A
Anupama Ray
Researcher at IBM
Publications - 29
Citations - 237
Anupama Ray is an academic researcher from IBM. The author has contributed to research in topics: Ticket & Service provider. The author has an hindex of 5, co-authored 27 publications receiving 148 citations. Previous affiliations of Anupama Ray include Indian Institute of Technology Delhi & Variable Energy Cyclotron Centre.
Papers
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Proceedings ArticleDOI
Multi-level Attention Network using Text, Audio and Video for Depression Prediction
TL;DR: A novel multi-level attention based network for multi-modal depression prediction that fuses features from audio, video and text modalities while learning the intra and intermodality relevance.
Proceedings ArticleDOI
Text recognition using deep BLSTM networks
TL;DR: A Deep Bidirectional Long Short Term Memory (LSTM) based Recurrent Neural Network architecture for text recognition that uses Connectionist Temporal Classification (CTC) for training to learn the labels of an unsegmented sequence with unknown alignment.
Proceedings ArticleDOI
An End-to-End Trainable Framework for Joint Optimization of Document Enhancement and Recognition
Anupama Ray,Manoj Sharma,Avinash Upadhyay,Megh Makwana,Santanu Chaudhury,Akkshita Trivedi,Ajay Kumar Singh,Anil Kumar Saini +7 more
TL;DR: An end-to-end trainable deep-learning based framework for joint optimization of document enhancement and recognition, using a generative adversarial network (GAN) based framework to perform image denoising followed by deep back projection network (DBPN) for super-resolution.
Proceedings Article
Semantic Parsing for Technical Support Questions
TL;DR: An approach for semantic parsing of technical questions that uses grammatical structure to extract these attributes as a baseline, and a CRF based model that can improve performance considerably in the presence of annotated data for training are presented.
Proceedings ArticleDOI
A Multimodal Corpus for Emotion Recognition in Sarcasm
TL;DR: Exhaustive experimentation with multimodal (text, audio, and video) fusion models establishes a benchmark for exact emotion recognition in sarcasm and outperforms the state-of-art sarcasm detection.