Research Interests
My area of interests lie in Multi-modal AI especially in the intersection of Natural Language Processing,
Computer Vision and Robotics. I had worked in the areas of multi-modal dialog systems, domain adaptation,
text classification etc.
I like to reproduce results from the papers published in CVPR, ICCV, Neurips, ICLR etc.
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News
- Paper accepted at ACM 22 .
- New pre-print on dialog systems.
- Paper accepted at ICPR 22.
- New pre-print on interactive video retrieval systems.
- I will be joining Intel AI as a research intern in summer 2022.
- Serving as a reviewer for ACL rolling review .
- Working as RA under Prof. Shashank on inductive biases in language models.
- Started my Masters program at UNC Chapel Hill.
- Paper submitted to ACM TOMM journal on end-to-end slot identification in multi-modal dialog systems.
- Served as reviewer for ACL'21
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Peer Reviewed Publications |
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Learning to Retrieve Videos by Asking Questions
Avinash Madasu,
Junier Oliva and
Gedas Bertasius
ACM MM, 2022
arxiv
We propose a novel framework for Video Retrieval using Dialog (ViReD), which enables the user to interact with an AI agent via
multiple rounds of dialog. We also demonstrate that our proposed approach also generalizes to the real-world settings that involve
interactions with real humans, thus, demonstrating the robustness and generality of our framework.
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Sequential Domain Adaptation through
Elastic Weight Consolidation for Sentiment Analysis
Avinash Madasu,
Vijjini Anvesh Rao
ICPR, 2020
arxiv
We present a anti-curriculum based sequential domain adaptation. The sequential domain adaptation is trained elastic weight consolidation.
The proposed approach outperformed previous SOTA architectures and the training time is very less compared to previous methods. It is also
architecture agnostic.
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A Position Aware Decay Weighted Network for Aspect based Sentiment Analysis
Avinash Madasu,
Vijjini Anvesh Rao
NLDB, 2020
arxiv
We propose a model that leverages the positional information of the aspect. The proposed model introduces a decay mechanism based on position.
This decay function mandates the contribution of input words for ABSA. The performance is measured on two standard datasets from SemEval 2014 Task 4.
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Sequential Learning of Convolutional Features for Effective Text Classification
Avinash Madasu,
Vijjini Anvesh Rao
EMNLP, 2019
arxiv
We propose a Sequential Convolutional Attentive Recurrent Network (SCARN). Extensive experiments establish that
SCARN outperforms other recurrent convolutional architectures with significantly less parameters.
Furthermore, SCARN achieves better performance compared to equally large various deep CNN and LSTM architectures |
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Efficient Feature Selection techniques for Sentiment Analysis
Avinash Madasu,
Sivasankar E
Multimedia Tools and Applications, 2019
arxiv
In this paper, we aim to study the performance of different feature selection techniques for sentiment analysis.
Ensemble techniques are applied on classifiers to enhance the performance on sentiment analysis.
We show that, when the best FS techniques are trained using ensemble methods achieve remarkable results on sentiment analysis and outperforms neural networks.
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Gated Convolutional Neural Networks for Domain Adaptation
Avinash Madasu,
Vijjini Anvesh Rao
NLDB, 2019
arxiv
In this paper, we propose Gated CNN for domain adaptation in sentiment analysis. Extensive experimentation on two
standard datasets reveal that training with Gated Convolutional Neural Networks give significantly better performance on target domains than
regular convolution and recurrent based architectures.
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Effectiveness of Self Normalizing Neural Networks for Text Classification
Avinash Madasu,
Vijjini Anvesh Rao
CICLing, 2019
arxiv
In this paper we aim to show the effectiveness of proposed, Self Normalizing Convolutional Neural Networks(SCNN) on text classification.
We analyze their performance with the standard CNN architecture used on several text classification datasets. Our experiments demonstrate that
SCNN achieves comparable results to standard CNN model with significantly fewer parameters. Furthermore it also outperforms CNN with equal number of parameters.
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A Study of Feature Extraction techniques for Sentiment Analysis
Avinash Madasu,
Sivasankar E
IEMIS, 2018
arxiv
We perform a study on the performance of unsupervised feature extraction techniques TF-IDF and Paragraph2Vec for sentiment classification.
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Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference
Avinash Madasu,
- Fine-tuned BERT Base Uncased model on SemEval 2014 datasets Laptop and Restaurant for Aspect Based Sentiment Analysis.
- Hidden representations are taken from CLS token in each of the 12 hidden layers. These representations are trained using LSTM.
- Achieved near State-of-the-art results of 84% on Restaurant and 77% on Laptop (Metric:Accuracy).
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Adaptive Methods for Nonconvex Optimization
Avinash Madasu,
- Successfully reproduced the results of the paper \Adaptive Methods for Nonconvex Optimization".
- Implemented Yogi optimizer as proposed in the paper. This implementation is included in the open source project pytorch-optimizer".
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Highway Networks
Avinash Madasu,
- Implemented a Highway Network for performing Image Classification on CIFAR-10 dataset.
- Achieved an accuracy of 70.35% with a simple 3 layer Highway Network.
- Implemented Yogi optimizer as proposed in the paper. This implementation is included in the open source project pytorch-optimizer".
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All you need to know about Normalization
Avinash Madasu,
- Studied the effects of using different Normalization techniques like Batch Normalization, Layer Normalization and RMS Normalization on CNN for Image Classification.
- Evaluated pros and cons of each of the Normalization techniques and their dynamics while training CNN.
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Recurrent-Neural-Filters
Avinash Madasu,
- Implemented a class of Convolutional Neural Networks that utilize LSTM networks as convolutional filters.
- Achieved a competitive accuracy of 88% on SST-2 dataset..
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Design taken from here & inspiration from here
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