134 citations · 459 across the 38 of their papers we have counts for
4 papers · 1 filter
Leveraging Filter Correlations for Deep Model Compression
Pravendra Singh, Vinay Kumar Verma, Piyush Rai +1
We present a filter correlation based model compression approach for deep convolutional neural networks. Our approach iteratively identifies pairs of filters with the largest pairw…
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Shikhar Vashishth, Manik Bhandari, Prateek Yadav +3
Word embeddings have been widely adopted across several NLP applications. Most existing word embedding methods utilize sequential context of a word to learn its embedding. While th…
Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels
Gundeep Arora, Anupreet Porwal, Kanupriya Agarwal +2
The latent feature relational model (LFRM) is a generative model for graph-structured data to learn a binary vector representation for each node in the graph. The binary vector den…
A Generative Approach to Zero-Shot and Few-Shot Action Recognition
Ashish Mishra, Vinay Kumar Verma, M Shiva Krishna Reddy +3
We present a generative framework for zero-shot action recognition where some of the possible action classes do not occur in the training data. Our approach is based on modeling ea…