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Found 35 papers, 5 papers with code
Regularization for Adversarial Robust Learning
We numerically validate our proposed method in supervised learning, reinforcement learning, and contextual learning and showcase its state-of-the-art performance against various adversarial attacks.
Data-driven Multistage Distributionally Robust Linear Optimization with Nested Distance
We study multistage distributionally robust linear optimization, where the uncertainty set is defined as a ball of distribution centered at a scenario tree using the nested distance.
SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic
Fast convolution algorithms, including Winograd and FFT, can efficiently accelerate convolution operations in deep models.
Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships
This module incorporates a relation-aware encoder and a feedback training strategy.
Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets
We present a new framework to address the non-convex robust hypothesis testing problem, wherein the goal is to seek the optimal detector that minimizes the maximum of worst-case type-I and type-II risk functions.
Sentinel-Guided Zero-Shot Learning: A Collaborative Paradigm without Real Data Exposure
With increasing concerns over data privacy and model copyrights, especially in the context of collaborations between AI service providers and data owners, an innovative SG-ZSL paradigm is proposed in this work.
Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA
Fourthly, we employed ICP, SICP or CPD algorithm to achieve a fine registration for the point clouds (together with the special points of APIGs) of the LV epicardial surfaces (LVERs) in SPECT and CTA images.
Timed-Elastic-Band Based Variable Splitting for Autonomous Trajectory Planning
Existing trajectory planning methods are struggling to handle the issue of autonomous track swinging during navigation, resulting in significant errors when reaching the destination.
Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
We categorize sources of discrimination in the ML pipeline into two classes: aleatoric discrimination, which is inherent in the data distribution, and epistemic discrimination, which is due to decisions made during model development.
A Finite Element-Inspired Hypergraph Neural Network: Application to Fluid Dynamics Simulations
We term this method a finite element-inspired hypergraph neural network, in short FEIH($\phi$)-GNN.
Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics
To alleviate these errors, we propose a novel technique for learning coupled spatial-temporal correlation using a 3D convolution network.
Predicting fluid-structure interaction with graph neural networks
The structural state is implicitly modeled by the movement of the mesh on the solid-fluid interface; hence it makes the proposed framework quasi-monolithic.
A Short and General Duality Proof for Wasserstein Distributionally Robust Optimization
We present a general duality result for Wasserstein distributionally robust optimization that holds for any Kantorovich transport cost, measurable loss function, and nominal probability distribution.
SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification
Compared with the existing literature, our WNFG of EEG signals achieves up to 10 times of redundant edge reduction, and our approach achieves up to 97 times of model pruning without loss of classification accuracy.
Absolute Zero-Shot Learning
Considering the increasing concerns about data copyright and privacy issues, we present a novel Absolute Zero-Shot Learning (AZSL) paradigm, i. e., training a classifier with zero real data.
Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination Systems
In this paper, we propose a phase based Simultaneous Localization and Mapping (Phase-SLAM) framework for fast and accurate SLI sensor pose estimation and 3D object reconstruction.
Generalization Bounds for (Wasserstein) Robust Optimization
(Distributionally) robust optimization has gained momentum in machine learning community recently, due to its promising applications in developing generalizable learning paradigms.
Sinkhorn Distributionally Robust Optimization
We study distributionally robust optimization (DRO) with Sinkhorn distance -- a variant of Wasserstein distance based on entropic regularization.
Hierarchical Non-Stationary Temporal Gaussian Processes With $L^1$-Regularization
This paper is concerned with regularized extensions of hierarchical non-stationary temporal Gaussian processes (NSGPs) in which the parameters (e. g., length-scale) are modeled as GPs.
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels
In this paper, we analyze the generalization of models trained by noisy iterative algorithms.
Generalize Ultrasound Image Segmentation via Instant and Plug & Play Style Transfer
Deep segmentation models that generalize to images with unknown appearance are important for real-world medical image analysis.
Mutual Calibration between Explicit and Implicit Deep Generative Models
Deep generative models are generally categorized into explicit models and implicit models.
Reliable Off-policy Evaluation for Reinforcement Learning
In a sequential decision-making problem, off-policy evaluation estimates the expected cumulative reward of a target policy using logged trajectory data generated from a different behavior policy, without execution of the target policy.
Two-sample Test using Projected Wasserstein Distance
We develop a projected Wasserstein distance for the two-sample test, a fundamental problem in statistics and machine learning: given two sets of samples, to determine whether they are from the same distribution.
Contrastive Rendering for Ultrasound Image Segmentation
However, the lack of sharp boundaries in US images still remains an inherent challenge for segmentation.
Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality
In this paper, we develop a non-asymptotic framework for analyzing the out-of-sample performance for Wasserstein robust learning and the generalization bound for its related Lipschitz and gradient regularization problems.
Distributionally Robust Weighted $k$-Nearest Neighbors
When the samples are limited, robustness is especially crucial to ensure the generalization capability of the classifier.
Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer
In this paper, we propose a novel and intuitive framework to remove the appearance shift, and hence improve the generalization ability of DNNs.
Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators
To take full advantages of both models and enable mutual compensation, we propose a novel joint training framework that bridges an explicit (unnormalized) density estimator and an implicit sample generator via Stein discrepancy.
Stein Bridging: Enabling Mutual Reinforcement between Explicit and Implicit Generative Models
Deep generative models are generally categorized into explicit models and implicit models.
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
We develop a novel computationally efficient and general framework for robust hypothesis testing.
Wasserstein Distributionally Robust Optimization and Variation Regularization
Wasserstein distributionally robust optimization (DRO) has recently achieved empirical success for various applications in operations research and machine learning, owing partly to its regularization effect.
Image Fusion With Cosparse Analysis Operator
In our approach, we formulate the multi-focus image fusion problem in terms of an analysis sparse model, and simultaneously perform the restoration and fusion of multi-focus images.
Multi-ary Pulse Amplitude Modulated Signal Processing Using Bistable Stochastic Resonance
On this basis, the mechanism of the BSR system response to MPAM signal inputs is elucidated, and a corresponding decoding scheme is proposed.
Sensing Subjective Well-being from Social Media
Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media.
