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Theory

1072 directly classified papers

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Papers

Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex AISTATS 2019
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent AISTATS 2019
On the Connection Between Learning Two-Layer Neural Networks and Tensor Decomposition AISTATS 2019
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks AISTATS 2019
Deep Neural Networks Learn Non-Smooth Functions Effectively AISTATS 2019
Towards a Theoretical Understanding of Hashing-Based Neural Nets AISTATS 2019
Transformer Dissection: An Unified Understanding for Transformer’s Attention via the Lens of Kernel IJCNLP 2019
Learnability for the Information Bottleneck UAI 2019
Efficient Neural Network Verification with Exactness Characterization UAI 2019
ResNet and Batch-normalization Improve Data Separability ACML 2019
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds NIPS 2019
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks NIPS 2019
Deep Scale-spaces: Equivariance Over Scale NIPS 2019
Conditional Independence Testing using Generative Adversarial Networks NIPS 2019
On Exact Computation with an Infinitely Wide Neural Net NIPS 2019
Initialization of ReLUs for Dynamical Isometry NIPS 2019
Semi-flat minima and saddle points by embedding neural networks to overparameterization NIPS 2019
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks NIPS 2019
Deep ReLU Networks Have Surprisingly Few Activation Patterns NIPS 2019
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks NIPS 2019
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks NIPS 2019
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel NIPS 2019
Approximate Feature Collisions in Neural Nets NIPS 2019
A Solvable High-Dimensional Model of GAN NIPS 2019
Are deep ResNets provably better than linear predictors? NIPS 2019