Search Results for author: Ashish Sardana

Found 6 papers, 5 papers with code

[Re] Training Binary Neural Networks using the Bayesian Learning Rule

1 code implementation RC 2020 Prateek Garg, Lakshya Singhal, Ashish Sardana

When we tried this in a semantic segmentation context, we found that the results were very underwhelming and in contrast with the seemingly good results by the STE optimizer even with much hyperparameter tuning.

Continual Learning Semantic Segmentation

[Re] Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings

1 code implementation RC 2020 Jishnu Jaykumar P, Ashish Sardana

In addition to making the codebase more modular and easy to navigate, we have made changes to incorporate different transformers in the question embedding module.

Answer Selection Knowledge Graph Embedding +5

Stochastic Talking Face Generation Using Latent Distribution Matching

1 code implementation21 Nov 2020 Ravindra Yadav, Ashish Sardana, Vinay P Namboodiri, Rajesh M Hegde

Indeed, just having the ability to generate a single talking face would make a system almost robotic in nature.

Talking Face Generation Video Generation

Speech Prediction in Silent Videos using Variational Autoencoders

no code implementations14 Nov 2020 Ravindra Yadav, Ashish Sardana, Vinay P Namboodiri, Rajesh M Hegde

Understanding the relationship between the auditory and visual signals is crucial for many different applications ranging from computer-generated imagery (CGI) and video editing automation to assisting people with hearing or visual impairments.

Video Editing

Multilogue-Net: A Context-Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation

1 code implementation WS 2020 Aman Shenoy, Ashish Sardana

Sentiment Analysis and Emotion Detection in conversation is key in several real-world applications, with an increase in modalities available aiding a better understanding of the underlying emotions.

Multimodal Sentiment Analysis

Multilogue-Net: A Context Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation

1 code implementation arXiv preprint 2020 Aman Shenoy, Ashish Sardana

Sentiment Analysis and Emotion Detection in conversation is key in several real-world applications, with an increase in modalities available aiding a better understanding of the underlying emotions.

Ranked #8 on Multimodal Sentiment Analysis on CMU-MOSEI (using extra training data)

Emotion Recognition in Conversation Multimodal Emotion Recognition +1

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