Reasoning presents a significant and challenging issue for Large Languag...
The field of protein folding research has been greatly advanced by deep
...
Federated weather forecasting is a promising collaborative learning fram...
Deep learning-based approaches, such as AlphaFold2 (AF2), have significa...
Information retrieval (IR) plays a crucial role in locating relevant
res...
In this work, we propose a simple method that applies a large language m...
Image-text retrieval (ITR) is a task to retrieve the relevant images/tex...
To tackle the global climate challenge, it urgently needs to develop a
c...
Long document retrieval aims to fetch query-relevant documents from a
la...
To improve the performance of the dual-encoder retriever, one effective
...
A common concern when a policymaker draws causal inferences from and mak...
Device Model Generalization (DMG) is a practical yet under-investigated
...
In large-scale retrieval, the lexicon-weighting paradigm, learning weigh...
Retrieval models based on dense representations in semantic space have b...
RNA structure determination and prediction can promote RNA-targeted drug...
This paper focuses on text data augmentation for few-shot NLP tasks. The...
A ranker plays an indispensable role in the de facto 'retrieval rera...
Large-scale retrieval is to recall relevant documents from a huge collec...
Generating new events given context with correlated ones plays a crucial...
Learning sentence embeddings in an unsupervised manner is fundamental in...
Influenced by the great success of deep learning via cloud computing and...
Event correlation reasoning infers whether a natural language paragraph
...
Sequential diagnosis prediction on the Electronic Health Record (EHR) ha...
Aspect-level sentiment classification (ALSC) aims at identifying the
sen...
Privacy protection is an ethical issue with broad concern in Artificial
...
Healthcare representation learning on the Electronic Health Record (EHR)...
Graph neural networks (GNN) have been successful in many fields, and der...
Accurate protein structure prediction from amino-acid sequences is criti...
Proteins structure prediction has long been a grand challenge over the p...
To leverage enormous unlabeled data on distributed edge devices, we form...
Many graph embedding approaches have been proposed for knowledge graph
c...
Electronic health records (EHRs) are longitudinal records of a patient's...
Federated learning enables collaboratively training machine learning mod...
Federated learning has received great attention for its capability to tr...
We improve both the open-set generalization and efficiency of link predi...
In this work, we aim at equipping pre-trained language models with struc...
Distantly supervised relation extraction intrinsically suffers from nois...
We consider the problem of conversational question answering over a
larg...
In longitudinal electronic health records (EHRs), the event records of a...
Many algorithms for Knowledge-Based Question Answering (KBQA) depend on
...
In this paper, we propose a self-attention mechanism, dubbed "fast
direc...
Recurrent neural networks (RNN), convolutional neural networks (CNN) and...
Many natural language processing tasks solely rely on sparse dependencie...
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are wide...