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Found 24 papers, 7 papers with code
TopWORDS-Seg: Simultaneous Text Segmentation and Word Discovery for Open-Domain Chinese Texts via Bayesian Inference
Processing open-domain Chinese texts has been a critical bottleneck in computational linguistics for decades, partially because text segmentation and word discovery often entangle with each other in this challenging scenario.
Performance-Driven QUBO for Recommender Systems on Quantum Annealers
We propose Counterfactual Analysis Quadratic Unconstrained Binary Optimization (CAQUBO) to solve QUBO problems for feature selection in recommender systems.
CRUISE on Quantum Computing for Feature Selection in Recommender Systems
In this paper, we use Quantum Annealers to address the feature selection problem in recommendation algorithms.
Efficient Surgical Tool Recognition via HMM-Stabilized Deep Learning
Recognizing various surgical tools, actions and phases from surgery videos is an important problem in computer vision with exciting clinical applications.
Transfer Learning-Enhanced Instantaneous Multi-Person Indoor Localization by CSI
Passive indoor localization, integral to smart buildings, emergency response, and indoor navigation, has traditionally been limited by a focus on single-target localization and reliance on multi-packet CSI.
Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning
This study aims to minimize the influence of fake news on social networks by deploying debunkers to propagate true news.
Explainable History Distillation by Marked Temporal Point Process
To efficiently solve \acrshort{ehd}, we rewrite the task into a \gls{01ip} and directly estimate the solution to the program by a model called \acrfull{model}.
Total-effect Test May Erroneously Reject So-called "Full" or "Complete" Mediation
The procedure for establishing mediation, i. e., determining that an independent variable X affects a dependent variable Y through some mediator M, has been under debate.
Intensity-free Integral-based Learning of Marked Temporal Point Processes
In the marked temporal point processes (MTPP), a core problem is to parameterize the conditional joint PDF (probability distribution function) $p^*(m, t)$ for inter-event time $t$ and mark $m$, conditioned on the history.
TSNet-SAC: Leveraging Transformers for Efficient Task Scheduling
In future 6G Mobile Edge Computing (MEC), autopilot systems require the capability of processing multimodal data with strong interdependencies.
Construction of unbiased dental template and parametric dental model for precision digital dentistry
In this study, we develop an unbiased dental template by constructing an accurate dental atlas from CBCT images with guidance of teeth segmentation.
ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios
The input data is augmented into two distorted views and an encoder learns the representations that are invariant to distortions -- cross-view prediction.
FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback
To fill this gap, we propose a Generative Adversarial Networks (GANs) based learning algorithm FairGAN mapping the exposure fairness issue to the problem of negative preferences in implicit feedback data.
CSSR: A Context-Aware Sequential Software Service Recommendation Model
We propose a novel software service recommendation model to help users find their suitable repositories in GitHub.
MANDERA: Malicious Node Detection in Federated Learning via Ranking
Although we know that the benign gradients and Byzantine attacked gradients are distributed differently, to detect the malicious gradients is challenging due to (1) the gradient is high-dimensional and each dimension has its unique distribution and (2) the benign gradients and the attacked gradients are always mixed (two-sample test methods cannot apply directly).
ExamGAN and Twin-ExamGAN for Exam Script Generation
There are opportunities to further improve the quality of generated exam scripts in various aspects.
Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios
Machine learning models are useful for scenario classification but most of them assume that data received during the testing are from one of the classes used in the training.
Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity
In this work, a method is proposed to address this challenge by introducing a clustering technique based on a novel data-adaptive similarity measure, called Random Forest Activation Pattern (RFAP) similarity.
A Knowledge Graph based Approach for Mobile Application Recommendation
With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders.
Multi-level Training and Bayesian Optimization for Economical Hyperparameter Optimization
Hyperparameters play a critical role in the performances of many machine learning methods.
An Improved Historical Embedding without Alignment
Our method outperformed three other popular methods in terms of the number of words correctly identified to have changed in meaning.
Sequential Learning for Dirichlet Process Mixtures
We propose an evidence upper bound (EUBO) to act as the surrogate loss, and fit a DP mixture to the given data by minimizing the EUBO, which is equivalent to minimizing the KL-divergence between the target distribution and the DP mixture.
NGEMM: Optimizing GEMM for Deep Learning via Compiler-based Techniques
Various approaches have been developed by leveraging techniques such as vectorization and memory layout to improve the performance of integer GEMM.
DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data
More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods.
