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Found 6 papers, 3 papers with code
The Effect of Pretraining on Extractive Summarization for Scientific Documents
Large pretrained models have seen enormous success in extractive summarization tasks.
Multi-Row, Multi-Span Distant Supervision For Table+Text Question
Question answering (QA) over tables and linked text, also called TextTableQA, has witnessed significant research in recent years, as tables are often found embedded in documents along with related text.
Stateful Detection of Model Extraction Attacks
Machine-Learning-as-a-Service providers expose machine learning (ML) models through application programming interfaces (APIs) to developers.
A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection
Tapestry combines ideas from compressed sensing and combinatorial group testing with a novel noise model for RT-PCR used for generation of synthetic data.
ACTIVETHIEF: Model Extraction Using Active Learning and Unannotated Public Data
We demonstrate that (1) it is possible to use ACTIVETHIEF to extract deep classifiers trained on a variety of datasets from image and text domains, while querying the model with as few as 10-30% of samples from public datasets, (2) the resulting model exhibits a higher transferability success rate of adversarial examples than prior work, and (3) the attack evades detection by the state-of-the-art model extraction detection method, PRADA.
A framework for the extraction of Deep Neural Networks by leveraging public data
Machine learning models trained on confidential datasets are increasingly being deployed for profit.
