Search Results for author:
Found 14 papers, 1 papers with code
Zero-shot cross-lingual open domain question answering
People speaking different kinds of languages search for information in a cross-lingual manner.
R3 : Refined Retriever-Reader pipeline for Multidoc2dial
In this paper, we present our submission to the DialDoc shared task based on the MultiDoc2Dial dataset.
ICNN: INPUT-CONDITIONED FEATURE REPRESENTATION LEARNING FOR TRANSFORMATION-INVARIANT NEURAL NETWORK
And our proposed decoder network serves the purpose of reducing the transformation present in the input image by learning to construct a representative image of the input image class.
Smaller Models, Better Generalization
Reducing network complexity has been a major research focus in recent years with the advent of mobile technology.
MTCNET: Multi-task Learning Paradigm for Crowd Count Estimation
The auxiliary task helps in capturing the relevant scale-related information to improve the performance of the main task.
Learning Discriminative features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition
This paper proposes a Convolutional Neural Network (CNN) inspired by Multitask Learning (MTL) and based on speech features trained under the joint supervision of softmax loss and center loss, a powerful metric learning strategy, for the recognition of emotion in speech.
Visual Context-aware Convolution Filters for Transformation-invariant Neural Network
We propose a novel visual context-aware filter generation module which incorporates contextual information present in images into Convolutional Neural Networks (CNNs).
From Fully Supervised to Zero Shot Settings for Twitter Hashtag Recommendation
To overcome this limitation, we propose a Zero Shot Learning (ZSL) paradigm for predicting unseen hashtag labels by learning the relationship between the semantic space of tweets and the embedding space of hashtag labels.
Deep Learning based Emotion Recognition System Using Speech Features and Transcriptions
This paper proposes a speech emotion recognition method based on speech features and speech transcriptions (text).
Focal Loss based Residual Convolutional Neural Network for Speech Emotion Recognition
This paper proposes a Residual Convolutional Neural Network (ResNet) based on speech features and trained under Focal Loss to recognize emotion in speech.
Stance Detection in Code-Mixed Hindi-English Social Media Data using Multi-Task Learning
We apply our approach on Hindi-English code-mixed corpus against the target entity - {``}Demonetisation.
Deep Learning Techniques for Humor Detection in Hindi-English Code-Mixed Tweets
We propose bilingual word embeddings based on word2vec and fastText models (CBOW and Skip-gram) to address the problem of Humor detection in Hindi-English code-mixed tweets in combination with deep learning architectures.
Exploiting SIFT Descriptor for Rotation Invariant Convolutional Neural Network
The SIFT descriptor layer captures the orientation and the spatial relationship of the features extracted by convolutional layer.
Learning Neural Network Classifiers with Low Model Complexity
Our proposed approach yields benefits across a wide range of architectures, in comparison to and in conjunction with methods such as Dropout and Batch Normalization, and our results strongly suggest that deep learning techniques can benefit from model complexity control methods such as the LCNN learning rule.
