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Found 19 papers, 7 papers with code
Intention Reasoning Network for Multi-Domain End-to-end Task-Oriented Dialogue
Based on such a mechanism, we further propose an intention reasoning network (IR-Net), which consists of joint and multi-hop reasoning, to obtain intention-aware representations of conceptual tokens that can be used to capture the concept shifts involved in task-oriented conversations, so as to effectively identify user’s intention and generate more accurate responses.
UniTranSeR: A Unified Transformer Semantic Representation Framework for Multimodal Task-Oriented Dialog System
Specifically, we first embed the multimodal features into a unified Transformer semantic space to prompt inter-modal interactions, and then devise a feature alignment and intention reasoning (FAIR) layer to perform cross-modal entity alignment and fine-grained key-value reasoning, so as to effectively identify user’s intention for generating more accurate responses.
GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification
Accurate fact verification depends on performing fine-grained reasoning over crucial entities by capturing their latent logical relations hidden in multiple evidence clues, which is generally lacking in existing fact verification models.
Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models
In this paper, we introduce a versatile adaptation approach that can effectively work under all three settings.
DiffSpeaker: Speech-Driven 3D Facial Animation with Diffusion Transformer
We suspect this is due to a shortage of paired audio-4D data, which is crucial for the Transformer to effectively perform as a denoiser within the Diffusion framework.
EmoSpeaker: One-shot Fine-grained Emotion-Controlled Talking Face Generation
Additionally, we propose an emotion intensity control method using a fine-grained emotion matrix.
Generative Multi-Modal Knowledge Retrieval with Large Language Models
Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications.
LMD: Faster Image Reconstruction with Latent Masking Diffusion
Then, we combine the advantages of MAEs and DPMs to design a progressive masking diffusion model, which gradually increases the masking proportion by three different schedulers and reconstructs the latent features from simple to difficult, without sequentially performing denoising diffusion as in DPMs or using fixed high masking ratio as in MAEs, so as to alleviate the high training time-consumption predicament.
AdapEdit: Spatio-Temporal Guided Adaptive Editing Algorithm for Text-Based Continuity-Sensitive Image Editing
With the great success of text-conditioned diffusion models in creative text-to-image generation, various text-driven image editing approaches have attracted the attentions of many researchers.
Comparative Study on Semi-supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System
This study systematically compares semi-supervised learning methods applied for anomaly detection in hydraulic condition monitoring systems.
OTAvatar: One-shot Talking Face Avatar with Controllable Tri-plane Rendering
In this paper, we propose One-shot Talking face Avatar (OTAvatar), which constructs face avatars by a generalized controllable tri-plane rendering solution so that each personalized avatar can be constructed from only one portrait as the reference.
Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network
To enhance the security of in-vehicle networks and promote the research in this area, based upon a large scale of CAN network traffic data with the extracted valuable features, this study comprehensively compared fully-supervised machine learning with semi-supervised machine learning methods for CAN message anomaly detection.
Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation
To reach this goal, we construct the nearest neighborhood for every target data and take it as the fundamental clustering unit by building our objective on the geometry.
Enriching Medcial Terminology Knowledge Bases via Pre-trained Language Model and Graph Convolutional Network
Afterwards, both semantic and structure embeddings are combined to measure the relevancy between the terminology and the entity.
NE-LP: Normalized Entropy and Loss Prediction based Sampling for Active Learning in Chinese Word Segmentation on EHRs
Meanwhile, to minimize the computational cost of learning, we propose a joint model including a word segmenter and a loss prediction model.
Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text
Entity and relation extraction is the necessary step in structuring medical text.
CBOWRA: A Representation Learning Approach for Medication Anomaly Detection
Electronic health record is an important source for clinical researches and applications, and errors inevitably occur in the data, which could lead to severe damages to both patients and hospital services.
Question Answering based Clinical Text Structuring Using Pre-trained Language Model
Clinical text structuring is a critical and fundamental task for clinical research.
Estimate the Warfarin Dose by Ensemble of Machine Learning Algorithms
Here, we present novel algorithms using stacked generalization frameworks to estimate the warfarin dose, within which different types of machine learning algorithms function together through a meta-machine learning model to maximize the prediction accuracy.
