Search Results for author:
Found 25 papers, 6 papers with code
High Speed Rotation Estimation with Dynamic Vision Sensors
By designing a series of signal processing algorithms bespoke for dynamic vision sensing on mobile devices, EV-Tach is able to extract the rotational speed accurately from the event stream produced by dynamic vision sensing on rotary targets.
Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets
We investigate a data quality-aware dynamic client selection problem for multiple federated learning (FL) services in a wireless network, where each client offers dynamic datasets for the simultaneous training of multiple FL services, and each FL service demander has to pay for the clients under constrained monetary budgets.
A Comparative Study on Application of Class-Imbalance Learning for Severity Prediction of Adverse Events Following Immunization
In collaboration with the Liaoning CDC, China, we propose a prediction system to predict the subsequent hospitalization of children with adverse reactions based on data on adverse events following immunization.
Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk
Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observation.
One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancer
Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine learning and abductive learning, in inventing advanced artificial intelligence technologies.
StackVAE-G: An efficient and interpretable model for time series anomaly detection
We also leverage a graph learning module to learn a sparse adjacency matrix to explicitly capture the stable interrelation structure among multiple time series channels for the interpretable pattern reconstruction of interrelated channels.
The collider tests of a leptophilic scalar for the anomalous magnetic moments
Our results show that the leptophilic scalar in the mass range of $\mathcal{O}(10)- \mathcal{O}(1000 )\,\rm GeV$ can be fully probed by the future experimental searches at the HL-LHC and the lepton colliders at their early stages.
High Energy Physics - Phenomenology
A Survey on Ensemble Learning under the Era of Deep Learning
An urgent problem needs to be solved is how to take the significant advantages of ensemble deep learning while reduce the required expenses so that many more applications in specific fields can benefit from it.
Learn to Segment Retinal Lesions and Beyond
This paper attacks the three challenges in the context of diabetic retinopathy (DR) grading.
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e. g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models.
Improving Adversarial Robustness via Promoting Ensemble Diversity
Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial attacks.
Transferable Adversarial Attacks for Image and Video Object Detection
Adversarial examples have been demonstrated to threaten many computer vision tasks including object detection.
Type-II 2HDM under the Precision Measurements at the $Z$-pole and a Higgs Factory
We also find that the expected accuracies at the $Z$-pole and at a Higgs factory are quite complementary in constraining mass splittings of heavy Higgs bosons.
High Energy Physics - Phenomenology
Message Passing Stein Variational Gradient Descent
Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods.
A Grassmannian Approach to Zero-Shot Learning for Network Intrusion Detection
This can be overcome with Zero-Shot Learning, a new approach in the field of Computer Vision, which can be described in two stages: the Attribute Learning and the Inference Stage.
SAM: Semantic Attribute Modulation for Language Modeling and Style Variation
This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation.
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation
We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features.
Dropout Training for SVMs with Data Augmentation
Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of both linear and nonlinear SVMs.
Dropout Training for Support Vector Machines
To deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square (IRLS) algorithm by exploring data augmentation techniques.
Gibbs Max-margin Topic Models with Data Augmentation
Gibbs max-margin supervised topic models minimize an expected margin loss, which is an upper bound of the existing margin loss derived from an expected prediction rule.
Discriminative Relational Topic Models
Many scientific and engineering fields involve analyzing network data.
Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs
When the regularization is induced from a linear operator on the posterior distributions, such as the expectation operator, we present a general convex-analysis theorem to characterize the solution of RegBayes.
On the Complexity of Trial and Error
On one hand, despite the seemingly very little information provided by the verification oracle, efficient algorithms do exist for a number of important problems.
Infinite Latent SVM for Classification and Multi-task Learning
Unlike existing nonparametric Bayesian models, which rely solely on specially conceived priors to incorporate domain knowledge for discovering improved latent representations, we study nonparametric Bayesian inference with regularization on the desired posterior distributions.
Predictive Subspace Learning for Multi-view Data: a Large Margin Approach
Learning from multi-view data is important in many applications, such as image classification and annotation.
