Search Results for author:
Found 7 papers, 5 papers with code
AdaNorm: Adaptive Gradient Norm Correction based Optimizer for CNNs
In this paper, we propose a novel AdaNorm based SGD optimizers by correcting the norm of gradient in each iteration based on the adaptive training history of gradient norm.
Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark
The most popular and common non-linearity layers are activation functions (AFs), such as Logistic Sigmoid, Tanh, ReLU, ELU, Swish and Mish.
AdaInject: Injection Based Adaptive Gradient Descent Optimizers for Convolutional Neural Networks
Overall, we observe very promising performance improvement of existing optimizers with the proposed AdaInject approach.
diffGrad: An Optimization Method for Convolutional Neural Networks
In this paper, a novel optimizer is proposed based on the difference between the present and the immediate past gradient (i. e., diffGrad).
A BLSTM Network for Printed Bengali OCR System with High Accuracy
It sometimes recognizes a character of Bengali into the same character of a non-Bengali script, especially Assamese, which has no distinction from Bengali, except for a few characters.
Optical Character Recognition (OCR)
Vocal Bursts Intensity Prediction
LiSHT: Non-Parametric Linearly Scaled Hyperbolic Tangent Activation Function for Neural Networks
The proposed LiSHT is non-parametric and tackles the dying gradient problem.
Trajectory-based Scene Understanding using Dirichlet Process Mixture Model
If usual traffic paths are learned in a nonparametric way, manual interventions in road marking road can be avoided.
