Subba Reddy Oota

Subba Reddy Oota
National Institute for Research in Computer Science and Control | INRIA · Computer Science

Research Scholar

About

29
Publications
6,822
Reads
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57
Citations
Introduction
Subba Reddy Oota currently working as a Research Assistant at the Centre for Cognitive Science (CogSci), Machine Learning Lab, and Natural Language Processing Lab at International Institute of Information Technology, Hyderabad. Subba's most recent submission is "ExpertoCoder: Capturing Divergent Brain Regions Using Mixture of Regression Experts" at NeuralComputation Journal (under review).
Additional affiliations
June 2016 - present
International Institute of Information Technology, Hyderabad
Position
  • Research Assistant
January 2016 - June 2016
International Institute of Information Technology, Hyderabad
Position
  • Research Assistant
Education
August 2014 - July 2016
September 2007 - May 2011
K L University
Field of study
  • Computer Science

Publications

Publications (29)
Preprint
Full-text available
Several popular Transformer based language models have been found to be successful for text-driven brain encoding. However, existing literature leverages only pretrained text Transformer models and has not explored the efficacy of task-specific learned Transformer representations. In this work, we explore transfer learning from representations lear...
Preprint
Full-text available
Graph Convolutional Networks (GCN) have achieved state-of-art results on single text classification tasks like sentiment analysis, emotion detection, etc. However, the performance is achieved by testing and reporting on resource-rich languages like English. Applying GCN for multi-task text classification is an unexplored area. Moreover, training a...
Article
Full-text available
Due to the lack of a large annotated corpus, many resource-poor Indian languages struggle to reap the benefits of recent deep feature representations in Natural Language Processing (NLP). Moreover, adopting existing language models trained on large English corpora for Indian languages is often limited by data availability, rich morphological variat...
Preprint
Full-text available
Enabling effective brain-computer interfaces requires understanding how the human brain encodes stimuli across modalities such as visual, language (or text), etc. Brain encoding aims at constructing fMRI brain activity given a stimulus. There exists a plethora of neural encoding models which study brain encoding for single mode stimuli: visual (pre...
Preprint
Full-text available
How the brain captures the meaning of linguistic stimuli across multiple views is still a critical open question in neuroscience. Consider three different views of the concept apartment: (1) picture (WP) presented with the target word label, (2) sentence (S) using the target word, and (3) word cloud (WC) containing the target word along with other...
Preprint
Full-text available
Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the resting-state as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural conne...
Preprint
Full-text available
The Affordable care Act of 2010 had introduced Readmission reduction program in 2012 to reduce avoidable re-admissions to control rising healthcare costs. Wound care impacts 15 of medicare beneficiaries making it one of the major contributors of medicare health care cost. Health plans have been exploring proactive health care services that can focu...
Preprint
Full-text available
fMRI semantic category understanding using linguistic encoding models attempts to learn a forward mapping that relates stimuli to the corresponding brain activation. Classical encoding models use linear multivariate methods to predict brain activation (all the voxels) given the stimulus. However, these methods mainly assume multiple regions as one...
Conference Paper
Full-text available
Automated evaluation of handwritten answers has been a challenging problem for scaling the education system for many years. Speeding up the evaluation remains as the major bottleneck for enhancing the throughput of instructors. This paper describes an effective method for automatically evaluating the short descriptive handwritten answers from the d...
Conference Paper
Full-text available
Learning a forward mapping that relates stimuli to the corresponding brain activation measured by functional magnetic resonance imaging (fMRI) is termed as estimating encoding models. Computational tractability usually forces current encoding as well as decoding solutions to typically consider only a small subset of voxels from the actual 3D volume...
Preprint
Full-text available
Estimating the intensity of emotion has gained significance as modern textual inputs in potential applications like social media, e-retail markets, psychology, advertisements etc., carry a lot of emotions, feelings, expressions along with its meaning. However, the approaches of traditional sentiment analysis primarily focuses on classifying the sen...
Preprint
Full-text available
fMRI semantic category understanding using linguistic encoding models attempt to learn a forward mapping that relates stimuli to the corresponding brain activation. Classical encoding models use linear multi-variate methods to predict the brain activation (all voxels) given the stimulus. However, these methods essentially assume multiple regions as...
Preprint
Full-text available
The dispute of how the human brain represents conceptual knowledge has been argued in many scientific fields. Brain imaging studies have shown that the spatial patterns of neural activation in the brain are correlated with thinking about different semantic categories of words (for example, tools, animals, and buildings) or when viewing the related...
Chapter
Neural word embeddings have been widely used in modern NLP applications as they provide vector representation of words and capture the semantic properties of words and the linguistic relationship between the words. Many research groups have released their own version of word embeddings. However, they are trained on generic corpora, which limits the...
Conference Paper
Full-text available
Bizarre news items are those news items so strange and unusual that readers might question the claims presented in the news. This paper presents the first machine learning approach to bizarre news detection in online news media. We contribute by compiling the first bizarre news corpus of 23754 bizarre news headlines, and by developing a bizarre new...
Presentation
Full-text available
Details on ”Metastability of cortical BOLD Signals in Maturation and Senescence”
Article
Full-text available
Clickbait is a pejorative term describing web content that is aimed at generating online advertising revenue, especially at the expense of quality or accuracy, relying on sensationalist headlines or eye-catching thumbnail pictures to attract click-throughs and to encourage forwarding of the material over online social networks. We use distributed w...
Conference Paper
Full-text available
Transfer learning algorithms can be used when sufficient amount of training data is available in the source domain and limited training data is available in the target domain. The transfer of knowledge from one domain to another requires similarity between two domains. In many resource-poor languages, it is rare to find labeled training data in bot...
Conference Paper
Full-text available
Sentiment Analysis is one of the most active research areas in natural language processing and an extensively studied problem in data mining, web mining and text mining for English language. With the proliferation of social media these days, data is widely increasing in regional languages along with English. Telugu is one such regional language wit...

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