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Found 11 papers, 2 papers with code
Recovering from Privacy-Preserving Masking with Large Language Models
To effectively perform adaptation, textual data of users is typically stored on servers or their local devices, where downstream natural language processing (NLP) models can be directly trained using such in-domain data.
Language Agnostic Data-Driven Inverse Text Normalization
With the emergence of automatic speech recognition (ASR) models, converting the spoken form text (from ASR) to the written form is in urgent need.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction
In this work, we explore the method of employing contrastive learning to improve the text representation from the BERT model for relation extraction.
Gradient Regularized Contrastive Learning for Continual Domain Adaptation
To better understand this issue, we study the problem of continual domain adaptation, where the model is presented with a labelled source domain and a sequence of unlabelled target domains.
Modal Uncertainty Estimation via Discrete Latent Representations
Many important problems in the real world don't have unique solutions.
Investigation of BERT Model on Biomedical Relation Extraction Based on Revised Fine-tuning Mechanism
In this paper, we will investigate the method of utilizing the entire layer in the fine-tuning process of BERT model.
Contrastive Visual-Linguistic Pretraining
We evaluate CVLP on several down-stream tasks, including VQA, GQA and NLVR2 to validate the superiority of contrastive learning on multi-modality representation learning.
Gradient Regularized Contrastive Learning for Continual Domain Adaptation
At the core of our method, gradient regularization plays two key roles: (1) enforces the gradient of contrastive loss not to increase the supervised training loss on the source domain, which maintains the discriminative power of learned features; (2) regularizes the gradient update on the new domain not to increase the classification loss on the old target domains, which enables the model to adapt to an in-coming target domain while preserving the performance of previously observed domains.
Adversarial Learning for Supervised and Semi-supervised Relation Extraction in Biomedical Literature
Adversarial training is a technique of improving model performance by involving adversarial examples in the training process.
Adapting Object Detectors with Conditional Domain Normalization
Then this domain-vector is used to encode the features from another domain through a conditional normalization, resulting in different domains' features carrying the same domain attribute.
Ranked #1 on
Unsupervised Domain Adaptation
on SIM10K to BDD100K
Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks
Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics.
Ranked #1 on
Blood pressure estimation
on MIMIC-III
