Timezone: »
Currently, deep neural networks are deployed on low-power portable devices by first training a full-precision model using powerful hardware, and then deriving a corresponding low-precision model for efficient inference on such systems. However, training models directly with coarsely quantized weights is a key step towards learning on embedded platforms that have limited computing resources, memory capacity, and power consumption. Numerous recent publications have studied methods for training quantized networks, but these studies have mostly been empirical. In this work, we investigate training methods for quantized neural networks from a theoretical viewpoint. We first explore accuracy guarantees for training methods under convexity assumptions. We then look at the behavior of these algorithms for non-convex problems, and show that training algorithms that exploit high-precision representations have an important greedy search phase that purely quantized training methods lack, which explains the difficulty of training using low-precision arithmetic.
Author Information
Hao Li (Amazon Web Services)
Soham De (University of Maryland, College Park)
Zheng Xu (University of Maryland, College Park)
Christoph Studer (Cornell University)
Hanan Samet (University of Maryland at College Park)
Tom Goldstein (University of Maryland)
More from the Same Authors
-
2020 : An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process »
David Tran · Alex Valtchanov · Keshav R Ganapathy · Raymond Feng · Eric Slud · Micah Goldblum · Tom Goldstein -
2021 : Execute Order 66: Targeted Data Poisoning for Reinforcement Learning via Minuscule Perturbations »
Harrison Foley · Liam Fowl · Tom Goldstein · Gavin Taylor -
2021 : Diurnal or Nocturnal? Federated Learning from Periodically Shifting Distributions »
Chen Zhu · Zheng Xu · Mingqing Chen · Jakub Konečný · Andrew S Hard · Tom Goldstein -
2022 : Investigating Reproducibility from the Decision Boundary Perspective. »
Gowthami Somepalli · Arpit Bansal · Liam Fowl · Ping-yeh Chiang · Yehuda Dar · Richard Baraniuk · Micah Goldblum · Tom Goldstein -
2022 : A Deep Dive into Dataset Imbalance and Bias in Face Identification »
Valeriia Cherepanova · Steven Reich · Samuel Dooley · Hossein Souri · John Dickerson · Micah Goldblum · Tom Goldstein -
2022 : SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training »
Gowthami Somepalli · Avi Schwarzschild · Micah Goldblum · C. Bayan Bruss · Tom Goldstein -
2022 : Transfer Learning with Deep Tabular Models »
Roman Levin · Valeriia Cherepanova · Avi Schwarzschild · Arpit Bansal · C. Bayan Bruss · Tom Goldstein · Andrew Wilson · Micah Goldblum -
2022 : A Deep Dive into Dataset Imbalance and Bias in Face Identification »
Valeriia Cherepanova · Steven Reich · Samuel Dooley · Hossein Souri · John Dickerson · Micah Goldblum · Tom Goldstein -
2022 : A Deep Dive into Dataset Imbalance and Bias in Face Identification »
Valeriia Cherepanova · Steven Reich · Samuel Dooley · Hossein Souri · John Dickerson · Micah Goldblum · Tom Goldstein -
2022 : Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries »
Yuxin Wen · Arpit Bansal · Hamid Kazemi · Eitan Borgnia · Micah Goldblum · Jonas Geiping · Tom Goldstein -
2022 : Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation »
Hong-Min Chu · Jonas Geiping · Liam Fowl · Micah Goldblum · Tom Goldstein -
2022 : Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models »
Liam Fowl · Jonas Geiping · Steven Reich · Yuxin Wen · Wojciech Czaja · Micah Goldblum · Tom Goldstein -
2022 : DP-InstaHide: Data Augmentations Provably Enhance Guarantees Against Dataset Manipulations »
Eitan Borgnia · Jonas Geiping · Valeriia Cherepanova · Liam Fowl · Arjun Gupta · Amin Ghiasi · Furong Huang · Micah Goldblum · Tom Goldstein -
2022 : Transfer Learning with Deep Tabular Models »
Roman Levin · Valeriia Cherepanova · Avi Schwarzschild · Arpit Bansal · C. Bayan Bruss · Tom Goldstein · Andrew Wilson · Micah Goldblum -
2022 Poster: Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability »
Roman Levin · Manli Shu · Eitan Borgnia · Furong Huang · Micah Goldblum · Tom Goldstein -
2022 Poster: Robustness Disparities in Face Detection »
Samuel Dooley · George Z Wei · Tom Goldstein · John Dickerson -
2022 Poster: Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models »
Manli Shu · Weili Nie · De-An Huang · Zhiding Yu · Tom Goldstein · Anima Anandkumar · Chaowei Xiao -
2022 Poster: Autoregressive Perturbations for Data Poisoning »
Pedro Sandoval-Segura · Vasu Singla · Jonas Geiping · Micah Goldblum · Tom Goldstein · David Jacobs -
2022 Poster: Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch »
Hossein Souri · Liam Fowl · Rama Chellappa · Micah Goldblum · Tom Goldstein -
2022 Poster: End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking »
Arpit Bansal · Avi Schwarzschild · Eitan Borgnia · Zeyad Emam · Furong Huang · Micah Goldblum · Tom Goldstein -
2021 Poster: GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training »
Chen Zhu · Renkun Ni · Zheng Xu · Kezhi Kong · W. Ronny Huang · Tom Goldstein -
2020 : The Intrinsic Dimension of Images and Its Impact on Learning »
Chen Zhu · Micah Goldblum · Ahmed Abdelkader · Tom Goldstein · Phillip Pope -
2020 Workshop: Workshop on Dataset Curation and Security »
Nathalie Baracaldo · Yonatan Bisk · Avrim Blum · Michael Curry · John Dickerson · Micah Goldblum · Tom Goldstein · Bo Li · Avi Schwarzschild -
2020 Poster: Detection as Regression: Certified Object Detection with Median Smoothing »
Ping-yeh Chiang · Michael Curry · Ahmed Abdelkader · Aounon Kumar · John Dickerson · Tom Goldstein -
2020 Poster: Certifying Confidence via Randomized Smoothing »
Aounon Kumar · Alexander Levine · Soheil Feizi · Tom Goldstein -
2020 Poster: Adversarially Robust Few-Shot Learning: A Meta-Learning Approach »
Micah Goldblum · Liam Fowl · Tom Goldstein -
2020 Poster: MetaPoison: Practical General-purpose Clean-label Data Poisoning »
W. Ronny Huang · Jonas Geiping · Liam Fowl · Gavin Taylor · Tom Goldstein -
2020 Poster: Certifying Strategyproof Auction Networks »
Michael Curry · Ping-yeh Chiang · Tom Goldstein · John Dickerson -
2019 Poster: Adversarial training for free! »
Ali Shafahi · Mahyar Najibi · Mohammad Amin Ghiasi · Zheng Xu · John Dickerson · Christoph Studer · Larry Davis · Gavin Taylor · Tom Goldstein -
2018 Poster: Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks »
Ali Shafahi · W. Ronny Huang · Mahyar Najibi · Octavian Suciu · Christoph Studer · Tudor Dumitras · Tom Goldstein -
2018 Poster: Visualizing the Loss Landscape of Neural Nets »
Hao Li · Zheng Xu · Gavin Taylor · Christoph Studer · Tom Goldstein -
2016 Workshop: Machine Learning for Education »
Richard Baraniuk · Jiquan Ngiam · Christoph Studer · Phillip Grimaldi · Andrew Lan -
2015 : Spotlight »
Furong Huang · William Gray Roncal · Tom Goldstein -
2015 Poster: Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing »
Tom Goldstein · Min Li · Xiaoming Yuan -
2014 Workshop: Human Propelled Machine Learning »
Richard Baraniuk · Michael Mozer · Divyanshu Vats · Christoph Studer · Andrew E Waters · Andrew Lan