activity
20182021
most citedA Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference

162 citations · 182 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL20219 cited

One to rule them all: Towards Joint Indic Language Hate Speech Detection

Mehar Bhatia, Tenzin Singhay Bhotia, Akshat Agarwal +5

This paper is a contribution to the Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) 2021 shared task. Social media today is a hotbed of toxic an…

cs.CL202011 cited

Indic-Transformers: An Analysis of Transformer Language Models for Indian Languages

Kushal Jain, Adwait Deshpande, Kumar Shridhar +2

Language models based on the Transformer architecture have achieved state-of-the-art performance on a wide range of NLP tasks such as text classification, question-answering, and t…

stat.ME2020

Kernel Two-Sample and Independence Tests for Non-Stationary Random Processes

Felix Laumann, Julius von Kügelgen, Mauricio Barahona

Two-sample and independence tests with the kernel-based MMD and HSIC have shown remarkable results on i.i.d. data and stationary random processes. However, these statistics are not…

econ.EM2020

Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals

Felix Laumann, Julius von Kügelgen, Mauricio Barahona

The United Nations' ambitions to combat climate change and prosper human development are manifested in the Paris Agreement and the Sustainable Development Goals (SDGs), respectivel…

cs.LG2019162 cited

A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference

Kumar Shridhar, Felix Laumann, Marcus Liwicki

Artificial Neural Networks are connectionist systems that perform a given task by learning on examples without having prior knowledge about the task. This is done by finding an opt…

cs.LG2018

Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference

Kumar Shridhar, Felix Laumann, Marcus Liwicki

We introduce a novel uncertainty estimation for classification tasks for Bayesian convolutional neural networks with variational inference. By normalizing the output of a Softplus…