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Found 22 papers, 8 papers with code
Implicit Neural Differential Model for Spatiotemporal Dynamics
In this work, we introduce Im-PiNDiff, a novel implicit physics-integrated neural differentiable solver for stable and accurate modeling of spatiotemporal dynamics.
AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows
Constructing patient-specific vascular models remains labor-intensive, error-prone, and time-consuming, limiting their clinical applications.
Hierarchical LoG Bayesian Neural Network for Enhanced Aorta Segmentation
Accurate segmentation of the aorta and its associated arch branches is crucial for diagnosing aortic diseases.
Personalized Clustering via Targeted Representation Learning
In this paper, we propose a personalized clustering method that explicitly performs targeted representation learning by interacting with users via modicum task information (e. g., $\textit{must-link}$ or $\textit{cannot-link}$ pairs) to guide the clustering direction.
CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent Diffusion Models with Neural Fields
Eddy-resolving turbulence simulations require stochastic inflow conditions that accurately replicate the complex, multi-scale structures of turbulence.
Semi-Supervised Learning via Weight-aware Distillation under Class Distribution Mismatch
Specifically, WAD captures adaptive weights and high-quality pseudo labels to target instances by exploring point mutual information (PMI) in representation space to maximize the role of unlabeled data and filter unknown categories.
PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN
Beyond topical relevance, passage ranking for open-domain factoid question answering also requires a passage to contain an answer (answerability).
Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics
An efficient supervised learning solution is proposed to map the geometric inputs to the hemodynamics predictions in latent spaces.
FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging
With the aim of obtaining an outstanding performance with less time cost, we propose a novel model in a vertically federated setting termed as Federated Gradient Boosting Forest (FedGBF).
Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences.
Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals
The final response is selected according to the predicted knowledge, the goal to achieve, and the context.
Inductive Topic Variational Graph Auto-Encoder for Text Classification
T-VGAE inherits the interpretability of the topic model and the efficient information propagation mechanism of VGAE.
Emotion Eliciting Machine: Emotion Eliciting Conversation Generation based on Dual Generator
Then we propose a dual encoder-decoder structure to model the generation of responses in both positive and negative side based on the changes of the user's emotion status in the conversation.
BERT4SO: Neural Sentence Ordering by Fine-tuning BERT
Sentence ordering aims to arrange the sentences of a given text in the correct order.
Content Selection Network for Document-grounded Retrieval-based Chatbots
It is thus crucial to select the part of document content relevant to the current conversation context.
Contrastive Coding for Active Learning Under Class Distribution Mismatch
However, its performance deteriorates under class distribution mismatch, wherein the unlabeled data contain many samples out of the class distribution of labeled data.
Meta-Learning for Neural Relation Classification with Distant Supervision
Distant supervision provides a means to create a large number of weakly labeled data at low cost for relation classification.
VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification
Much progress has been made recently on text classification with methods based on neural networks.
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases
Furthermore, we propose a diversified point network to generate a set of diverse keyphrases out of the word graph in the decoding process.
A Long-Short Demands-Aware Model for Next-Item Recommendation
Recommending the right products is the central problem in recommender systems, but the right products should also be recommended at the right time to meet the demands of users, so as to maximize their values.
Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection
The system aims at exploring the potential of context information of terms for emotion analysis.
