Large pretrained models have seen enormous success in extractive summarization tasks.
This leads to a noisy multi-instance training regime involving not only rows of the table, but also spans of linked text.
Ranked #1 on Question Answering on HybridQA
Machine-Learning-as-a-Service providers expose machine learning (ML) models through application programming interfaces (APIs) to developers.
1 code implementation • 16 May 2020 • Sabyasachi Ghosh, Rishi Agarwal, Mohammad Ali Rehan, Shreya Pathak, Pratyush Agrawal, Yash Gupta, Sarthak Consul, Nimay Gupta, Ritika, Ritesh Goenka, Ajit Rajwade, Manoj Gopalkrishnan
Tapestry combines ideas from compressed sensing and combinatorial group testing with a novel noise model for RT-PCR used for generation of synthetic 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.
Machine learning models trained on confidential datasets are increasingly being deployed for profit.