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Found 53 papers, 11 papers with code
Convergence of Continuous Normalizing Flows for Learning Probability Distributions
We establish non-asymptotic error bounds for the distribution estimator based on CNFs, in terms of the Wasserstein-2 distance.
Penalized Generative Variable Selection
Deep networks are increasingly applied to a wide variety of data, including data with high-dimensional predictors.
DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning of Gough-Stewart Platform
As executed on a GPU, our two-stage method can ensure the requirement for real-time computation.
Conditional Stochastic Interpolation for Generative Learning
We propose a conditional stochastic interpolation (CSI) approach to learning conditional distributions.
Gaussian Interpolation Flows
Gaussian denoising has emerged as a powerful principle for constructing simulation-free continuous normalizing flows for generative modeling.
DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Macular Hole Reconstruction with Stochastic Retinal Defect Augmentation and Dynamic Weight Composition
Macular hole diagnosis and treatment rely heavily on spatial and quantitative data, yet the scarcity of such data has impeded the progress of deep learning techniques for effective segmentation and real-time 3D reconstruction.
G10: Enabling An Efficient Unified GPU Memory and Storage Architecture with Smart Tensor Migrations
Based on this unified GPU memory and storage architecture, G10 utilizes compiler techniques to characterize the tensor behaviors in deep learning workloads.
Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models
For the quadratic loss in nonparametric regression, we show that the adversarial excess risk bound can be improved over those for a general loss.
Wasserstein Generative Regression
Our approach simultaneously estimates a regression function and a conditional generator using a generative learning framework, where a conditional generator is a function that can generate samples from a conditional distribution.
Differentiable Neural Networks with RePU Activation: with Applications to Score Estimation and Isotonic Regression
We establish error bounds for simultaneously approximating $C^s$ smooth functions and their derivatives using RePU-activated deep neural networks.
Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction
We also derive non-asymptotic upper bounds for the difference of the lengths between the proposed non-crossing conformal prediction interval and the theoretically oracle prediction interval.
Fast localization and single-pixel imaging of the moving object using time-division multiplexing
When imaging moving objects, single-pixel imaging produces motion blur.
Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks
We propose a penalized nonparametric approach to estimating the quantile regression process (QRP) in a nonseparable model using rectifier quadratic unit (ReQU) activated deep neural networks and introduce a novel penalty function to enforce non-crossing of quantile regression curves.
Deep Sufficient Representation Learning via Mutual Information
We propose a mutual information-based sufficient representation learning (MSRL) approach, which uses the variational formulation of the mutual information and leverages the approximation power of deep neural networks.
Learning Video Representations of Human Motion From Synthetic Data
In this paper, we take an early step towards video representation learning of human actions with the help of largescale synthetic videos, particularly for human motion representation enhancement.
Wasserstein Generative Learning of Conditional Distribution
The proposed approach uses a conditional generator to transform a known distribution to the target conditional distribution.
Non-Asymptotic Error Bounds for Bidirectional GANs
Our results are also applicable to the Wasserstein bidirectional GAN if the target distribution is assumed to have a bounded support.
A Learning-based Approach Towards Automated Tuning of SSD Configurations
Thanks to the mature manufacturing techniques, solid-state drives (SSDs) are highly customizable for applications today, which brings opportunities to further improve their storage performance and resource utilization.
Relative Entropy Gradient Sampler for Unnormalized Distributions
We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions.
An error analysis of generative adversarial networks for learning distributions
This paper studies how well generative adversarial networks (GANs) learn probability distributions from finite samples.
Non-asymptotic Excess Risk Bounds for Classification with Deep Convolutional Neural Networks
To establish these results, we derive an upper bound for the covering number for the class of general convolutional neural networks with a bias term in each convolutional layer, and derive new results on the approximation power of CNNs for any uniformly-continuous target functions.
Dynamic sensitivity of quantum Rabi model with quantum criticality
We study the dynamic sensitivity of the quantum Rabi model, which exhibits quantum criticality in the finite-component-system case.
Quantum Physics
Sufficient and Disentangled Representation Learning
We provide strong statistical guarantees for the learned representation by establishing an upper bound on the excess error of the objective function and show that it reaches the nonparametric minimax rate under mild conditions.
Toward Understanding Supervised Representation Learning with RKHS and GAN
At the population level, we formulate the ideal representation learning task as that of finding a nonlinear map that minimizes the sum of losses characterizing conditional independence (with RKHS) and disentanglement (with GAN).
Ranked #4 on
Image Classification
on Kuzushiji-MNIST
Quantum simulation of a three-mode optomechanical system based on the Fredkin-type interaction
The realization of multimode optomechanical interactions in the single-photon strong-coupling regime is a desired task in cavity optomechanics, but it remains a challenge in realistic physical systems.
Quantum Physics
Generative Learning With Euler Particle Transport
The key task in training is the estimation of the density ratios or differences that determine the residual maps.
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks
Test samples with the backdoor key will then be classified into the target class specified by the attacker.
Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition
Emotion recognition remains a complex task due to speaker variations and low-resource training samples.
Ranked #1 on
Speech Emotion Recognition
on IEMOCAP
(using extra training data)
Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline
Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance.
Deep Dimension Reduction for Supervised Representation Learning
We propose a deep dimension reduction approach to learning representations with these characteristics.
Efficient Use of heuristics for accelerating XCS-based Policy Learning in Markov Games
Besides, taking advantages of the condition representation and matching mechanism of XCS, the heuristic policies and the opponent model can provide guidance for situations with similar feature representation.
BoostTree and BoostForest for Ensemble Learning
Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance.
Learning Implicit Generative Models with Theoretical Guarantees
We then solve the McKean-Vlasov equation numerically using the forward Euler iteration, where the forward Euler map depends on the density ratio (density difference) between the distribution at current iteration and the underlying target distribution.
On Newton Screening
Based on this KKT system, a built-in working set with a relatively small size is first determined using the sum of primal and dual variables generated from the previous iteration, then the primal variable is updated by solving a least-squares problem on the working set and the dual variable updated based on a closed-form expression.
A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models
Feature selection is important for modeling high-dimensional data, where the number of variables can be much larger than the sample size.
Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction
Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph.
Conversational Emotion Analysis via Attention Mechanisms
Different from the emotion recognition in individual utterances, we propose a multimodal learning framework using relation and dependencies among the utterances for conversational emotion analysis.
Domain adversarial learning for emotion recognition
The secondary task is to learn a common representation where speaker identities can not be distinguished.
Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition
Prior works on speech emotion recognition utilize various unsupervised learning approaches to deal with low-resource samples.
Speech Emotion Recognition via Contrastive Loss under Siamese Networks
It outperforms the baseline system that is optimized without the contrastive loss function with 1. 14% and 2. 55% in the weighted accuracy and the unweighted accuracy, respectively.
Expression Analysis Based on Face Regions in Read-world Conditions
To sum up, the contributions of this paper lie in two areas: 1) We visualize concerned areas of human faces in emotion recognition; 2) We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization
Takagi-Sugeno-Kang (TSK) fuzzy systems are flexible and interpretable machine learning models; however, they may not be easily optimized when the data size is large, and/or the data dimensionality is high.
On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression
Fuzzy systems have achieved great success in numerous applications.
Improving speech emotion recognition via Transformer-based Predictive Coding through transfer learning
I have submitted a new version to arXiv:1910. 13806.
SNAP: A semismooth Newton algorithm for pathwise optimization with optimal local convergence rate and oracle properties
We propose a semismooth Newton algorithm for pathwise optimization (SNAP) for the LASSO and Enet in sparse, high-dimensional linear regression.
Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition
We test our method in the EmotiW 2018 challenge and we gain promising results.
Active Learning for Regression Using Greedy Sampling
Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection.
Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression
Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample.
Photo-Guided Exploration of Volume Data Features
In this work, we pose the question of whether, by considering qualitative information such as a sample target image as input, one can produce a rendered image of scientific data that is similar to the target.
Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression
We propose an algorithm, semismooth Newton coordinate descent (SNCD), for the elastic-net penalized Huber loss regression and quantile regression in high dimensional settings.
A Unified Primal Dual Active Set Algorithm for Nonconvex Sparse Recovery
In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations.
Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
Penalized regression is an attractive framework for variable selection problems.
SCAD-penalized regression in high-dimensional partially linear models
We consider the problem of simultaneous variable selection and estimation in partially linear models with a divergent number of covariates in the linear part, under the assumption that the vector of regression coefficients is sparse.
Statistics Theory Statistics Theory 62J05, 62G08 (Primary) 62E20 (Secondary)
