Principled Weight Initialisation for Input-Convex Neural Networks

19 December 2023

Papers citing "Principled Weight Initialisation for Input-Convex Neural Networks"

8 / 8 papers shown

Title
Revisiting Residual Connections: Orthogonal Updates for Stable and Efficient Deep Networks Giyeong Oh Woohyun Cho Siyeol Kim Suhwan Choi Younjae Yu 2 0 0 17 May 2025
Gradient Networks Shreyas Chaudhari Srinivasa Pranav J. M. F. Moura 58 0 0 28 Jan 2025
Optimal Flow Matching: Learning Straight Trajectories in Just One Step Nikita Kornilov Petr Mokrov Alexander Gasnikov Alexander Korotin 34 11 0 19 Mar 2024
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks Lisa Schneckenreiter Richard Freinschlag Florian Sestak Johannes Brandstetter G. Klambauer Andreas Mayr 46 5 0 07 Mar 2024
Real-Time Machine-Learning-Based Optimization Using Input Convex LSTM Zihao Wang Donghan Yu Zhe Wu 29 6 0 13 Nov 2023
Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping James Martens Andy Ballard Guillaume Desjardins G. Swirszcz Valentin Dalibard Jascha Narain Sohl-Dickstein S. Schoenholz 88 43 0 05 Oct 2021
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 233 348 0 14 Jun 2018
Input Convex Neural Networks Brandon Amos Lei Xu J. Zico Kolter 187 599 0 22 Sep 2016