Event correlation reasoning infers whether a natural language paragraph containing multiple events conforms to human common sense.
It consists of (1) a pairwise type-enriched sentence encoding module injecting both context-free and -related backgrounds to alleviate sentence-level wrong labeling, and (2) a hierarchical type-sentence alignment module enriching a sentence with the triple fact's basic attributes to support long-tail relations.
Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain.
Aspect-level sentiment classification (ALSC) aims at identifying the sentiment polarity of a specified aspect in a sentence.
This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for spatial-temporal graph data.
Implementing an open innovation framework in the healthcare industry, namely open health, is to enhance innovation and creative capability of health-related organisations by building a next-generation collaborative framework with partner organisations and the research community.
By comparison, a mixture of multiple global models could capture the heterogeneity across various users if assigning the users to different global models (i. e., centers) in FL.
Procedural text understanding aims at tracking the states (e. g., create, move, destroy) and locations of the entities mentioned in a given paragraph.
To address this, we propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
The heterogeneity across devices usually hinders the optimization convergence and generalization performance of federated learning (FL) when the aggregation of devices' knowledge occurs in the gradient space.
Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen).
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.
To resolve this problem, we propose Isometric Propagation Network (IPN), which learns to strengthen the relation between classes within each space and align the class dependency in the two spaces.
Typical methods to study cognitive function are to record the electrical activities of animal neurons during the training of animals performing behavioral tasks.
Few-shot learning aims to train a classifier given only a few samples per class that are highly insufficient to describe the whole data distribution.
In this paper, we introduce an efficient method, \name, to extract the local inference chains by optimizing a differentiable sparse scoring for the filters and layers to preserve the outputs on given data from a local region.
We also instantiate our framework with different combinations, which set the new state of the art on benchmark-simulated and real-world datasets with noisy labels.
Specifically, our method maintains a dynamically updating confusion matrix, which analyzes confusable classes in the dataset.
Numerous deep reinforcement learning agents have been proposed, and each of them has its strengths and flaws.
Many graph embedding approaches have been proposed for knowledge graph completion via link prediction.
Then, facilitated by the proposed base model, we introduce collaborating relation features shared among relations in the hierarchies to promote the relation-augmenting process and balance the training data for long-tail relations.
Electronic health records (EHRs) are longitudinal records of a patient's interactions with healthcare systems.
To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes.
We consider the problem of multi-domain few-shot image classification, where unseen classes and examples come from diverse data sources.
Ranked #1 on Few-Shot Image Classification on Meta-Dataset
The key challenge of patient journey understanding is to design an effective encoding mechanism which can properly tackle the aforementioned multi-level structured patient journey data with temporal sequential visits and a set of medical codes.
Recent few-shot learning works focus on training a model with prior meta-knowledge to fast adapt to new tasks with unseen classes and samples.
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.
Ranked #2 on Univariate Time Series Forecasting on Electricity
However, due to the diverse nature of user behaviors, assigning users' gradients to different global models (i. e., centers) can better capture the heterogeneity of data distributions across users.
In experiments, we achieve state-of-the-art performance on three benchmarks and a zero-shot dataset for link prediction, with highlights of inference costs reduced by 1-2 orders of magnitude compared to a textual encoding method.
Ranked #1 on Link Prediction on WN18RR (using extra training data)
In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training, to inject language models with structured knowledge via learning from raw text.
For time series classification task using 1D-CNN, the selection of kernel size is critically important to ensure the model can capture the right scale salient signal from a long time-series.
Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision.
We consider the problem of conversational question answering over a large-scale knowledge base.
In this paper, we propose a medical concept embedding method based on applying a self-attention mechanism to represent each medical concept.
These two problems lead to a poorly-trained semantic parsing model.
Graph clustering is a fundamental task which discovers communities or groups in networks.
Ranked #6 on Node Clustering on Cora
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.
Ranked #6 on Traffic Prediction on PEMS-BAY
The resulting graph of prototypes can be continually re-used and updated for new tasks and classes.
It addresses the ``many-class'' problem by exploring the class hierarchy, e. g., the coarse-class label that covers a subset of fine classes, which helps to narrow down the candidates for the fine class and is cheaper to obtain.
In this paper, we propose a novel framework called, dual attention graph convolutional networks (DAGCN) to address these problems.
Ranked #20 on Graph Classification on NCI1
Based on this framework, we derive two variants of adversarial models, the adversarially regularized graph autoencoder (ARGA) and its variational version, adversarially regularized variational graph autoencoder (ARVGA), to learn the graph embedding effectively.
Ranked #5 on Node Clustering on Cora
In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields.
Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestion by training models in distributed clients rather than training in a central server.
Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing.
Modeling user-item interaction patterns is an important task for personalized recommendations.
Neural networks equipped with self-attention have parallelizable computation, light-weight structure, and the ability to capture both long-range and local dependencies.
Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment.
In this paper, we propose a model, called "bi-directional block self-attention network (Bi-BloSAN)", for RNN/CNN-free sequence encoding.
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.
Ranked #2 on Link Prediction on Pubmed
In this paper, we integrate both soft and hard attention into one context fusion model, "reinforced self-attention (ReSA)", for the mutual benefit of each other.
Ranked #52 on Natural Language Inference on SNLI
In this paper, we propose a novel marginalized graph autoencoder (MGAE) algorithm for graph clustering.
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively.
Ranked #65 on Natural Language Inference on SNLI
In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars.