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Found 20 papers, 4 papers with code
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
Based on this proposal, we introduce Unified Spatio-Temporal Diffusion Models (USTD) to address the tasks uniformly within the uncertainty-aware diffusion framework.
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
To address these issues, we propose UniTime for effective cross-domain time series learning.
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting
To mitigate these limitations, we introduce the LargeST benchmark dataset.
Graph Neural Processes for Spatio-Temporal Extrapolation
We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph.
Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference
Specifically, we introduce a joint spatiotemporal graph attention network to learn the relations across space and time for short-term patterns.
Unsupervised Domain Adaptation Network with Category-Centric Prototype Aligner for Biomedical Image Segmentation
Specifically, our approach consists of two key modules, a conditional domain discriminator~(CDD) and a category-centric prototype aligner~(CCPA).
Incorporate Semantic Structures into Machine Translation Evaluation via UCCA
Copying mechanism has been commonly used in neural paraphrasing networks and other text generation tasks, in which some important words in the input sequence are preserved in the output sequence.
Evaluation of company investment value based on machine learning
In this paper, company investment value evaluation models are established based on comprehensive company information.
Predicting Long-Term Skeletal Motions by a Spatio-Temporal Hierarchical Recurrent Network
The primary goal of skeletal motion prediction is to generate future motion by observing a sequence of 3D skeletons.
Meteor++ 2.0: Adopt Syntactic Level Paraphrase Knowledge into Machine Translation Evaluation
This paper describes Meteor++ 2. 0, our submission to the WMT19 Metric Shared Task.
Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings
In multi-sense word embeddings, contextual variations in corpus may cause a univocal word to be embedded into different sense vectors.
Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation
In machine translation evaluation, a good candidate translation can be regarded as a paraphrase of the reference.
Understanding and Improving Multi-Sense Word Embeddings via Extended Robust Principal Component Analysis
Unsupervised learned representations of polysemous words generate a large of pseudo multi senses since unsupervised methods are overly sensitive to contextual variations.
Context Aware Document Embedding
Recently, doc2vec has achieved excellent results in different tasks.
Max-Cosine Matching Based Neural Models for Recognizing Textual Entailment
Recognizing textual entailment is a fundamental task in a variety of text mining or natural language processing applications.
Real Multi-Sense or Pseudo Multi-Sense: An Approach to Improve Word Representation
Previous researches have shown that learning multiple representations for polysemous words can improve the performance of word embeddings on many tasks.
Domain Ontology Learning Enhanced by Optimized Relation Instance in DBpedia
Experiment shows that the result of ontology learning from corpus of computer science can be improved via the relation instances extracted from DBpedia in the same field.
Construction of Diachronic Ontologies from People's Daily of Fifty Years
Preliminary experiments show that our algorithm outperforms the Google{'}s RNN and K-means based algorithm in both concepts discovery and concepts hierarchical clustering.
