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Found 19 papers, 1 papers with code
C3SASR: Cheap Causal Convolutions for Self-Attentive Sequential Recommendation
We evaluate the effectiveness of the proposed model in terms of both accurate and calibrated sequential recommendation.
IDP-PGFE: An Interpretable Disruption Predictor based on Physics-Guided Feature Extraction
Understanding why a predictor makes a certain prediction can be as crucial as the prediction's accuracy for future tokamak disruption predictors.
Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor
Based on the feature extractor trained on J-TEXT, the disruption prediction model was transferred to EAST data with mere 20 discharges from EAST experiment.
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity.
Long-Tail Session-based Recommendation from Calibration
Accurate predictions in session-based recommendations have progressed, but a few studies have focused on skewed recommendation lists caused by popularity bias.
The Stackelberg Equilibrium for One-sided Zero-sum Partially Observable Stochastic Games
We propose a space partition approach to solve the game iteratively and show that the value function of the leader is piece-wise linear and the value function of the follower is piece-wise constant for multiple stages.
Subject-independent Human Pose Image Construction with Commodity Wi-Fi
Existing papers achieve good results when constructing the images of subjects who are in the prior training samples.
A Multi-View Dynamic Fusion Framework: How to Improve the Multimodal Brain Tumor Segmentation from Multi-Views?
Moreover, the multi-view fusion loss, which consists of the segmentation loss, the transition loss and the decision loss, is proposed to facilitate the training process of multi-view learning networks so as to keep the consistency of appearance and space, not only in the process of fusing segmentation results, but also in the process of training the learning network.
CentiTrack: Towards Centimeter-Level Passive Gesture Tracking with Commodity WiFi
Gesture awareness plays a crucial role in promoting human-computer interface.
Graph Neural Network Encoding for Community Detection in Attribute Networks
Based on the new encoding method and the two objectives, a multiobjective evolutionary algorithm (MOEA) based upon NSGA-II, termed as continuous encoding MOEA, is developed for the transformed community detection problem with continuous decision variables.
Protein structure and sequence re-analysis of 2019-nCoV genome does not indicate snakes as its intermediate host or the unique similarity between its spike protein insertions and HIV-1
As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, careful analysis of its transmission and cellular mechanisms is sorely needed.
KRM-based Dialogue Management
A KRM-based dialogue management (DM) is proposed using to implement human-computer dialogue system in complex scenarios.
MAssistant: A Personal Knowledge Assistant for MOOC Learners
Massive Open Online Courses (MOOCs) have developed rapidly and attracted large number of learners.
A Random Interaction Forest for Prioritizing Predictive Biomarkers
Precision medicine is becoming a focus in medical research recently, as its implementation brings values to all stakeholders in the healthcare system.
Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
Our method is built as an end-to-end framework for segmentation and classification.
Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning
In this paper, we propose a method for air quality measurement based on double-channel convolutional neural network ensemble learning to solve the problem of feature extraction for different parts of environmental images.
Ball: An R package for detecting distribution difference and association in metric spaces
The rapid development of modern technology facilitates the appearance of numerous unprecedented complex data which do not satisfy the axioms of Euclidean geometry, while most of the statistical hypothesis tests are available in Euclidean or Hilbert spaces.
Computation
Drug–drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths
Results In this article, we present a hierarchical recurrent neural networks (RNNs)-based method to integrate the SDP and sentence sequence for DDI extraction task.
Regression trees for longitudinal and multiresponse data
Previous algorithms for constructing regression tree models for longitudinal and multiresponse data have mostly followed the CART approach.
