3 citations · 6 across the 19 of their papers we have counts for
9 papers · 1 filter
Sequential Compression Layers for Efficient Federated Learning in Foundational Models
Navyansh Mahla, Sunny Gupta, Amit Sethi
Federated Learning (FL) has gained popularity for fine-tuning large language models (LLMs) across multiple nodes, each with its own private data. While LoRA has been widely adopted…
FLD+: Data-efficient Evaluation Metric for Generative Models
Pranav Jeevan, Neeraj Nixon, Amit Sethi
We introduce a new metric to assess the quality of generated images that is more reliable, data-efficient, compute-efficient, and adaptable to new domains than the previous metrics…
WaveMixSR-V2: Enhancing Super-resolution with Higher Efficiency
Pranav Jeevan, Neeraj Nixon, Amit Sethi
Recent advancements in single image super-resolution have been predominantly driven by token mixers and transformer architectures. WaveMixSR utilized the WaveMix architecture, empl…
Normalizing Flow-Based Metric for Image Generation
Pranav Jeevan, Neeraj Nixon, Amit Sethi
We propose two new evaluation metrics to assess realness of generated images based on normalizing flows: a simpler and efficient flow-based likelihood distance (FLD) and a more exa…
FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator
Sunny Gupta, Nikita Jangid, Amit Sethi
Federated Learning (FL) facilitates data privacy by enabling collaborative in-situ training across decentralized clients. Despite its inherent advantages, FL faces significant chal…
FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch
Sunny Gupta, Mohit Jindal, Pankhi Kashyap +2
Federated learning faces a critical challenge in balancing communication efficiency with rapid convergence, especially for second-order methods. While Newton-type algorithms achiev…
EDSNet: Efficient-DSNet for Video Summarization
Ashish Prasad, Pranav Jeevan, Amit Sethi
Current video summarization methods largely rely on transformer-based architectures, which, due to their quadratic complexity, require substantial computational resources. In this…
CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging
Sunny Gupta, Amit Sethi
Federated Learning (FL) offers a privacy-preserving approach to train models on decentralized data. Its potential in healthcare is significant, but challenges arise due to cross-cl…
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision
Pranav Jeevan, Amit Sethi
In contemporary computer vision applications, particularly image classification, architectural backbones pre-trained on large datasets like ImageNet are commonly employed as featur…