Search Results for author: Manjesh Kumar Hanawal

Found 7 papers, 3 papers with code

CEEBERT: Cross-Domain Inference in Early Exit BERT

1 code implementation23 May 2024 Divya Jyoti Bajpai, Manjesh Kumar Hanawal

Experimental results on five distinct datasets with BERT and ALBERT models demonstrate CeeBERT's ability to improve latency by reducing unnecessary computations with minimal drop in performance.

Domain Adaptation

FAIR: Filtering of Automatically Induced Rules

no code implementations23 Feb 2024 Divya Jyoti Bajpai, Ayush Maheshwari, Manjesh Kumar Hanawal, Ganesh Ramakrishnan

The availability of large annotated data can be a critical bottleneck in training machine learning algorithms successfully, especially when applied to diverse domains.

text-classification Text Classification

I-SplitEE: Image classification in Split Computing DNNs with Early Exits

1 code implementation19 Jan 2024 Divya Jyoti Bajpai, Aastha Jaiswal, Manjesh Kumar Hanawal

The recent advances in Deep Neural Networks (DNNs) stem from their exceptional performance across various domains.

Computational Efficiency Image Classification

HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers

no code implementations30 Dec 2023 Debamita Ghosh, Manjesh Kumar Hanawal, Nikola Zlatanova

To overcome this, {\it\HB} works with the discrete values of phase-shifting parameters and exploits their unimodal relations with channel gains to learn the optimal values faster.

ICQ: A Quantization Scheme for Best-Arm Identification Over Bit-Constrained Channels

no code implementations30 Apr 2023 Fathima Zarin Faizal, Adway Girish, Manjesh Kumar Hanawal, Nikhil Karamchandani

We study the problem of best-arm identification in a distributed variant of the multi-armed bandit setting, with a central learner and multiple agents.


Cheap Bandits

no code implementations15 Jun 2015 Manjesh Kumar Hanawal, Venkatesh Saligrama, Michal Valko, R\' emi Munos

We consider stochastic sequential learning problems where the learner can observe the \textit{average reward of several actions}.

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