141 citations · 173 across the 5 of their papers we have counts for
5 papers
Privacy Enhancement for Cloud-Based Few-Shot Learning
Archit Parnami, Muhammad Usama, Liyue Fan +1
Requiring less data for accurate models, few-shot learning has shown robustness and generality in many application domains. However, deploying few-shot models in untrusted environm…
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning
Archit Parnami, Minwoo Lee
Few-Shot Learning refers to the problem of learning the underlying pattern in the data just from a few training samples. Requiring a large number of data samples, many deep learnin…
Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social Networks
Archit Parnami, Mayuri Deshpande, Anant Kumar Mishra +1
Recent advances in neural networks have solved common graph problems such as link prediction, node classification, node clustering, node recommendation by developing embeddings of…
Pruning Attention Heads of Transformer Models Using A* Search: A Novel Approach to Compress Big NLP Architectures
Archit Parnami, Rahul Singh, Tarun Joshi
Recent years have seen a growing adoption of Transformer models such as BERT in Natural Language Processing and even in Computer Vision. However, due to their size, there has been…
Few-Shot Keyword Spotting With Prototypical Networks
Archit Parnami, Minwoo Lee
Recognizing a particular command or a keyword, keyword spotting has been widely used in many voice interfaces such as Amazon's Alexa and Google Home. In order to recognize a set of…