4 citations · 6 across the 6 of their papers we have counts for
17 papers
SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification
Benjamin Feuer, Jiawei Xu, Niv Cohen +3
Data curation is the problem of how to collect and organize samples into a dataset that supports efficient learning. Despite the centrality of the task, little work has been devote…
FlowBench: A Large Scale Benchmark for Flow Simulation over Complex Geometries
Ronak Tali, Ali Rabeh, Cheng-Hau Yang +10
Simulating fluid flow around arbitrary shapes is key to solving various engineering problems. However, simulating flow physics across complex geometries remains numerically challen…
DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models
Nastaran Saadati, Minh Pham, Nasla Saleem +5
Recent advances in decentralized deep learning algorithms have demonstrated cutting-edge performance on various tasks with large pre-trained models. However, a pivotal prerequisite…
Mitigating the Impact of Attribute Editing on Face Recognition
Sudipta Banerjee, Sai Pranaswi Mullangi, Shruti Wagle +2
Through a large-scale study over diverse face images, we show that facial attribute editing using modern generative AI models can severely degrade automated face recognition system…
Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks
Benjamin Feuer, Chinmay Hegde, Niv Cohen
Tabular classification has traditionally relied on supervised algorithms, which estimate the parameters of a prediction model using its training data. Recently, Prior-Data Fitted N…
Exploring Dataset-Scale Indicators of Data Quality
Benjamin Feuer, Chinmay Hegde
Modern computer vision foundation models are trained on massive amounts of data, incurring large economic and environmental costs. Recent research has suggested that improving data…