Search Results for author:
Found 28 papers, 6 papers with code
Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis
In light of this, in this paper we propose a wavelet-based neural network structure called multilevel Wavelet Decomposition Network (mWDN) for building frequency-aware deep learning models for time series analysis.
Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction
ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods.
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Traffic Prediction
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A Matlab Toolbox for Feature Importance Ranking
Moreover, the toolbox is evaluated on a database of 163 ultrasound images.
Variational Quantum Circuits for Quantum State Tomography
We demonstrate our method by performing numerical simulations for the tomography of the ground state of a one-dimensional quantum spin chain, using a variational quantum circuit simulator.
Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations
Online conversations can go in many directions: some turn out poorly due to antisocial behavior, while others turn out positively to the benefit of all.
Towards General Error Diagnosis via Behavioral Testing in Machine Translation
Behavioral testing offers a crucial means of diagnosing linguistic errors and assessing capabilities of NLP models.
Social Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data
First, public opinions in online media dominate traditional polls in Taiwan election prediction in terms of both predictive power and timeliness.
Complementary Segmentation of Primary Video Objects with Reversible Flows
Segmenting primary objects in a video is an important yet challenging problem in computer vision, as it exhibits various levels of foreground/background ambiguities.
SVM-based Deep Stacking Networks
The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning.
Understanding Urban Dynamics via Context-aware Tensor Factorization with Neighboring Regularization
In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factorization model (NR-cNTF) to discover interpretable urban dynamics from urban heterogeneous data.
AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks
Recent years have witnessed the successful marriage of finance innovations and AI techniques in various finance applications including quantitative trading (QT).
Recommendation with Attribute-aware Product Networks: A Representation Learning Model
Specifically, eRAN first maps items connected in attribute networks to low-dimensional embedding vectors through a deep autoencoder, and then an attention mechanism is applied to model the attractions of attributes to users, from which personalized item representation can be derived.
Beyond Similarity: Relation Embedding with Dual Attentions for Item-based Recommendation
Given the effectiveness and ease of use, Item-based Collaborative Filtering (ICF) methods have been broadly used in industry in recent years.
System Design and Analysis for Energy-Efficient Passive UAV Radar Imaging System using Illuminators of Opportunity
Then, a set of mission performance evaluators is established to quantitatively assess the capability of the system in a comprehensive manner, including UAV navigation, passive SAR imaging and communication.
Multi-Aspect Temporal Network Embedding: A Mixture of Hawkes Process View
Along this vein, we propose a Mixture of Hawkes-based Temporal Network Embeddings (MHNE) model to capture the aspect-driven neighborhood formation of networks.
Augmenting Topic Aware Knowledge-Grounded Conversations with Dynamic Built Knowledge Graphs
Dialog topic management and background knowledge selection are essential factors for the success of knowledge-grounded open-domain conversations.
HyObscure: Hybrid Obscuring for Privacy-Preserving Data Publishing
We then design a novel hybrid protection mechanism called HyObscure, to cross-iteratively optimize the generalization and obfuscation operations for maximum privacy protection under a certain utility guarantee.
Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method
In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method.
Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks
Basically, we propose to perturb the original network by adding or removing links, and expect the embedding generated on the perturbed network can leak little information about private links but hold high utility for various downstream tasks.
Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
The bladder contour quality was primarily affected by using IV contrast.
Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense
While having achieved great success in rich real-life applications, deep neural network (DNN) models have long been criticized for their vulnerability to adversarial attacks.
SCAT: Robust Self-supervised Contrastive Learning via Adversarial Training for Text Classification
Specifically, SCAT modifies random augmentations of the data in a fully labelfree manner to generate adversarial examples.
When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation
In this paper, we propose a new multi-task learning approach for SAR ATR, which could obtain the accurate category and precise shape of the targets simultaneously.
Quantum generative adversarial learning in photonics
Quantum Generative Adversarial Networks (QGANs), an intersection of quantum computing and machine learning, have attracted widespread attention due to their potential advantages over classical analogs.
Experimental quantum natural gradient optimization in photonics
Our work opens up a vista of utilizing QNG in photonics to implement practical near-term quantum applications.
Language Model as an Annotator: Unsupervised Context-aware Quality Phrase Generation
Finally, we merge the quality phrases from both the Annotator and Generator as the final predictions, considering their complementary nature and distinct characteristics.
Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning
Developing effective Map Entity Representation Learning (MERL) methods is crucial to extracting embedding information from electronic maps and converting map entities into representation vectors for downstream applications.
