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Found 7 papers, 2 papers with code
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.
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.
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).
Efficient Text-driven Motion Generation via Latent Consistency Training
Motion diffusion models have recently proven successful for text-driven human motion generation.
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.
