding in Semantic Communications
A Transformer-based generative model builds an environment-aware channel knowledge base that is injected into JSCC encoders and decoders, achieving 10^{-3} level channel estimation error and outperforming benchmarks in semantic communication performance.