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Found 6 papers, 2 papers with code
Semi-Supervised Language Models for Identification of Personal Health Experiential from Twitter Data: A Case for Medication Effects
We investigated two semi-supervised learning methods, with different mixes of labeled and unlabeled data in the training set, to understand the impact on classification performance.
Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating
Post-market surveillance, the practice of monitoring the safe use of pharmaceutical drugs is an important part of pharmacovigilance.
Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework
Variational Autoencoder (VAE) is an effective framework to model the interdependency for non-autoregressive neural machine translation (NAT).
CLIPose: Category-Level Object Pose Estimation with Pre-trained Vision-Language Knowledge
Inspired by this motivation, we propose CLIPose, a novel 6D pose framework that employs the pre-trained vision-language model to develop better learning of object category information, which can fully leverage abundant semantic knowledge in image and text modalities.
Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation Learning
As the most essential property in a video, motion information is critical to a robust and generalized video representation.
Secrets of RLHF in Large Language Models Part I: PPO
Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model.
