DeepAI
Log In Sign Up

Inherent Weight Normalization in Stochastic Neural Networks

10/27/2019
by   Georgios Detorakis, et al.
0

Multiplicative stochasticity such as Dropout improves the robustness and generalizability of deep neural networks. Here, we further demonstrate that always-on multiplicative stochasticity combined with simple threshold neurons are sufficient operations for deep neural networks. We call such models Neural Sampling Machines (NSM). We find that the probability of activation of the NSM exhibits a self-normalizing property that mirrors Weight Normalization, a previously studied mechanism that fulfills many of the features of Batch Normalization in an online fashion. The normalization of activities during training speeds up convergence by preventing internal covariate shift caused by changes in the input distribution. The always-on stochasticity of the NSM confers the following advantages: the network is identical in the inference and learning phases, making the NSM suitable for online learning, it can exploit stochasticity inherent to a physical substrate such as analog non-volatile memories for in-memory computing, and it is suitable for Monte Carlo sampling, while requiring almost exclusively addition and comparison operations. We demonstrate NSMs on standard classification benchmarks (MNIST and CIFAR) and event-based classification benchmarks (N-MNIST and DVS Gestures). Our results show that NSMs perform comparably or better than conventional artificial neural networks with the same architecture.

READ FULL TEXT

page 1

page 2

page 3

page 4

02/11/2015

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Training Deep Neural Networks is complicated by the fact that the distri...
02/20/2021

Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference

Many real-world mission-critical applications require continual online l...
05/17/2022

Need is All You Need: Homeostatic Neural Networks Adapt to Concept Shift

In living organisms, homeostasis is the natural regulation of internal s...
02/20/2017

Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks

Traditionally, multi-layer neural networks use dot product between the o...
11/03/2022

An Adaptive Batch Normalization in Deep Learning

Batch Normalization (BN) is a way to accelerate and stabilize training i...
03/29/2021

Online Defense of Trojaned Models using Misattributions

This paper proposes a new approach to detecting neural Trojans on Deep N...
09/19/2018

Removing the Feature Correlation Effect of Multiplicative Noise

Multiplicative noise, including dropout, is widely used to regularize de...