Search Results for author:
Found 106 papers, 15 papers with code
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
Referring Image Segmentation Using Text Supervision
Hence, we propose a novel weakly-supervised RIS framework to formulate the target localization problem as a classification process to differentiate between positive and negative text expressions.
PolSF: PolSAR image dataset on San Francisco
In this paper, we have collected five open polarimetric SAR images, which are images of the San Francisco area.
C-CNN: Contourlet Convolutional Neural Networks
Second, the spatial-spectral feature fusion strategy is designed to incorporate the spectral features into CNN architecture.
LINFA: a Python library for variational inference with normalizing flow and annealing
Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions.
Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space
Most few-shot image classification methods are trained based on tasks.
Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds
To address the challenges of long-tailed classification, researchers have proposed several approaches to reduce model bias, most of which assume that classes with few samples are weak classes.
Ranked #19 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
ZC3: Zero-Shot Cross-Language Code Clone Detection
In this paper, we propose a novel method named ZC3 for Zero-shot Cross-language Code Clone detection.
Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models
To reduce the computational cost without sacrificing inferential accuracy, we propose Normalizing Flow with Adaptive Surrogate (NoFAS), an optimization strategy that alternatively updates the normalizing flow parameters and surrogate model parameters.
Sentiment Analysis and Effect of COVID-19 Pandemic using College SubReddit Data
Conclusions: Our study results are consistent with the findings in the literature on the negative impacts of the pandemic on people's emotions and psychological states.
AdaAnn: Adaptive Annealing Scheduler for Probability Density Approximation
We demonstrate the computational efficiency of the AdaAnn scheduler for variational inference with normalizing flows on a number of examples, including density approximation and parameter estimation for dynamical systems.
Has Sentiment Returned to the Pre-pandemic Level? A Sentiment Analysis Using U.S. College Subreddit Data from 2019 to 2022
As impact of COVID-19 pandemic winds down, both individuals and society gradually return to pre-pandemic activities.
Analysis of Thompson Sampling for Graphical Bandits Without the Graphs
To fill this gap, we propose a variant of Thompson Sampling, that attains the optimal regret in the directed setting within a logarithmic factor.
Whiteout: Gaussian Adaptive Noise Regularization in Deep Neural Networks
Noise injection (NI) is an efficient technique to mitigate over-fitting in neural networks (NNs).
UCBoost: A Boosting Approach to Tame Complexity and Optimality for Stochastic Bandits
We show that UCBoost($D$) enjoys $O(1)$ complexity for each arm per round as well as regret guarantee that is $1/e$-close to that of the kl-UCB algorithm.
Learning Intrinsic Sparse Structures within Long Short-Term Memory
This work aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the sizes of basic structures within LSTM units, including input updates, gates, hidden states, cell states and outputs.
A Change-Detection based Framework for Piecewise-stationary Multi-Armed Bandit Problem
The multi-armed bandit problem has been extensively studied under the stationary assumption.
Information Directed Sampling for Stochastic Bandits with Graph Feedback
We consider stochastic multi-armed bandit problems with graph feedback, where the decision maker is allowed to observe the neighboring actions of the chosen action.
Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks
We study the stochastic multi-armed bandit (MAB) problem in the presence of side-observations across actions that occur as a result of an underlying network structure.
Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Development
Based on the knowledge of these meta-stable patterns, we suggested an interpolation scheme with only a concrete and finite set of known patterns to accurately predict the ground and excited state properties of the entire dynamics trajectories.
Polarimetric Hierarchical Semantic Model and Scattering Mechanism Based PolSAR Image Classification
For polarimetric SAR (PolSAR) image classification, it is a challenge to classify the aggregated terrain types, such as the urban area, into semantic homogenous regions due to sharp bright-dark variations in intensity.
PANDA: AdaPtive Noisy Data Augmentation for Regularization of Undirected Graphical Models
We propose an AdaPtive Noise Augmentation (PANDA) technique to regularize the estimation and construction of undirected graphical models.
SUSAN: Segment Unannotated image Structure using Adversarial Network
However, the supervised training of highly efficient CNNs with deeper structure and more network parameters requires a large number of training images and paired tissue masks.
SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction
The undersampled images are generated by a fixed undersampling pattern in the training, and the trained network is then applied to reconstruct new images acquired with the same pattern in the inference.
Construction of Differentially Private Empirical Distributions from a low-order Marginals Set through Solving Linear Equations with l2 Regularization
We introduce a new algorithm, Construction of dIfferentially Private Empirical Distributions from a low-order marginals set tHrough solving linear Equations with l2 Regularization (CIPHER), that produces differentially private empirical joint distributions from a set of low-order marginals.
Ling@CASS Solution to the NLP-TEA CGED Shared Task 2018
When using inputs as simple as Chinese characters, the ensembled system achieves a precision at 86. 56{\%} in the detection of erroneous sentences, and a precision at 51. 53{\%} in the correction of errors of Selection and Missing types.
CMMC-BDRC Solution to the NLP-TEA-2018 Chinese Grammatical Error Diagnosis Task
Chinese grammatical error diagnosis is an important natural language processing (NLP) task, which is also an important application using artificial intelligence technology in language education.
An End-to-End Joint Unsupervised Learning of Deep Model and Pseudo-Classes for Remote Sensing Scene Representation
Finally, joint learning of the pseudo-center loss and the pseudo softmax loss which is formulated with the samples and the pseudo labels is developed for unsupervised remote sensing scene representation to obtain discriminative representations from the scenes.
Data Poisoning Attacks on Stochastic Bandits
Then we study a form of online attacks on bandit algorithms and propose an adaptive attack strategy against any bandit algorithm without the knowledge of the bandit algorithm.
SketchGAN: Joint Sketch Completion and Recognition With Generative Adversarial Network
Hand-drawn sketch recognition is a fundamental problem in computer vision, widely used in sketch-based image and video retrieval, editing, and reorganization.
SpecNet: Spectral Domain Convolutional Neural Network
Often, these feature maps mimic natural photographs in the sense that their energy is concentrated in the spectral domain.
Semi-supervised Complex-valued GAN for Polarimetric SAR Image Classification
However, their interpretation faces some challenges, e. g., deficiency of labeled data, inadequate utilization of data information and so on.
Pixel DAG-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification
Exploiting rich spatial and spectral features contributes to improve the classification accuracy of hyperspectral images (HSIs).
PolSAR Image Classification based on Polarimetric Scattering Coding and Sparse Support Matrix Machine
In this paper, a novel POLSAR image classification method is proposed based on polarimetric scattering coding and sparse support matrix machine.
A Survey of Deep Learning-based Object Detection
Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class.
A Dynamic Analysis of Energy Storage with Renewable and Diesel Generation using Volterra Equations
A dynamic evolutionary model based on the first kind Volterra integral equation is used in both cases.
A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning
To enable the knowledge sharing between related tasks, we creatively propose a Multi-Task Learning (MTL) framework to learn two related tasks in code completion jointly.
Bayesian convolutional neural network based MRI brain extraction on nonhuman primates
The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine.
Deep Learning and Bayesian Deep Learning Based Gender Prediction in Multi-Scale Brain Functional Connectivity
Hence, in this study we propose to predict gender from multiple scales of brain FC with deep learning, which can extract full FC patterns as features.
Contextual Bandits with Side-Observations
We show that this asymptotically optimal regret is upper-bounded as $O\left(|\chi(\mathcal{G})|\log T\right)$, where $|\chi(\mathcal{G})|$ is the domination number of $\mathcal{G}$.
MultiResolution Attention Extractor for Small Object Detection
Small objects are difficult to detect because of their low resolution and small size.
A modified axiomatic foundation of the analytic hierarchy process
The phenomenon of ranking reversal is revisited from a theoretical viewpoint under the modified axiomatic foundation.
A Partially Observable MDP Approach for Sequential Testing for Infectious Diseases such as COVID-19
In this paper, we show that the testing problem can be cast as a sequential learning-based resource allocation problem with constraints, where the input to the problem is provided by a time-varying social contact graph obtained through various contact tracing tools.
SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches
Sketch-based image retrieval (SBIR) has been a popular research topic in recent years.
Selective Cascade of Residual ExtraTrees
We propose a novel tree-based ensemble method named Selective Cascade of Residual ExtraTrees (SCORE).
Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction
The network is carefully constructed by stacking multiple attention units in depth to fully model dense interactions over token-label spaces, in which two basic attention units are proposed to explicitly capture fine-grained correlations across different modalities (e. g., token-to-token and labelto-token).
Multi-Armed Bandits with Dependent Arms
We study a variant of the classical multi-armed bandit problem (MABP) which we call as Multi-Armed Bandits with dependent arms.
Search for the reaction $e^{+}e^{-} \rightarrow π^{+}π^{-} χ_{cJ}$ and a charmonium-like structure decaying to $χ_{cJ}π^{\pm}$ between 4.18 and 4.60 GeV
We search for the process $e^{+}e^{-}\rightarrow \pi ^{+}\pi ^{-} \chi_{cJ}$ ($J=0, 1, 2$) and for a charged charmonium-like state in the $\pi ^{\pm} \chi_{cJ}$ subsystem.
High Energy Physics - Experiment
Cross section measurement of $e^+e^- \to p\bar{p}η$ and $e^+e^- \to p\bar{p}ω$ at center-of-mass energies between 3.773 GeV and 4.6 GeV
Based on $14. 7~\textrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected with the BESIII detector at the BEPCII collider at 17 different center-of-mass energies between $3. 7730~\textrm{GeV}$ and $4. 5995~\textrm{GeV}$, Born cross sections of the two processes $e^+e^- \to p\bar{p}\eta$ and $e^+e^- \to p\bar{p}\omega$ are measured for the first time.
High Energy Physics - Experiment
Measurements of the center-of-mass energies of $e^{+}e^{-}$ collisions at BESIII
During the 2016-17 and 2018-19 running periods, the BESIII experiment collected 7. 5~fb$^{-1}$ of $e^+e^-$ collision data at center-of-mass energies ranging from 4. 13 to 4. 44 GeV.
High Energy Physics - Experiment
Model independent determination of the spin of the $Ω^{-}$ and its polarization alignment in $ψ(3686)\rightarrowΩ^{-}\barΩ^{+}$
We present an analysis of the process $\psi(3686) \to \Omega^- \bar{\Omega}^+$ ($\Omega^-\to K^-\Lambda$, $\bar{\Omega}^+\to K^+\bar{\Lambda}$, $\Lambda\to p\pi^-$, $\bar{\Lambda}\to \bar{p}\pi^+$) based on a data set of $448\times 10^6$ $\psi(3686)$ decays collected with the BESIII detector at the BEPCII electron-positron collider.
High Energy Physics - Experiment
Measurement of the absolute branching fractions for purely leptonic $D_s^+$ decays
Constraining our measurement to the Standard Model expectation of lepton universality ($R=9. 75$), we find the more precise results $\cal B(D_s^+\to \tau^+\nu_\tau) = (5. 22\pm0. 10\pm 0. 14)\times10^{-2}$ and $A_{\it CP}(\tau^\pm\nu_\tau) = (-0. 1\pm1. 9\pm1. 0)\%$.
High Energy Physics - Experiment
Evidence for $Z_{c}^{\pm}$ decays into the $ρ^{\pm} η_{c}$ final state
We study $e^{+}e^{-}$ collisions with a $\pi^{+}\pi^{-}\pi^{0}\eta_{c}$ final state using data samples collected with the BESIII detector at center-of-mass energies $\sqrt{s}=4. 226$, $4. 258$, $4. 358$, $4. 416$, and $4. 600$ GeV.
High Energy Physics - Experiment
Effective and Fast: A Novel Sequential Single Path Search for Mixed-Precision Quantization
Since model quantization helps to reduce the model size and computation latency, it has been successfully applied in many applications of mobile phones, embedded devices and smart chips.
MWQ: Multiscale Wavelet Quantized Neural Networks
Inspired by the characteristics of images in the frequency domain, we propose a novel multiscale wavelet quantization (MWQ) method.
One Model for All Quantization: A Quantized Network Supporting Hot-Swap Bit-Width Adjustment
When the precision of quantization is adjusted, it is necessary to fine-tune the quantized model or minimize the quantization noise, which brings inconvenience in practical applications.
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles
Computational virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery.
Regret theory under fear of the unknown
The application to the medical decision making with an unknown risk is studied and the effects of the fear function are investigated.
Dynamic Control for Random Access in Deadline-Constrained Broadcasting
To overcome the infeasibility in obtaining an optimal or near-optimal scheme from the POMDP framework, we investigate the behaviors of the optimal scheme for two extreme cases in the MDP framework, and leverage intuition gained from these behaviors to propose a heuristic scheme for the realistic environment with TDR close to the maximum achievable TDR in the idealized environment.
Noise-Augmented Privacy-Preserving Empirical Risk Minimization with Dual-purpose Regularizer and Privacy Budget Retrieval and Recycling
When the target regularization is for variable selection, we propose a new regularizer that achieves both privacy and sparsity guarantees simultaneously.
Vis-TOP: Visual Transformer Overlay Processor
Vis-TOP summarizes the characteristics of all visual Transformer models and implements a three-layer and two-level transformation structure that allows the model to be switched or changed freely without changing the hardware architecture.
New Generation Deep Learning For Video Object Dection:A Survery
Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used.
GAFnet: Group Attention Fusion Network for PAN and MS Image High-Resolution Classification
Second, the group spatial attention and group spectral attention modules are proposed to extract image features.
Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization
With our strong online data augmentation strategy, the proposed SSReg shows the potential of self-supervised learning without using negative pairs and it can significantly improve the performance of self-supervised speaker representation learning with a simple Siamese network architecture.
Transformers Meet Visual Learning Understanding: A Comprehensive Review
Three image tasks and two video tasks of computer vision are investigated.
Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networks
While most of these methods focus on designing novel reconstruction networks or new training strategies for a given undersampling pattern, e. g., Cartesian undersampling or Non-Cartesian sampling, to date, there is limited research aiming to learn and optimize k-space sampling strategies using deep neural networks.
Compact Model Training by Low-Rank Projection with Energy Transfer
In this paper, we devise a new training method, low-rank projection with energy transfer (LRPET), that trains low-rank compressed networks from scratch and achieves competitive performance.
Adaptive Noisy Data Augmentation for Regularized Estimation and Inference in Generalized Linear Models
We propose the AdaPtive Noise Augmentation (PANDA) procedure to regularize the estimation and inference of generalized linear models (GLMs).
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design.
Efficient Reinforcement Learning from Demonstration Using Local Ensemble and Reparameterization with Split and Merge of Expert Policies
The current work on reinforcement learning (RL) from demonstrations often assumes the demonstrations are samples from an optimal policy, an unrealistic assumption in practice.
A Dual-fusion Semantic Segmentation Framework With GAN For SAR Images
With the better representation capability of optical images, we propose to enrich SAR images with generated optical images via the generative adversative network (GAN) trained by numerous SAR and optical images.
dual unet:a novel siamese network for change detection with cascade differential fusion
Change detection (CD) of remote sensing images is to detect the change region by analyzing the difference between two bitemporal images.
A Survey on Natural Language Processing for Programming
Natural language processing for programming aims to use NLP techniques to assist programming.
Delving into Semantic Scale Imbalance
Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations.
Deep-learning-based on-chip rapid spectral imaging with high spatial resolution
Spectral imaging extends the concept of traditional color cameras to capture images across multiple spectral channels and has broad application prospects.
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search
It is computationally expensive to determine which LL Pareto weight in the LL Pareto weight set is the most appropriate for each UL solution.
Syntax and Domain Aware Model for Unsupervised Program Translation
The experimental results on function translation tasks between Python, Java, and C++ show that SDA-Trans outperforms many large-scale pre-trained models, especially for unseen language translation.
Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Records
We use normalizing flows (NF), a family of deep generative models, to estimate the probability density of a dataset with differential privacy (DP) guarantees, from which privacy-preserving synthetic data are generated.
Noise-Augmented $\ell_0$ Regularization of Tensor Regression with Tucker Decomposition
We propose a method named NA$_0$CT$^2$ (Noise Augmentation for $\ell_0$ regularization on Core Tensor in Tucker decomposition) to regularize the parameters in tensor regression (TR), coupled with Tucker decomposition.
A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning
While these models can be highly accurate in prediction, results obtained from these models with the use of sensitive data may be susceptible to privacy attacks.
Multi-model fusion for Aerial Vision and Dialog Navigation based on human attention aids
Drones have been widely used in many areas of our daily lives.
Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction
Our method is applied to multi-coil MRI and quantitative MRI reconstruction, leveraging the domain-conditioned diffusion model within the frequency and parameter domains.
DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning
Differential privacy (DP) is the state-of-the-art framework for guaranteeing privacy for individuals when releasing aggregated statistics or building statistical/machine learning models from data.
LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch
Therefore the pruning strategy can gradually prune the network and automatically determine the appropriate pruning rates for each layer.
Changes-Aware Transformer: Learning Generalized Changes Representation
After refinement, the changed pixels in the difference feature space are closer to each other, which facilitates change detection.
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
Orthogonal Uncertainty Representation of Data Manifold for Robust Long-Tailed Learning
The disadvantage is that these methods generally pursue models with balanced class accuracy on the data manifold, while ignoring the ability of the model to resist interference.
Large Language Model-Aware In-Context Learning for Code Generation
Large language models (LLMs) have shown impressive in-context learning (ICL) ability in code generation.
Data-Centric Long-Tailed Image Recognition
In the context of the long-tail scenario, models exhibit a strong demand for high-quality data.
Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification
Visible-infrared person re-identification (VI-ReID) aims to search the same pedestrian of interest across visible and infrared modalities.
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)
We also prove that MUST. WO is equivalent to sampling with replacement in PA.
Interpersonal Relationship Analysis with Dyadic EEG Signals via Learning Spatial-Temporal Patterns
Interpersonal relationship quality is pivotal in social and occupational contexts.
Efficient approximation of Earth Mover's Distance Based on Nearest Neighbor Search
Earth Mover's Distance (EMD) is an important similarity measure between two distributions, used in computer vision and many other application domains.
A match made in consistency heaven: when large language models meet evolutionary algorithms
Pre-trained large language models (LLMs) have powerful capabilities for generating creative natural text.
Geometric Prior Guided Feature Representation Learning for Long-Tailed Classification
In this work, we propose to leverage the geometric information of the feature distribution of the well-represented head class to guide the model to learn the underlying distribution of the tail class.
TNANet: A Temporal-Noise-Aware Neural Network for Suicidal Ideation Prediction with Noisy Physiological Data
Current methods predominantly focus on image and text data or address artificially introduced noise, neglecting the complexities of natural noise in time-series analysis.
Recasting Regional Lighting for Shadow Removal
Based on this observation, we propose to condition the restoration of attenuated textures on the corrected local lighting in the shadow region.
DeepEMC-T2 Mapping: Deep Learning-Enabled T2 Mapping Based on Echo Modulation Curve Modeling
The modified U-Net employs several new features to improve the accuracy of T2/PD estimation.
Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks
Diffusion models trained on large-scale datasets have achieved remarkable progress in image synthesis.
RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts
In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learning
Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging.
Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments
Automated generation of feedback on programming assignments holds significant benefits for programming education, especially when it comes to advanced assignments.
Exploring and Evaluating Hallucinations in LLM-Powered Code Generation
The rise of Large Language Models (LLMs) has significantly advanced many applications on software engineering tasks, particularly in code generation.
Map Optical Properties to Subwavelength Structures Directly via a Diffusion Model
The inverse design methods proposed for these subwavelength structures are vital to the development of new photonic devices.
