Search Results for author:
Found 5 papers, 4 papers with code
TOKEN is a MASK: Few-shot Named Entity Recognition with Pre-trained Language Models
Transferring knowledge from one domain to another is of practical importance for many tasks in natural language processing, especially when the amount of available data in the target domain is limited.
Preventing Author Profiling through Zero-Shot Multilingual Back-Translation
Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e. g. their gender or ethnicity.
SuperCoder: Program Learning Under Noisy Conditions From Superposition of States
We propose a new method of program learning in a Domain Specific Language (DSL) which is based on gradient descent with no direct search.
Privacy Guarantees for De-identifying Text Transformations
Machine Learning approaches to Natural Language Processing tasks benefit from a comprehensive collection of real-life user data.
On the effect of normalization layers on Differentially Private training of deep Neural networks
Differentially private stochastic gradient descent (DPSGD) is a variation of stochastic gradient descent based on the Differential Privacy (DP) paradigm, which can mitigate privacy threats that arise from the presence of sensitive information in training data.
