Predicting Training Time Without Training

28 August 2020

Papers citing "Predicting Training Time Without Training"

21 / 21 papers shown

Title
PreNeT: Leveraging Computational Features to Predict Deep Neural Network Training Time Alireza Pourali Arian Boukani Hamzeh Khazaei 72 0 0 20 Dec 2024
Predicting the Encoding Error of SIRENs Jeremy Vonderfecht Feng Liu AI4CE 40 3 0 29 Oct 2024
UnifiedNN: Efficient Neural Network Training on the Cloud Xingyu Lou Arthi Padmanabhan Spyridon Mastorakis FedML 46 0 0 02 Aug 2024
Neural Lineage Runpeng Yu Xinchao Wang 34 4 0 17 Jun 2024
Diffusion Soup: Model Merging for Text-to-Image Diffusion Models Benjamin Biggs Arjun Seshadri Yang Zou Achin Jain Aditya Golatkar Yusheng Xie Alessandro Achille Ashwin Swaminathan Stefano Soatto MoMe DiffM 43 10 0 12 Jun 2024
Off-the-Shelf Neural Network Architectures for Forex Time Series Prediction come at a Cost Theodoros Zafeiriou Dimitris Kalles AI4TS 29 0 0 17 May 2024
The fine print on tempered posteriors Konstantinos Pitas Julyan Arbel 30 1 0 11 Sep 2023
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models Guillermo Ortiz-Jiménez Alessandro Favero P. Frossard MoMe 51 110 0 22 May 2023
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation Noel Loo Ramin Hasani Mathias Lechner Alexander Amini Daniela Rus DD 42 5 0 02 Feb 2023
The Underlying Correlated Dynamics in Neural Training Rotem Turjeman Tom Berkov I. Cohen Guy Gilboa 27 3 0 18 Dec 2022
PROFET: Profiling-based CNN Training Latency Prophet for GPU Cloud Instances Sungjae Lee Y. Hur Subin Park Kyungyong Lee 19 1 0 10 Aug 2022
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels Yaodong Yu Alexander Wei Sai Praneeth Karimireddy Yi Ma Michael I. Jordan FedML 17 30 0 13 Jul 2022
Cold Posteriors through PAC-Bayes Konstantinos Pitas Julyan Arbel 26 5 0 22 Jun 2022
Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training? J. Mok Byunggook Na Ji-Hoon Kim Dongyoon Han Sungroh Yoon AAML 42 23 0 28 Mar 2022
Benchmarking Resource Usage for Efficient Distributed Deep Learning Nathan C. Frey Baolin Li Joseph McDonald Dan Zhao Michael Jones David Bestor Devesh Tiwari V. Gadepally S. Samsi 35 9 0 28 Jan 2022
Scaling Neural Tangent Kernels via Sketching and Random Features A. Zandieh Insu Han H. Avron N. Shoham Chaewon Kim Jinwoo Shin 11 31 0 15 Jun 2021
What can linearized neural networks actually say about generalization? Guillermo Ortiz-Jiménez Seyed-Mohsen Moosavi-Dezfooli P. Frossard 29 43 0 12 Jun 2021
Random Features for the Neural Tangent Kernel Insu Han H. Avron N. Shoham Chaewon Kim Jinwoo Shin 24 9 0 03 Apr 2021
A linearized framework and a new benchmark for model selection for fine-tuning Aditya Deshpande Alessandro Achille Avinash Ravichandran Hao Li L. Zancato Charless C. Fowlkes Rahul Bhotika Stefano Soatto Pietro Perona ALM 115 46 0 29 Jan 2021
Estimating informativeness of samples with Smooth Unique Information Hrayr Harutyunyan Alessandro Achille Giovanni Paolini Orchid Majumder Avinash Ravichandran Rahul Bhotika Stefano Soatto 27 24 0 17 Jan 2021
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 308 2,890 0 15 Sep 2016