120 citations · 133 across the 5 of their papers we have counts for
4 papers · 1 filter
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Lu Jiang, Di Huang, Mason Liu +1
Performing controlled experiments on noisy data is essential in understanding deep learning across noise levels. Due to the lack of suitable datasets, previous research has only ex…
Graph Representation Ensemble Learning
Palash Goyal, Di Huang, Sujit Rokka Chhetri +3
Representation learning on graphs has been gaining attention due to its wide applicability in predicting missing links, and classifying and recommending nodes. Most embedding metho…
ArduCode: Predictive Framework for Automation Engineering
Arquimedes Canedo, Palash Goyal, Di Huang +2
Automation engineering is the task of integrating, via software, various sensors, actuators, and controls for automating a real-world process. Today, automation engineering is supp…
Benchmarks for Graph Embedding Evaluation
Palash Goyal, Di Huang, Ankita Goswami +3
Graph embedding is the task of representing nodes of a graph in a low-dimensional space and its applications for graph tasks have gained significant traction in academia and indust…