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Works68 from public data
- DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding794
A novel attention mechanism in which the attention between elements from input sequence(s) is directional and multi-dimensional (i.e., feature-wise) and a light-weight neural net is proposed, based solely on the proposed attention without any RNN/CNN structure, which outperforms complicated RNN models on both prediction quality and time efficiency.
- Multi-center federated learning: clients clustering for better personalization399
A novel multi-center aggregation mechanism to cluster clients using their models’ parameters is proposed that learns multiple global models from data as the cluster centers, and simultaneously derives the optimal matching between users and centers and outperforms several popular baseline methods.
- Federated unsupervised representation learning174
The federated contrastive averaging with dictionary and alignment (FedCA) algorithm, composed of two key modules: a dictionary module to aggregate the representations of samples from each client which can be shared with all clients for consistency of representation space, is adopted for local model training.
- Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling148
An RNN/CNN-free sentence-encoding model, "reinforced self-attention network (ReSAN)", solely based on ReSA is proposed, which achieves state-of-the-art performance on both the Stanford Natural Language Inference and the Sentences Involving Compositional Knowledge datasets.
- Thread of Thought Unraveling Chaotic Contexts93
This work introduces the "Thread of Thought" strategy, which draws inspiration from human cognitive processes and systematically segments and analyzes extended contexts while adeptly selecting pertinent information within the chaotic context.
- Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction90
A brand-new light-weight neural framework to address the distantly supervised relation extraction problem and alleviate the defects in previous selective attention framework is proposed, which achieves a new state-of-the-art performance in terms of both AUC and top-n precision metrics.
- Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base85
This work proposes an innovative multi-task learning framework where a pointer-equipped semantic parsing model is designed to resolve coreference in conversations, and naturally empower joint learning with a novel type-aware entity detection model.
- Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health78
This chapter will discuss how federated learning can enable the development of an open health ecosystem with the support of AI.
- Re-Reading Improves Reasoning in Large Language Models74
RE2 demonstrates strong generality and compatibility with most thought-eliciting prompting methods, including CoT, and its adaptability is revealed, showing how it can be effectively integrated with different LLMs, thought-eliciting prompting, and ensemble strategies.
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- Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning74
This work proposes an entity masking scheme that exploits relational knowledge underlying the text, and uses knowledge graphs to obtain distractors for the masked entities, and proposes a novel distractor-suppressed ranking objective which is optimized jointly with masked language model.
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- EventBERT: A Pre-Trained Model for Event Correlation Reasoning69
EventBERT is proposed, a pre-trained model to encapsulate eventuality knowledge from unlabeled text and outperforms existing pre- trained models by a large margin, e.g., 6.5 ∼ 23%, in zero-shot learning of these tasks.
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- Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning57
A knowledge model to learn the inferential relations between events from the whole eventuality KG and a neural script adapter to extend the knowledge model for inferring the associated relations be-tween an event chain and a subsequent event candidate is presented.
- Large Language Models are Strong Zero-Shot Retriever54
The Language language model as Retriever (LameR), is built upon no other neural models but an LLM, while breaking brute-force combinations of retrievers with LLMs and lifting the performance of zero-shot retrieval to be very competitive on benchmark datasets.
- BianCang: A Traditional Chinese Medicine Large Language Model50
BianCang is proposed, a TCM-specific LLM, using a two-stage training process that first injects domainspecific knowledge and then aligns it through targeted stimulation to enhance diagnostic and differentiation capabilities.
- PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings50
A novel Peer-Contrastive Learning (PCL) with diverse augmentations is proposed, which performs peer-positive contrast as well as peer-network cooperation, which offers an inherent anti-bias ability and an effective way to learn from diverse augmentation.
- LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval48
A brand-new pre-training framework, lexicon-bottlenecked masked autoencoder (LexMAE), to learn importance-aware lexicon representations, which shows state-of-the-art zero-shot transfer capability on BEIR benchmark with 12 datasets.
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- UnifieR: A Unified Retriever for Large-Scale Retrieval37
This work proposes a new learning framework, Unifier, which unifies dense-vector and lexicon-based retrieval in one model with a dual-representing capability, and experiments on passage retrieval benchmarks verify its effectiveness in both paradigms.
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- LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Sparse Retrieval36
- Sequential Diagnosis Prediction with Transformer and Ontological Representation36
An end-to-end robust transformer-based model called SETOR is proposed, which exploits neural ordinary differential equation to handle both irregular intervals between a patient’s visits with admitted timestamps and length of stay in each visit, and to alleviate the limitation of insufficient data by integrating medical ontology.
- Temporal Self-Attention Network for Medical Concept Embedding35
This work proposes a novel attention mechanism which captures the contextual information and temporal relationships between medical concepts and is the first to exploit temporal self-attentive relations between medical events.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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