activity
20102024
most citedDesigning Neural Network Architectures using Reinforcement Learning

424 citations · 542 across the 22 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.DC20242 cited

Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix

Seungeun Oh, Sihun Baek, Jihong Park +5

In computer vision, the vision transformer (ViT) has increasingly superseded the convolutional neural network (CNN) for improved accuracy and robustness. However, ViT's large model…

cs.CV2024

NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticity

Akshat Dave, Tianyi Zhang, Aaron Young +3

Photoelasticity enables full-field stress analysis in transparent objects through stress-induced birefringence. Existing techniques are limited to 2D slices and require destructive…

cs.LG20241 cited

Data Measurements for Decentralized Data Markets

Charles Lu, Mohammad Mohammadi Amiri, Ramesh Raskar

Decentralized data markets can provide more equitable forms of data acquisition for machine learning. However, to realize practical marketplaces, efficient techniques for seller se…

cs.MA2024

Private Agent-Based Modeling

Ayush Chopra, Arnau Quera-Bofarull, Nurullah Giray-Kuru +2

The practical utility of agent-based models in decision-making relies on their capacity to accurately replicate populations while seamlessly integrating real-world data streams. Ye…

cs.CV2024

DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images

Zaid Tasneem, Akshat Dave, Abhishek Singh +4

Neural radiance fields (NeRFs) show potential for transforming images captured worldwide into immersive 3D visual experiences. However, most of this captured visual data remains si…

cs.LG20241 cited

CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models

Abhishek Singh, Gauri Gupta, Ritvik Kapila +5

Federated Learning (FL) enables collaborative optimization of machine learning models across decentralized data by aggregating model parameters. Our approach extends this concept b…