network width

Bayesian Treatment of the Spectrum of the Empirical Kernel in (Sub)Linear-Width Neural Networks ICLR 2025 Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective ICML 2025 Optimization for Neural Operators can Benefit from Width ICML 2025 SPvR: Structured Pruning via Ranking UAI 2025 Bayesian Adaptation of Network Depth and Width for Continual Learning ICML 2024 Exploring the Complexity of Deep Neural Networks through Functional Equivalence ICML 2024 Revisiting Neural Networks for Continual Learning: An Architectural Perspective IJCAI 2024 Quasi-Equivalence between Width and Depth of Neural Networks JMLR 2023 Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture NeurIPS 2022 Wide Neural Networks Forget Less Catastrophically ICML 2022 Width is Less Important than Depth in ReLU Neural Networks COLT 2022 Adversarial Robustness Guarantees for Random Deep Neural Networks ICML 2021 Are wider nets better given the same number of parameters? ICLR 2021 BCNet: Searching for Network Width With Bilaterally Coupled Network CVPR 2021 Do Wider Neural Networks Really Help Adversarial Robustness? NeurIPS 2021 Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks NeurIPS 2021 Frivolous Units: Wider Networks Are Not Really That Wide AAAI 2021 AOWS: Adaptive and Optimal Network Width Search With Latency Constraints CVPR 2020 Densely Connected Search Space for More Flexible Neural Architecture Search CVPR 2020 Finite Depth and Width Corrections to the Neural Tangent Kernel ICLR 2020 Limits to Depth Efficiencies of Self-Attention NeurIPS 2020 Rethinking Bias-Variance Trade-off for Generalization of Neural Networks ICML 2020 The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent ICML 2020 Universal Approximation with Deep Narrow Networks COLT 2020 Wide Hidden Expansion Layer for Deep Convolutional Neural Networks WACV 2020