Search Results for author:
Found 18 papers, 6 papers with code
ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision
For example, chemistry research needs to study dozens to hundreds of distinct, fine-grained entity types, making consistent and accurate annotation difficult even for crowds of domain experts.
When and How: Learning Identifiable Latent States for Nonstationary Time Series Forecasting
To solve this problem, we propose to learn IDentifiable latEnt stAtes (IDEA) to detect when the distribution shifts occur.
Calibration-then-Calculation: A Variance Reduced Metric Framework in Deep Click-Through Rate Prediction Models
The metric variance comes from the randomness inherent in the training process of deep learning pipelines.
CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
Identifying the underlying time-delayed latent causal processes in sequential data is vital for grasping temporal dynamics and making downstream reasoning.
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems.
Temporally Disentangled Representation Learning under Unknown Nonstationarity
In unsupervised causal representation learning for sequential data with time-delayed latent causal influences, strong identifiability results for the disentanglement of causally-related latent variables have been established in stationary settings by leveraging temporal structure.
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real-world applications, such as finance, retail, healthcare, etc.
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks
To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis.
PLOT: Prompt Learning with Optimal Transport for Vision-Language Models
To solve this problem, we propose to apply optimal transport to match the vision and text modalities.
TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations
Taxonomies are fundamental to many real-world applications in various domains, serving as structural representations of knowledge.
Learning Multi-granularity User Intent Unit for Session-based Recommendation
Specifically, we creatively propose Multi-granularity Intent Heterogeneous Session Graph which captures the interactions between different granularity intent units and relieves the burden of long-dependency.
Creating Training Sets via Weak Indirect Supervision
Creating labeled training sets has become one of the major roadblocks in machine learning.
Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation
However, simply integrating KGs in current KG-based RS models is not necessarily a guarantee to improve the recommendation performance, which may even weaken the holistic model capability.
Click-Through Rate Prediction
Knowledge-Aware Recommendation
+1
Who Should Go First? A Self-Supervised Concept Sorting Model for Improving Taxonomy Expansion
Taxonomies have been widely used in various machine learning and text mining systems to organize knowledge and facilitate downstream tasks.
Taxonomy Completion via Triplet Matching Network
Previous approaches focus on the taxonomy expansion, i. e. finding an appropriate hypernym concept from the taxonomy for a new query concept.
BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks
We propose BiTe-GCN, a novel GCN architecture with bidirectional convolution of both topology and features on text-rich networks to solve these limitations.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions.
Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision
We created this CORD-NER dataset with comprehensive named entity recognition (NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus (2020-03-13).
