3 citations · 3 across the 6 of their papers we have counts for
6 papers
Pooling Attention: Evaluating Pretrained Transformer Embeddings for Deception Classification
Sumit Mamtani, Abhijeet Bhure
This paper investigates fake news detection as a downstream evaluation of Transformer representations, benchmarking encoder-only and decoder-only pre-trained models (BERT, GPT-2, T…
Enhancing Transformer-Based Vision Models: Addressing Feature Map Anomalies Through Novel Optimization Strategies
Sumit Mamtani
Vision Transformers (ViTs) have demonstrated superior performance across a wide range of computer vision tasks. However, structured noise artifacts in their feature maps hinder dow…
Token-free Models for Sarcasm Detection
Sumit Mamtani, Maitreya Sonawane, Kanika Agarwal +1
Tokenization is a foundational step in most natural language processing (NLP) pipelines, yet it introduces challenges such as vocabulary mismatch and out-of-vocabulary issues. Rece…
Fine-Grained Classification: Connecting Metadata via Cross-Contrastive Pre-Training
Sumit Mamtani, Yash Thesia
Fine-grained visual classification aims to recognize objects belonging to many subordinate categories of a supercategory, where appearance alone often fails to distinguish highly s…
Axial-UNet: A Neural Weather Model for Precipitation Nowcasting
Sumit Mamtani, Maitreya Sonawane
Accurately predicting short-term precipitation is critical for weather-sensitive applications such as disaster management, aviation, and urban planning. Traditional numerical weath…
Deployment of ML Models using Kubeflow on Different Cloud Providers
Aditya Pandey, Maitreya Sonawane, Sumit Mamtani
This project aims to explore the process of deploying Machine learning models on Kubernetes using an open-source tool called Kubeflow [1] - an end-to-end ML Stack orchestration too…