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Found 11 papers, 2 papers with code
Enhancing Cross-lingual Sentence Embedding for Low-resource Languages with Word Alignment
The field of cross-lingual sentence embeddings has recently experienced significant advancements, but research concerning low-resource languages has lagged due to the scarcity of parallel corpora.
Generating, Reconstructing, and Representing Discrete and Continuous Data: Generalized Diffusion with Learnable Encoding-Decoding
The vast applications of deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations -- across various data types, such as discrete text/protein sequences and continuous images.
Reinforcement Learning from Bagged Reward: A Transformer-based Approach for Instance-Level Reward Redistribution
Instead, the learner only obtains rewards at the ends of bags, where a bag is defined as a partial sequence of a complete trajectory.
TLMCM Network for Medical Image Hierarchical Multi-Label Classification
Medical Image Hierarchical Multi-Label Classification (MI-HMC) is of paramount importance in modern healthcare, presenting two significant challenges: data imbalance and \textit{hierarchy constraint}.
Towards reporting bias in visual-language datasets: bimodal augmentation by decoupling object-attribute association
In this paper, we focus on the wide existence of reporting bias in visual-language datasets, embodied as the object-attribute association, which can subsequentially degrade models trained on them.
Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding
Learning multi-lingual sentence embeddings is a fundamental task in natural language processing.
WSPAlign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction
Most existing word alignment methods rely on manual alignment datasets or parallel corpora, which limits their usefulness.
A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients
We develop a deep learning approach to predicting a set of ventilator parameters for a mechanically ventilated septic patient using a long and short term memory (LSTM) recurrent neural network (RNN) model.
PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings
A straightforward solution is resorting to more diverse positives from a multi-augmenting strategy, while an open question remains about how to unsupervisedly learn from the diverse positives but with uneven augmenting qualities in the text field.
Taking Notes on the Fly Helps Language Pre-Training
In this paper, we focus on improving the efficiency of language pre-training methods through providing better data utilization.
Taking Notes on the Fly Helps BERT Pre-training
In this paper, we focus on improving the efficiency of language pre-training methods through providing better data utilization.
