Search Results for author:
Found 37 papers, 10 papers with code
Online Tensor Inference
Recent technological advances have led to contemporary applications that demand real-time processing and analysis of sequentially arriving tensor data.
Online Estimation and Inference for Robust Policy Evaluation in Reinforcement Learning
In this paper, we develop an online robust policy evaluation procedure, and establish the limiting distribution of our estimator, based on its Bahadur representation.
Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection
Early detection of dysplasia of the cervix is critical for cervical cancer treatment.
Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery with Supplementary Materials
Street-view imagery provides us with novel experiences to explore different places remotely.
Ranked #3 on
Image-Based Localization
on cvact
Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality
Stochastic gradient descent with momentum (SGDM) has been widely used in many machine learning and statistical applications.
Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose
Domain gap between synthetic and real data in visual regression (e. g. 6D pose estimation) is bridged in this paper via global feature alignment and local refinement on the coarse classification of discretized anchor classes in target space, which imposes a piece-wise target manifold regularization into domain-invariant representation learning.
Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent
With the fast development of big data, it has been easier than before to learn the optimal decision rule by updating the decision rule recursively and making online decisions.
Online Statistical Inference for Matrix Contextual Bandit
To fill in these gaps, in this work we consider a matrix contextual bandit framework where the true model parameter is a low-rank matrix, and propose a fully online procedure to simultaneously make sequential decision-making and conduct statistical inference.
Adaptive Data Fusion for Multi-task Non-smooth Optimization
We study the problem of multi-task non-smooth optimization that arises ubiquitously in statistical learning, decision-making and risk management.
Distributed Estimation and Inference for Semi-parametric Binary Response Models
The development of modern technology has enabled data collection of unprecedented size, which poses new challenges to many statistical estimation and inference problems.
Observer-based Leader-following Consensus for Positive Multi-agent Systems Over Time-varying Graphs
This paper addresses the leader-following consensus problem for discrete-time positive multi-agent systems over time-varying graphs.
BiCo-Net: Regress Globally, Match Locally for Robust 6D Pose Estimation
Inspired by the success of point-pair features, the goal of this paper is to recover the 6D pose of an object instance segmented from RGB-D images by locally matching pairs of oriented points between the model and camera space.
Mix-up Self-Supervised Learning for Contrast-agnostic Applications
Contrastive self-supervised learning has attracted significant research attention recently.
Andes_gym: A Versatile Environment for Deep Reinforcement Learning in Power Systems
The environment leverages the modeling and simulation capability of ANDES and the reinforcement learning (RL) environment OpenAI Gym to enable the prototyping and demonstration of RL algorithms for power systems.
Towards Visual Question Answering on Pathology Images
In this paper, we aim to develop a pathological visual question answering framework to analyze pathology images and answer medical questions related to these images.
On the Generation of Medical Dialogs for COVID-19
Training complex dialog generation models on small datasets bears high risk of overfitting.
Unsupervised data augmentation for object detection
Data augmentation has always been an effective way to overcome overfitting issue when the dataset is small.
Provably Correct Controller Synthesis of Switched Stochastic Systems with Metric Temporal Logic Specifications: A Case Study on Power Systems
In this paper, we present a provably correct controller synthesis approach for switched stochastic control systems with metric temporal logic (MTL) specifications with provable probabilistic guarantees.
Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning with Region-of-Interest Active Sampling
Frequency-constrained unit commitment (FCUC) is proposed to address this challenge.
Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods
We first present the asymptotic distribution for the Polyak-Ruppert-averaging type Kiefer-Wolfowitz (AKW) estimators, whose asymptotic covariance matrices depend on the distribution of search directions and the function-value query complexity.
Variance Reduction on General Adaptive Stochastic Mirror Descent
In this work, we investigate the idea of variance reduction by studying its properties with general adaptive mirror descent algorithms in nonsmooth nonconvex finite-sum optimization problems.
Hybrid Imitation Learning for Real-Time Service Restoration in Resilient Distribution Systems
Self-healing capability is one of the most critical factors for a resilient distribution system, which requires intelligent agents to automatically perform restorative actions online, including network reconfiguration and reactive power dispatch.
Pathological Visual Question Answering
To deal with the issue that a publicly available pathology VQA dataset is lacking, we create PathVQA dataset.
Deep Active Learning for Solvability Prediction in Power Systems
Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism.
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans
Besides, these works require a large number of CTs to train accurate diagnosis models, which are difficult to obtain.
COVID-CT-Dataset: A CT Scan Dataset about COVID-19
Using this dataset, we develop diagnosis methods based on multi-task learning and self-supervised learning, that achieve an F1 of 0. 90, an AUC of 0. 98, and an accuracy of 0. 89.
PathVQA: 30000+ Questions for Medical Visual Question Answering
To achieve this goal, the first step is to create a visual question answering (VQA) dataset where the AI agent is presented with a pathology image together with a question and is asked to give the correct answer.
Robust Synthesis of Wind Turbine Generators to Support Microgrid Frequency Considering Linearization-Induced Uncertainty
The capability to switch between grid-connected and islanded modes has promoted adoption of microgrid technology for powering remote locations.
HRank: Filter Pruning using High-Rank Feature Map
The principle behind our pruning is that low-rank feature maps contain less information, and thus pruned results can be easily reproduced.
Approximating Trajectory Constraints with Machine Learning -- Microgrid Islanding with Frequency Constraints
In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem.
Training convolutional neural networks with cheap convolutions and online distillation
The large memory and computation consumption in convolutional neural networks (CNNs) has been one of the main barriers for deploying them on resource-limited systems.
Reconstruction of Natural Visual Scenes from Neural Spikes with Deep Neural Networks
The SID is an end-to-end decoder with one end as neural spikes and the other end as images, which can be trained directly such that visual scenes are reconstructed from spikes in a highly accurate fashion.
First-order Newton-type Estimator for Distributed Estimation and Inference
The key component in our method is the proposal of a computationally efficient estimator of $\Sigma^{-1} w$, where $\Sigma$ is the population Hessian matrix and $w$ is any given vector.
Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks
When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to higher visual cortex.
Quantile Regression Under Memory Constraint
This paper proposes a computationally efficient method, which only requires an initial QR estimator on a small batch of data and then successively refines the estimator via multiple rounds of aggregations.
The ALMA Early Science view of FUor/EXor objects. I. Through the looking-glass of V2775 Ori
We report the detection of a marginally resolved circumstellar disc in the ALMA continuum with an integrated flux of $106 \pm 2$ mJy, characteristic radius of $\sim$ 30 au, inclination of $14. 0^{+7. 8}_{-14. 5}$ deg, and is oriented nearly face-on with respect to the plane of the sky.
Solar and Stellar Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Statistical Inference for Model Parameters in Stochastic Gradient Descent
Second, for high-dimensional linear regression, using a variant of the SGD algorithm, we construct a debiased estimator of each regression coefficient that is asymptotically normal.
