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Found 7 papers, 3 papers with code
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks
In particular, we develop KEMLP by integrating a diverse set of weak auxiliary models based on their logical relationships to the main DNN model that performs the target task.
A Data Quality-Driven View of MLOps
Developing machine learning models can be seen as a process similar to the one established for traditional software development.
Online Active Model Selection for Pre-trained Classifiers
Given $k$ pre-trained classifiers and a stream of unlabeled data examples, how can we actively decide when to query a label so that we can distinguish the best model from the rest while making a small number of queries?
FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data
Then we instantiate this search strategy by optimizing both a dedicated graph neural network (GNN) and the adjacency tensor associated with the defined feature graph.
Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions
Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data.
DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition
Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.
Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications
Modern scientific instruments produce vast amounts of data, which can overwhelm the processing ability of computer systems.
