Search Results for author:
Found 7 papers, 5 papers with code
Towards Robustness to Label Noise in Text Classification via Noise Modeling
Large datasets in NLP suffer from noisy labels, due to erroneous automatic and human annotation procedures.
Backdoors in Neural Models of Source Code
Deep neural networks are vulnerable to a range of adversaries.
Advances in Quantum Deep Learning: An Overview
The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing.
BAE: BERT-based Adversarial Examples for Text Classification
Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model.
Semantic Robustness of Models of Source Code
Deep neural networks are vulnerable to adversarial examples - small input perturbations that result in incorrect predictions.
Synthesizing Action Sequences for Modifying Model Decisions
When a model makes a consequential decision, e. g., denying someone a loan, it needs to additionally generate actionable, realistic feedback on what the person can do to favorably change the decision.
Fast GPU-Enabled Color Normalization for Digital Pathology
Normalizing unwanted color variations due to differences in staining processes and scanner responses has been shown to aid machine learning in computational pathology.
