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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
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Posts
What are Graph Compilers? (LLVM, MLIR)
Published:
Graph compilers Introduction
What are Transformer models?
Published:
Transformer Model Introduction
What are AI accelerators?
Published:
AI Accelerators Introduction
Deep Dive into AI Infrastructure Stack
Published:
AI System Introduction
AI Infrastructure stack can be broadly categorized in 4 major layers: 
portfolio
Action Spotting in Soccer broadcast Untrimmed videos
Published:
The task of spotting consists of finding the anchor time that identifies an event in a video and then classify the event to appropriate class. 
Deep Learning models Compression Techniques - Pruning, Knowledge Distillation
Published:
We aim to explore different model compression techniques in this project. 
Dependency Parsing in Natural Language Processing
Published:
In this project, we plan to implement the transition based and graph based dependency parser using neural network approaches for feature representation to extract the syntactic interpretations. 
Variational Autoencoder and Generative Adversarial Network
Published:
In this project we aim to implement variational autoencoder and generative adversarial network on MNIST dataset.
publications
Intelligent Jobs Scheduling in palladium Cloud using Machine learning
Published in Cadence TeqFest 2019, 2019
In this poster we put forward a job scheduling algorithm with the goal of the minimum completion time, maximum load balancing degree using an improved deferential algorithm in cloud computing. Our proposed solution is to use machine learning (ML) techniques, especially supervised learning techniques to learn and predict the compile time of a design at the parser stage of the palladium frontend compiler.
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A Comparative assessment of Data Mining Algorithms to predict fraudulent firms
Published in IEEE International Conference on Cloud Computing, Data Science & Engineering(Confluence), 2019
In this paper, we compare the classification models of Data mining that can predict the fraudulent firm based on current and past risk factors. Further, we apply the ensemble techniques to improve the models and finally compare the models based on accuracy and compute complexity. The dataset collected is multivariate having 18 attributes that have been considered as risk factors.
Recommended citation: H. Monish and A. C. Pandey, "A Comparative Assessment of Data Mining Algorithms to Predict Fraudulent Firms," 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2020, pp. 117-122, doi: 10.1109/Confluence47617.2020.9057968. https://ieeexplore.ieee.org/document/9057968