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Found 7 papers, 0 papers with code
Meta-Learning for Few-Shot Named Entity Recognition
Meta-learning has recently been proposed to learn models and algorithms that can generalize from a handful of examples.
Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App
MindScape aims to study the benefits of integrating time series behavioral patterns (e. g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being.
Towards Multi-Objective Statistically Fair Federated Learning
With this goal in mind, we conduct studies to show that FL is able to satisfy different fairness metrics under different data regimes consisting of different types of clients.
Graph-Based Semi-Supervised Learning for Natural Language Understanding
Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data.
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification
In particular, we show that (a) upsampling in latent space is a competitive baseline for feature space augmentation (b) adding the difference between two examples to a new example is a simple yet effective data augmentation method.
Multimodal Sparse Coding for Event Detection
Unsupervised feature learning methods have proven effective for classification tasks based on a single modality.
