network depth

On the Expressiveness of Rational ReLU Neural Networks With Bounded Depth ICLR 2025 On the Hardness of Training Deep Neural Networks Discretely AAAI 2025 Till the Layers Collapse: Compressing a Deep Neural Network Through the Lenses of Batch Normalization Layers. AAAI 2025 Understanding Sharpness Dynamics in NN Training with a Minimalist Example: The Effects of Dataset Difficulty, Depth, Stochasticity, and More ICML 2025 Bayesian Adaptation of Network Depth and Width for Continual Learning ICML 2024 Initial Guessing Bias: How Untrained Networks Favor Some Classes ICML 2024 Revisiting Neural Networks for Continual Learning: An Architectural Perspective IJCAI 2024 Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions ICLR 2023 Quasi-Equivalence between Width and Depth of Neural Networks JMLR 2023 Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers ICLR 2022 DF-ResNet: Boosting Speaker Verification Performance with Depth-First Design Interspeech 2022 Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization ICML 2022 Non-deep Networks NeurIPS 2022 Size and depth of monotone neural networks: interpolation and approximation NeurIPS 2022 The Uncanny Similarity of Recurrence and Depth ICLR 2022 Vanishing Curvature in Randomly Initialized Deep ReLU Networks AISTATS 2022 Width is Less Important than Depth in ReLU Neural Networks COLT 2022 Batch Normalization Orthogonalizes Representations in Deep Random Networks NeurIPS 2021 Provable Memorization via Deep Neural Networks using Sub-linear Parameters COLT 2021 Ringing ReLUs: Harmonic Distortion Analysis of Nonlinear Feedforward Networks ICLR 2021 Scaling Properties of Deep Residual Networks ICML 2021 Finite Depth and Width Corrections to the Neural Tangent Kernel ICLR 2020 Going Deep: Graph Convolutional Ladder-Shape Networks AAAI 2020 Limits to Depth Efficiencies of Self-Attention NeurIPS 2020 On the Number of Linear Regions of Convolutional Neural Networks ICML 2020