Search Results for author: Saurabh Goyal

Found 6 papers, 3 papers with code

ProtoNER: Few shot Incremental Learning for Named Entity Recognition using Prototypical Networks

no code implementations3 Oct 2023 Ritesh Kumar, Saurabh Goyal, Ashish Verma, Vatche Isahagian

\\ We present \textbf{ProtoNER}: Prototypical Network based end-to-end KVP extraction model that allows addition of new classes to an existing model while requiring minimal number of newly annotated training samples.

document understanding Incremental Learning +5

Compression of Deep Neural Networks by combining pruning and low rank decomposition

no code implementations20 Oct 2018 Saurabh Goyal, Anamitra R Choudhury, Vivek Sharma, Yogish Sabharwal, Ashish Verma

Large number of weights in deep neural networks make the models difficult to be deployed in low memory environments such as, mobile phones, IOT edge devices as well as "inferencing as a service" environments on the cloud.

Model Compression

Efficient Inferencing of Compressed Deep Neural Networks

no code implementations1 Nov 2017 Dharma Teja Vooturi, Saurabh Goyal, Anamitra R. Choudhury, Yogish Sabharwal, Ashish Verma

Large number of weights in deep neural networks makes the models difficult to be deployed in low memory environments such as, mobile phones, IOT edge devices as well as "inferencing as a service" environments on cloud.

Quantization

Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things

1 code implementation ICML 2017 Ashish Kumar, Saurabh Goyal, Manik Varma

This paper develops a novel tree-based algorithm, called Bonsai, for efficient prediction on IoT devices – such as those based on the Arduino Uno board having an 8 bit ATmega328P microcontroller operating at 16 MHz with no native floating point support, 2 KB RAM and 32 KB read-only flash.

Action Classification BIG-bench Machine Learning

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