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Found 11 papers, 2 papers with code
Exploring Sub-MeV Sensitivity of AstroSat-CZTI for ON-axis Bright Sources
Here we explore the possibility of using the Compton events as well as the low gain pixels to extend the spectroscopic energy range of CZTI for ON-axis bright X-ray sources.
Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena
Sub-MeV spectroscopy with AstroSat-CZT Imager for Gamma Ray Bursts
While the 2-pixel Compton scattered events (100 - 300 keV) are used to extract sensitive spectroscopic information, the inclusion of the low-gain pixels (around 20% of the detector plane) after careful calibration extends the energy range of Compton energy spectra to 600 keV.
High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics
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.
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).
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.
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).
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.
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.
Biconvex Relaxation for Semidefinite Programming in Computer Vision
We propose a general framework to approximately solve large-scale semidefinite problems (SDPs) at low complexity.
