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Found 7 papers, 3 papers with code
Confidence Is All You Need for MI Attacks
In this attack, adversaries aim to determine whether a particular point was used during the training of a target model.
Classification of COVID-19 Patients with their Severity Level from Chest CT Scans using Transfer Learning
Result and Conclusion: In our study, we have combined well-known strategies for pre-processing, feature extraction, and classification which brings us to a remarkable success rate of disease and its severity recognition with an accuracy of 96. 05% (97. 7% for Non-Severe COVID-19 images and 93% for Severe COVID-19 images).
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning
A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language.
A Data-Centric Framework for Composable NLP Workflows
Empirical natural language processing (NLP) systems in application domains (e. g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization.
Learning to Deceive with Attention-Based Explanations
Attention mechanisms are ubiquitous components in neural architectures applied to natural language processing.
AmazonQA: A Review-Based Question Answering Task
Observing that many questions can be answered based upon the available product reviews, we propose the task of review-based QA.
Measuring Density and Similarity of Task Relevant Information in Neural Representations
Neural models achieve state-of-the-art performance due to their ability to extract salient features useful to downstream tasks.
