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Found 10 papers, 1 papers with code
Partial Federated Learning
Federated Learning (FL) is a popular algorithm to train machine learning models on user data constrained to edge devices (for example, mobile phones) due to privacy concerns.
JAB: Joint Adversarial Prompting and Belief Augmentation
With the recent surge of language models in different applications, attention to safety and robustness of these models has gained significant importance.
FedMultimodal: A Benchmark For Multimodal Federated Learning
In order to facilitate the research in multimodal FL, we introduce FedMultimodal, the first FL benchmark for multimodal learning covering five representative multimodal applications from ten commonly used datasets with a total of eight unique modalities.
Federated Learning with Noisy User Feedback
Federated learning (FL) has recently emerged as a method for training ML models on edge devices using sensitive user data and is seen as a way to mitigate concerns over data privacy.
Towards Realistic Single-Task Continuous Learning Research for NER
There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications.
Sentence level estimation of psycholinguistic norms using joint multidimensional annotations
We then use this parameter at sentence level to estimate the norms.
Joint Multi-Dimensional Model for Global and Time-Series Annotations
In this work we address this by proposing a generative model for multi-dimensional annotation fusion, which models the dimensions jointly leading to more accurate ground truth estimates.
A system for the 2019 Sentiment, Emotion and Cognitive State Task of DARPAs LORELEI project
In this work, we describe our submission for the 2019 Sentiment, Emotion and Cognitive state (SEC) pilot task of the LORELEI project.
Linguistic analysis of differences in portrayal of movie characters
We examine differences in portrayal of characters in movies using psycholinguistic and graph theoretic measures computed directly from screenplays.
