Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

Neural networks (NNs) and decision trees (DTs) are both popular models of machine
learning, yet coming with mutually exclusive advantages and limitations. To bring the best of …

Neural-network (NN)-based methods show high performance in sound event localization
and detection (SELD). Conventional NN-based methods use two branches for a sound …

We study deep neural networks (DNNs) trained on natural image data with entirely random
labels. Despite its popularity in the literature, where it is often used to study memorization …

Benefited from the powerful ability of spatial temporal Graph Convolutional Networks (ST-
GCNs), skeleton-based human action recognition has gained promising success. However …

We present an unsupervised learning pipeline for dense depth, optical flow and egomotion
estimation for autonomous driving applications, using the event-based output of the …

Insights and analysis are only as good as the available data. Data cleaning is one of the
most important steps to create quality data decision making. Machine learning (ML) helps …

Multi-scale object detection is a preeminent challenge in computer vision and image
processing. Several deep learning models that are designed to detect various objects miss …

A sign language recognition system can be applied to reduce a communication gap
between deaf and normal persons. However, the Indian sign language recognition (ISL) …

This report describes our systems submitted to the DCASE2021 challenge task 3: sound
event localization and detection (SELD) with directional interference. Our previous system …