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Found 26 papers, 7 papers with code
KSAM: Infusing Multi-Source Knowledge into Dialogue Generation via Knowledge Source Aware Multi-Head Decoding
Knowledge-enhanced methods have bridged the gap between human beings and machines in generating dialogue responses.
Section-Aware Commonsense Knowledge-Grounded Dialogue Generation with Pre-trained Language Model
However, a dialogue is always aligned to a lot of retrieved fact candidates; as a result, the linearized text is always lengthy and then significantly increases the burden of using PLMs.
More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge
Despite achieving remarkable performance, previous knowledge-enhanced works usually only use a single-source homogeneous knowledge base of limited knowledge coverage.
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis
Specifically, on the client side, we condense the knowledge of each client into a small dataset and further enhance the condensation procedure with latent distribution constraints, facilitating the effective capture of high-quality knowledge.
Towards Federated Graph Learning in One-shot Communication
Specifically, for effective graph learning in one communication round, our method estimates and aggregates class-wise feature distribution statistics to construct a global pseudo-graph on the server, facilitating the training of a global graph model.
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection
Most of the existing methods assume the normal sample data clusters around a single central prototype while the real data may consist of multiple categories or subgroups.
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation
In medical image segmentation, personalized cross-silo federated learning (FL) is becoming popular for utilizing varied data across healthcare settings to overcome data scarcity and privacy concerns.
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis
In the evolving application of medical artificial intelligence, federated learning is notable for its ability to protect training data privacy.
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis
Federated learning is widely used in medical applications for training global models without needing local data access.
Large Language Model with Graph Convolution for Recommendation
To adapt text-based LLMs with structured graphs, We use the LLM as an aggregator in graph processing, allowing it to understand graph-based information step by step.
TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processing
Representation learning frameworks in unlabeled time series have been proposed for medical signal processing.
Bridging the Information Gap Between Domain-Specific Model and General LLM for Personalized Recommendation
Nevertheless, these methods encounter the certain issue that information such as community behavior pattern in RS domain is challenging to express in natural language, which limits the capability of LLMs to surpass state-of-the-art domain-specific models.
SHISRCNet: Super-resolution And Classification Network For Low-resolution Breast Cancer Histopathology Image
CF module extracts and fuses the multi-scale features of SR images for classification.
Histopathological Image Classification
Image Classification
+1
NCL: Textual Backdoor Defense Using Noise-augmented Contrastive Learning
At present, backdoor attacks attract attention as they do great harm to deep learning models.
Apple of Sodom: Hidden Backdoors in Superior Sentence Embeddings via Contrastive Learning
This paper finds that contrastive learning can produce superior sentence embeddings for pre-trained models but is also vulnerable to backdoor attacks.
TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene
In our experiments, the performance gain brought by GridMask is stronger than spectrum augmentation in ASCs.
Kallima: A Clean-label Framework for Textual Backdoor Attacks
Although Deep Neural Network (DNN) has led to unprecedented progress in various natural language processing (NLP) tasks, research shows that deep models are extremely vulnerable to backdoor attacks.
Revisiting Graph based Social Recommendation: A Distillation Enhanced Social Graph Network
We train a model that integrates information from the user-item interaction graph and the user-user social graph and train two auxiliary models that only use one of the above graphs respectively.
A Vertical Federated Learning Framework for Horizontally Partitioned Labels
To address these challenges, we propose a novel vertical federated learning framework named Cascade Vertical Federated Learning (CVFL) to fully utilize all horizontally partitioned labels to train neural networks with privacy-preservation.
Relation-Aware Neighborhood Matching Model for Entity Alignment
Besides comparing neighbor nodes when matching neighborhood, we also try to explore useful information from the connected relations.
Improving Knowledge-Aware Dialogue Response Generation by Using Human-Written Prototype Dialogues
Given a query, our approach first retrieves a set of prototype dialogues that are relevant to the query.
Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness
We collect and build a large-scale Chinese dataset aligned with the commonsense knowledge for dialogue generation.
BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements
In this paper, we perform a systematic investigation of backdoor attack on NLP models, and propose BadNL, a general NLP backdoor attack framework including novel attack methods.
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning.
Ranked #3 on
Link Prediction
on FB15k
(MR metric)
HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation
Recent years have witnessed a surge of interest on response generation for neural conversation systems.
An influence-based fast preceding questionnaire model for elderly assessments
The practice in an elderly-care company shows that the FPQM can reduce the number of attributes by 90. 56% with a prediction accuracy of 98. 39%.
