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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
PEFT Cheat Sheet: Succinct Explanations to the Numerous PEFT Methods for LLM
Published:
Have a quick look into the Parameter-Efficient Fine-Tuning (PEFT) methods for Large Language Models (LLMs) and discover how these techniques are revolutionizing machine learning by optimizing computational resources without compromising performance. Read more
Misnomer Alert: Dot Product and Inner Product are not the Same
Published:
Have you encountered cases where inner product and dot product are used synonymously? If you have, be alert that inner product and dot product are not same. Read more
Defeating The Size: Working with Large Tabular Data on AWS S3 using Snowpark
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We’ll explore the synergy between AWS S3, Snowpark, and Snowflake for efficient handling of large tabular data. By combining these tools, you can seamlessly process and analyze extensive datasets stored in AWS S3. Read more
Terraform a Scalable Comprehensive Sagemaker MultiModel Pipeline
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An end-to-end comprehensive scalable SageMaker pipeline for training, deploying and monitoring multiple models trained on different datasets to a single endpoint. Read more
Implementation and Empirical Analysis of Multi-Armed Bandit Problem
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Welcome to my latest blog post! Today, I am excited to share my recent exploration into the fascinating world of reinforcement learning, specifically focusing on the multi-armed bandit problem and its various solutions. As a foundation for my implementation, I closely followed the insightful book, Reinforcement Learning: An Introduction (second edition) by Richard S. Sutton and Andrew G. Barto. Read more
Critical Analysis: Why do tree-based models still outperform deep learning on typical tabular data?
Published:
It is an annotated criticism on the paper Why do tree-based models still outperform deep learning on typical tabular data?. A personal view on why it was accepted for publication at NEURIPS 2022 is discussed along with a brief description of the key contributions and the significance of the findings presented in the paper. Also, some of the key strengths and weaknesses with this paper, the approach taken by the authors, and the clarity and ease of understanding of the writing are discussed herewith. Read more
Finetuning XLS-R(Wav2Vec2) on OpenSLR Nepali ASR Dataset
Published:
Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in September 2020 by Alexei Baevski, Michael Auli, and Alex Conneau. Soon after the superior performance of Wav2Vec2 was demonstrated on one of the most popular English datasets for ASR, called LibriSpeech, Facebook AI presented a multi-lingual version of Wav2Vec2, called XLSR. XLSR stands for cross-lingual speech representations and refers to model’s ability to learn speech representations that are useful across multiple languages. Read more
Implementation of MetaGAN: An Adversarial Approach to Few-Shot Learning
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Humans can recognize objects from a few examples. Having seen a lot of animal images before, given very few images of novel animals, we can recognize them easily. But for deep learning models, we have to train them from scratch to learn a new task. Read more
portfolio
Voice Cloning using Transfer Learning from Speaker Verification to Multispeaker TTS
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Worked on implementing Text Independent Speaker Verification in order to condition the Multispeaker version of Tacotron to synthesize speech of text on the voice of the given person in the wild. Implemented sequence-to-sequence encoder-decoder model based on attention mechanism. Read more
Nepali Automatic Speech Recognition using Wav2Vec2 Model
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Worked on finetuning Wav2Vec2 model on OpenSLR’s Nepali ASR dataset. Preprocessed audio dataset with silence removal, voice activity detection, and noise augmentation. Read more
Scalable End to End Sagemaker MultiModel Pipeline
Published:
Worked on providing an end-to-end comprehensive scalable SageMaker pipeline for training and deploying multiple models trained on different datasets to a single endpoint. Read more
publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work. Read more
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work. Read more
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work. Read more
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
Paper Title Number 4
Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693. Read more
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3). http://academicpages.github.io/files/paper3.pdf
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown! Read more
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field. Read more
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post. Read more
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post. Read more