Search Results for author:
Found 7 papers, 4 papers with code
NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls
Specifically, we present NESTFUL, a benchmark to evaluate LLMs on nested sequences of API calls, i. e., sequences where the output of one API call is passed as input to a subsequent call.
ProtoNER: Few shot Incremental Learning for Named Entity Recognition using Prototypical Networks
\\ 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.
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
We demonstrate that our method attains up to 6. 8x reduction in inference time with <1% loss in accuracy when applied over ALBERT, a highly compressed version of BERT.
Compression of Deep Neural Networks by combining pruning and low rank decomposition
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.
Efficient Inferencing of Compressed Deep Neural Networks
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.
Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things
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.
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
Such applications demand prediction models with small storage and computational complexity that do not compromise significantly on accuracy.
