Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization

14 October 2017

Papers citing "Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization"

27 / 27 papers shown

Title
Dynamic Cross Attention for Audio-Visual Person Verification R Gnana Praveen Jahangir Alam 38 1 0 07 Mar 2024
Are Ensembles Getting Better all the Time? Pierre-Alexandre Mattei Damien Garreau OOD FedML 41 1 0 29 Nov 2023
Connecting NTK and NNGP: A Unified Theoretical Framework for Wide Neural Network Learning Dynamics Yehonatan Avidan Qianyi Li H. Sompolinsky 60 8 0 08 Sep 2023
On The Impact of Machine Learning Randomness on Group Fairness Prakhar Ganesh Hong Chang Martin Strobel Reza Shokri FaML 30 30 0 09 Jul 2023
Context Normalization Layer with Applications Bilal Faye M. Dilmi Hanene Azzag M. Lebbah D. Bouchaffra 23 0 0 14 Mar 2023
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators Malte J. Rasch C. Mackin Manuel Le Gallo An Chen A. Fasoli ... P. Narayanan H. Tsai G. Burr Abu Sebastian Vijay Narayanan 13 83 0 16 Feb 2023
NEON: Enabling Efficient Support for Nonlinear Operations in Resistive RAM-based Neural Network Accelerators Aditya Manglik Minesh Patel Haiyu Mao Behzad Salami Jisung Park Lois Orosa O. Mutlu 15 1 0 10 Nov 2022
Circling Back to Recurrent Models of Language Gábor Melis 32 0 0 03 Nov 2022
Quantum Vision Transformers El Amine Cherrat Iordanis Kerenidis Natansh Mathur Jonas Landman M. Strahm Yun. Y Li ViT 34 55 0 16 Sep 2022
ApproxTrain: Fast Simulation of Approximate Multipliers for DNN Training and Inference Jing Gong Hassaan Saadat Hasindu Gamaarachchi Haris Javaid X. Hu S. Parameswaran 31 12 0 09 Sep 2022
A Kernel-Expanded Stochastic Neural Network Y. Sun F. Liang 20 5 0 14 Jan 2022
AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator Chuteng Zhou F. García-Redondo Julian Büchel I. Boybat Xavier Timoneda Comas S. Nandakumar Shidhartha Das Abu Sebastian Manuel Le Gallo P. Whatmough 25 16 0 10 Nov 2021
Reservoir Transformers Sheng Shen Alexei Baevski Ari S. Morcos Kurt Keutzer Michael Auli Douwe Kiela 35 17 0 30 Dec 2020
Benchmarking Inference Performance of Deep Learning Models on Analog Devices Omobayode Fagbohungbe Lijun Qian 19 7 0 24 Nov 2020
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning Andreas Kirsch Clare Lyle Y. Gal 19 16 0 27 Mar 2020
Evaluating complexity and resilience trade-offs in emerging memory inference machines C. Bennett Ryan Dellana T. Xiao Ben Feinberg S. Agarwal S. Cardwell M. Marinella William M. Severa Brad Aimone 6 2 0 25 Feb 2020
Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation Chuteng Zhou Prad Kadambi Matthew Mattina P. Whatmough 13 35 0 14 Jan 2020
Mean-Field and Kinetic Descriptions of Neural Differential Equations Michael Herty T. Trimborn G. Visconti 33 6 0 07 Jan 2020
A Dual Camera System for High Spatiotemporal Resolution Video Acquisition Ming Cheng Zhan Ma M. Salman Asif Yiling Xu Haojie Liu Wenbo Bao Jun Sun 30 21 0 28 Sep 2019
Adaptive Regularization via Residual Smoothing in Deep Learning Optimization Jung-Kyun Cho Junseok Kwon Byung-Woo Hong 28 1 0 23 Jul 2019
Sequential Neural Networks as Automata William Merrill 10 74 0 04 Jun 2019
PANDA: AdaPtive Noisy Data Augmentation for Regularization of Undirected Graphical Models Yinan Li Xiao Liu Fang Liu 29 7 0 11 Oct 2018
Noisin: Unbiased Regularization for Recurrent Neural Networks Adji Bousso Dieng Rajesh Ranganath Jaan Altosaar David M. Blei 22 22 0 03 May 2018
Towards Robust Neural Networks via Random Self-ensemble Xuanqing Liu Minhao Cheng Huan Zhang Cho-Jui Hsieh FedML AAML 38 418 0 02 Dec 2017
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu M. Schuster Z. Chen Quoc V. Le Mohammad Norouzi ... Alex Rudnick Oriol Vinyals G. Corrado Macduff Hughes J. Dean AIMat 716 6,743 0 26 Sep 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015
To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout Prateek Jain Vivek Kulkarni Abhradeep Thakurta Oliver Williams 44 30 0 06 Mar 2015