24 citations · 109 across the 19 of their papers we have counts for
7 papers · 1 filter
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance
Vishaal Udandarao, Ameya Prabhu, Adhiraj Ghosh +5
Web-crawled pretraining datasets underlie the impressive "zero-shot" evaluation performance of multimodal models, such as CLIP for classification/retrieval and Stable-Diffusion for…
Random Representations Outperform Online Continually Learned Representations
Ameya Prabhu, Shiven Sinha, Ponnurangam Kumaraguru +3
Continual learning has primarily focused on the issue of catastrophic forgetting and the associated stability-plasticity tradeoffs. However, little attention has been paid to the e…
Online Continual Learning Without the Storage Constraint
Ameya Prabhu, Zhipeng Cai, Puneet Dokania +3
Traditional online continual learning (OCL) research has primarily focused on mitigating catastrophic forgetting with fixed and limited storage allocation throughout an agent's lif…
Simple Unsupervised Multi-Object Tracking
Shyamgopal Karthik, Ameya Prabhu, Vineet Gandhi
Multi-object tracking has seen a lot of progress recently, albeit with substantial annotation costs for developing better and larger labeled datasets. In this work, we remove the n…
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory
Ameya Prabhu, Vishal Batchu, Rohit Gajawada +2
Binarization is an extreme network compression approach that provides large computational speedups along with energy and memory savings, albeit at significant accuracy costs. We in…
Distribution-Aware Binarization of Neural Networks for Sketch Recognition
Ameya Prabhu, Vishal Batchu, Sri Aurobindo Munagala +2
Deep neural networks are highly effective at a range of computational tasks. However, they tend to be computationally expensive, especially in vision-related problems, and also hav…
Deep Expander Networks: Efficient Deep Networks from Graph Theory
Ameya Prabhu, Girish Varma, Anoop Namboodiri
Efficient CNN designs like ResNets and DenseNet were proposed to improve accuracy vs efficiency trade-offs. They essentially increased the connectivity, allowing efficient informat…