Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Denoising Autoencoders for Overgeneralization in Neural Networks
Generalized Denoising Auto-Encoders as Generative Models
Generalization in Deep Learning
Exploring Generalization in Deep Learning
On the Quality of the Initial Basin in Overspecified Neural Networks
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Understanding deep learning requires rethinking generalization
Generalisation in humans and deep neural networks
A Domain Adaptation Regularization for Denoising Autoencoders
Variational Approaches for Auto-Encoding Generative Adversarial Networks
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data
Denoising Criterion for Variational Auto-Encoding Framework
A Simple Weight Decay Can Improve Generalization
Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond
Learning Deep Network Representations with Adversarially Regularized Autoencoders
Adversarially Regularized Autoencoders
Marginalized Denoising Auto-encoders for Nonlinear Representations
Adversarial Noise Layer: Regularize Neural Network by Adding Noise
WebSeg: Learning Semantic Segmentation from Web Searches
Lattice-based motion planning for a general 2-trailer system
Heuristic Algorithms for Solving the Resource-Constrained Project Scheduling Problem: Classification and Computational Analysis
LIBSVM: A library for support vector machines
Observing the unexpected enhances infants’ learning and exploration