Search Results for author:
Found 38 papers, 11 papers with code
LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion
This paper addresses the challenge of perceiving complete object shapes through visual perception.
Docs2KG: Unified Knowledge Graph Construction from Heterogeneous Documents Assisted by Large Language Models
Even for a conservative estimate, 80% of enterprise data reside in unstructured files, stored in data lakes that accommodate heterogeneous formats.
Alterations of electrocortical activity during hand movements induced by motor cortex glioma
Glioma cells can reshape functional neuronal networks by hijacking neuronal synapses, leading to partial or complete neurological dysfunction.
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness Perspective
Graph representation learning has emerged as a machine learning go-to technique, outperforming traditional tabular view of data across many domains.
Rethinking The Uniformity Metric in Self-Supervised Learning
Uniformity plays an important role in evaluating learned representations, providing insights into self-supervised learning.
Ensemble linear interpolators: The role of ensembling
We introduce the multiplier-bootstrap-based bagged least square estimator, which can then be formulated as an average of the sketched least square estimators.
Gradient descent in matrix factorization: Understanding large initialization
Gradient Descent (GD) has been proven effective in solving various matrix factorization problems.
GeoVLN: Learning Geometry-Enhanced Visual Representation with Slot Attention for Vision-and-Language Navigation
Technically, we introduce a two-stage module that combine local slot attention and CLIP model to produce geometry-enhanced representation from such input.
Bounded Projection Matrix Approximation with Applications to Community Detection
Community detection is an important problem in unsupervised learning.
Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules
In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices.
Variance-aware robust reinforcement learning with linear function approximation under heavy-tailed rewards
Building upon AdaOFUL, we propose VARA for linear MDPs, which achieves a tighter variance-aware regret bound of $\widetilde{O}(d\sqrt{HG^*K})$.
Statistical Analysis of Karcher Means for Random Restricted PSD Matrices
Non-asymptotic statistical analysis is often missing for modern geometry-aware machine learning algorithms due to the possibly intricate non-linear manifold structure.
Sketched Ridgeless Linear Regression: The Role of Downsampling
We identify the optimal sketching size that minimizes out-of-sample prediction risks and demonstrate that the optimally sketched estimator exhibits stabler risk curves, eliminating the peaks of those for the full-sample estimator.
Quadratic Matrix Factorization with Applications to Manifold Learning
Motivated by manifold learning problems, this paper proposes a quadratic matrix factorization (QMF) framework to learn the curved manifold on which the dataset lies.
Online Linearized LASSO
Sparse regression has been a popular approach to perform variable selection and enhance the prediction accuracy and interpretability of the resulting statistical model.
Local Slot Attention for Vision-and-Language Navigation
Since the attention mechanism in the transformer architecture can better integrate inter- and intra-modal information of vision and language.
Distributed Sparse Multicategory Discriminant Analysis
This paper proposes a convex formulation for sparse multicategory linear discriminant analysis and then extend it to the distributed setting when data are stored across multiple sites.
The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations
The image-level performance of COVID-19 prescreening model in the China-Spain multicenter study achieved an AUC of 0. 913 (95% CI, 0. 898-0. 927), with a sensitivity of 0. 695 (95% CI, 0. 643-0. 748), a specificity of 0. 904 (95% CI, 0. 891 -0. 919), an accuracy of 0. 875(0. 861-0. 889), and a F1 of 0. 611(0. 568-0. 655).
Self-supervised 3D Human Mesh Recovery from Noisy Point Clouds
However, Chamfer distance is quite sensitive to noise and outliers, thus could be unreliable to assign correspondences.
Rapid COVID-19 Risk Screening by Eye-region Manifestations
It is still nontrivial to develop a new fast COVID-19 screening method with the easier access and lower cost, due to the technical and cost limitations of the current testing methods in the medical resource-poor districts.
Physical Layer Security Enhancement With Reconfigurable Intelligent Surface-Aided Networks
Reconfigurable intelligent surface (RIS)-aided wireless communications have drawn significant attention recently.
Information Theory Signal Processing Information Theory
Hybrid Template Canonical Correlation Analysis Method for Enhancing SSVEP Recognition under data-limited Condition
In this study, an advanced CCA-based algorithn called hybrid template canonical correlation analysis (HTCCA) was proposed to improve the performance of brain-computer interface (BCI) based on steady state visual evoked potential (SSVEP) uuder data-linited condition.
Learning to Augment Expressions for Few-shot Fine-grained Facial Expression Recognition
Considering the phenomenon of uneven data distribution and lack of samples is common in real-world scenarios, we further evaluate several tasks of few-shot expression learning by virtue of our F2ED, which are to recognize the facial expressions given only few training instances.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Bayesian high-dimensional linear regression with generic spike-and-slab priors
Spike-and-slab priors are popular Bayesian solutions for high-dimensional linear regression problems.
A unified framework of predicting binary interestingness of images based on discriminant correlation analysis and multiple kernel learning
In the modern content-based image retrieval systems, there is an increasingly interest in constructing a computationally effective model to predict the interestingness of images since the measure of image interestingness could improve the human-centered search satisfaction and the user experience in different applications.
DeepEnFM: Deep neural networks with Encoder enhanced Factorization Machine
Instead of learning the cross features directly, DeepEnFM adopts the Transformer encoder as a backbone to align the feature embeddings with the clues of other fields.
Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention
Heart disease is one of the most common diseases causing morbidity and mortality.
A Fine-Grained Facial Expression Database for End-to-End Multi-Pose Facial Expression Recognition
First, we create a new facial expression dataset of more than 200k images with 119 persons, 4 poses and 54 expressions.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
Iteratively Reweighted $\ell_1$-Penalized Robust Regression
This paper investigates tradeoffs among optimization errors, statistical rates of convergence and the effect of heavy-tailed errors for high-dimensional robust regression with nonconvex regularization.
Question Guided Modular Routing Networks for Visual Question Answering
Particularly, The QGMRN is composed of visual, textual and routing network.
Exact Cluster Recovery via Classical Multidimensional Scaling
Classical multidimensional scaling is an important dimension reduction technique.
User-Friendly Covariance Estimation for Heavy-Tailed Distributions
We offer a survey of recent results on covariance estimation for heavy-tailed distributions.
Methodology Statistics Theory Statistics Theory
Modified Multidimensional Scaling and High Dimensional Clustering
Multidimensional scaling is an important dimension reduction tool in statistics and machine learning.
Robust Sparse Reduced Rank Regression in High Dimensions
We propose robust sparse reduced rank regression for analyzing large and complex high-dimensional data with heavy-tailed random noise.
VECTORIZATION METHODS IN RECOMMENDER SYSTEM
The most used recommendation method is collaborative filtering, and the key part of collaborative filtering is to compute the similarity.
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
Our proposal is computationally tractable and produces an estimator that achieves the oracle rate of convergence.
Adaptive Huber Regression
We establish a sharp phase transition for robust estimation of regression parameters in both low and high dimensions: when $\delta \geq 1$, the estimator admits a sub-Gaussian-type deviation bound without sub-Gaussian assumptions on the data, while only a slower rate is available in the regime $0<\delta< 1$.
Statistics Theory Methodology Statistics Theory
Graphical Nonconvex Optimization for Optimal Estimation in Gaussian Graphical Models
Our proposal is computationally tractable and produces an estimator that achieves the oracle rate of convergence.
