Training Deep Networks without Learning Rates Through Coin Betting

05/22/2017
by   Francesco Orabona, et al.
0

Deep learning methods achieve state-of-the-art performance in many application scenarios. Yet, these methods require a significant amount of hyperparameters tuning in order to achieve the best results. In particular, tuning the learning rates in the stochastic optimization process is still one of the main bottlenecks. In this paper, we propose a new stochastic gradient descent procedure for deep networks that does not require any learning rate setting. Contrary to previous methods, we do not adapt the learning rates nor we make use of the assumed curvature of the objective function. Instead, we reduce the optimization process to a game of betting on a coin and propose a learning-rate-free optimal algorithm for this scenario. Theoretical convergence is proven for convex and quasi-convex functions and empirical evidence shows the advantage of our algorithm over popular stochastic gradient algorithms.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

10/25/2021

Accelerated Almost-Sure Convergence Rates for Nonconvex Stochastic Gradient Descent using Stochastic Learning Rates

Large-scale optimization problems require algorithms both effective and ...
02/15/2015

Equilibrated adaptive learning rates for non-convex optimization

Parameter-specific adaptive learning rate methods are computationally ef...
02/12/2020

LaProp: a Better Way to Combine Momentum with Adaptive Gradient

Identifying a divergence problem in Adam, we propose a new optimizer, La...
08/07/2018

Robust Implicit Backpropagation

Arguably the biggest challenge in applying neural networks is tuning the...
02/22/2021

A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization

Machine learning practitioners invest significant manual and computation...
02/14/2020

Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function

This article suggests that deterministic Gradient Descent, which does no...
06/25/2020

Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation

Many machine learning models require a training procedure based on runni...

Code Repositories

cocob

TensorFlow implementation of COCOB


view repo

chainer-cocob

COCOB-Backprop (https://arxiv.org/abs/1705.07795) implementation for Chainer


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.