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Found 25 papers, 3 papers with code
Global Context Enhanced Graph Neural Networks for Session-based Recommendation
In GCE-GNN, we propose a novel global-level item representation learning layer, which employs a session-aware attention mechanism to recursively incorporate the neighbors' embeddings of each node on the global graph.
Exploiting Group-level Behavior Pattern forSession-based Recommendation
In RNMSR, we propose to learn the user preference from both instance-level and group-level, respectively: (i) instance-level, which employs GNNs on a similarity-based item-pairwise session graph to capture the users' preference in instance-level.
Exploring Global Information for Session-based Recommendation
Based on BGNN, we propose a novel approach, called Session-based Recommendation with Global Information (SRGI), which infers the user preferences via fully exploring global item-transitions over all sessions from two different perspectives: (i) Fusion-based Model (SRGI-FM), which recursively incorporates the neighbor embeddings of each node on global graph into the learning process of session level item representation; and (ii) Constrained-based Model (SRGI-CM), which treats the global-level item-transition information as a constraint to ensure the learned item embeddings are consistent with the global item-transition.
Attention Sequence to Sequence Model for Machine Remaining Useful Life Prediction
Accurate estimation of remaining useful life (RUL) of industrial equipment can enable advanced maintenance schedules, increase equipment availability and reduce operational costs.
Recent Advances in Network-based Methods for Disease Gene Prediction
Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases.
A Frame-based Sentence Representation for Machine Reading Comprehension
Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC).
Efficient Constituency Parsing by Pointing
We propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks.
Differentiable Window for Dynamic Local Attention
We propose Differentiable Window, a new neural module and general purpose component for dynamic window selection.
Multi-View Collaborative Network Embedding
Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes.
StereoNeuroBayesSLAM: A Neurobiologically Inspired Stereo Visual SLAM System Based on Direct Sparse Method
We propose a neurobiologically inspired visual simultaneous localization and mapping (SLAM) system based on direction sparse method to real-time build cognitive maps of large-scale environments from a moving stereo camera.
Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification
Then, we propose Heterogeneous Graph ATtention networks (HGAT) to embed the HIN for short text classification based on a dual-level attention mechanism, including node-level and type-level attentions.
SL$^2$MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization
Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO).
HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems
This paper investigates the notion of learning user and item representations in non-Euclidean space.
Ranked #1 on
Recommendation Systems
on MovieLens 20M
(HR@10 metric)
Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working condition
The layers between the source and target feature extractor are partially untied during the training stage to take both training efficiency and domain adaptation into consideration.
Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation
Our proposed approach hinges upon the key intuition that the decision making process (in groups) is generally dynamic, i. e., a user's decision is highly dependent on the other group members.
Pruning Convolutional Neural Networks for Image Instance Retrieval
In this work, we focus on the problem of image instance retrieval with deep descriptors extracted from pruned Convolutional Neural Networks (CNN).
Incremental Minimax Optimization based Fuzzy Clustering for Large Multi-view Data
The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed IminimaxFCM are provided.
Keyphrase Extraction using Sequential Labeling
Keyphrases efficiently summarize a document's content and are used in various document processing and retrieval tasks.
Learning Social Circles in Ego Networks based on Multi-View Social Graphs
Based on extensive automatic and manual experimental evaluations, we deliver two major findings: first, multi-view clustering techniques perform better than common single-view clustering techniques, which only use one view or naively integrate all views for detection, second, the standard multi-view clustering technique is less robust than our modified technique, which selectively transfers information across views based on an assumption that sparse network structures are (potentially) incomplete.
Classification and its applications for drug-target interaction identification
Classification is one of the most popular and widely used supervised learning tasks, which categorizes objects into predefined classes based on known knowledge.
Microbial community pattern detection in human body habitats via ensemble clustering framework
To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data.
