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Found 9 papers, 4 papers with code
Enhancing Diffusion-based Point Cloud Generation with Smoothness Constraint
Diffusion models have been popular for point cloud generation tasks.
Development of a Deep Learning System for Intra-Operative Identification of Cancer Metastases
Our findings demonstrate the technical feasibility of an AI system for intra-operative identification of peritoneal surface metastases, but require future assessment in a multi-institutional clinical setting.
Interpretable Node Representation with Attribute Decoding
We further propose a new learning model, interpretable NOde Representation with Attribute Decoding (NORAD).
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
A graph generative model defines a distribution over graphs.
DiaKG: an Annotated Diabetes Dataset for Medical Knowledge Graph Construction
In order to accelerate the research for domain-specific knowledge graphs in the medical domain, we introduce DiaKG, a high-quality Chinese dataset for Diabetes knowledge graph, which contains 22, 050 entities and 6, 890 relations in total.
Using Graph Neural Networks for Mass Spectrometry Prediction
We compare our results to NEIMS, a neural network model that utilizes molecular fingerprints as inputs.
Localizing and Amortizing: Efficient Inference for Gaussian Processes
Additionally, the inference task is decomposed to small subtasks with several technique innovations, making our model well suits the stochastic optimization.
Context Selection for Embedding Models
Embedding models consider the probability of a target observation (a word or an item) conditioned on the elements in the context (other words or items).
A Conditional Multinomial Mixture Model for Superset Label Learning
We propose a probabilistic model, the Logistic StickBreaking Conditional Multinomial Model (LSB-CMM), to do the job.
