Search Results for author:
Found 57 papers, 16 papers with code
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation
Furthermore, we introduce a privacy-preserving decoder to mitigate user privacy leakage during knowledge transfer.
Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation
Building upon this insight, we propose a disentangled encoder that focuses on disentangling user and item embeddings into interest and social influence embeddings.
Merino: Entropy-driven Design for Generative Language Models on IoT Devices
Generative Large Language Models (LLMs) stand as a revolutionary advancement in the modern era of artificial intelligence (AI).
ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models
This paper explores a new post-hoc training-free compression paradigm for compressing Large Language Models (LLMs) to facilitate their wider adoption in various computing environments.
Cooperative Network Learning for Large-Scale and Decentralized Graphs
Here, we introduce a Cooperative Network Learning (CNL) framework to ensure secure graph computing for various graph tasks.
PB-LLM: Partially Binarized Large Language Models
This paper explores network binarization, a radical form of quantization, compressing model weights to a single bit, specifically for Large Language Models (LLMs) compression.
Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes
Despite the recent successes of vanilla Graph Neural Networks (GNNs) on various tasks, their foundation on pairwise networks inherently limits their capacity to discern latent higher-order interactions in complex systems.
Ranked #1 on
Node Classification
on Texas
Masked Cross-image Encoding for Few-shot Segmentation
Few-shot segmentation (FSS) is a dense prediction task that aims to infer the pixel-wise labels of unseen classes using only a limited number of annotated images.
Ranked #24 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
Imbalanced Large Graph Learning Framework for FPGA Logic Elements Packing Prediction
With imbalanced distribution of packed and unpacked logic elements, we further propose techniques such as graph oversampling and mini-batch training for this imbalanced learning task in large circuit graphs.
Multiobjective Hydropower Reservoir Operation Optimization with Transformer-Based Deep Reinforcement Learning
Due to shortage of water resources and increasing water demands, the joint operation of multireservoir systems for balancing power generation, ecological protection, and the residential water supply has become a critical issue in hydropower management.
Influential Simplices Mining via Simplicial Convolutional Network
It can tackle higher-order tasks by leveraging novel higher-order presentations: hierarchical bipartite graphs and higher-order hierarchical (HoH) Laplacians, where targeted simplices are grouped into a hub set and can interact with other simplices.
DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images
Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness.
RPTQ: Reorder-based Post-training Quantization for Large Language Models
In this paper, we identify that the challenge in quantizing activations in LLMs arises from varying ranges across channels, rather than solely the presence of outliers.
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures.
Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration
Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data.
DynamicLight: Dynamically Tuning Traffic Signal Duration with DRL
Deep reinforcement learning (DRL) is becoming increasingly popular in implementing traffic signal control (TSC).
Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.
Lightweight Vision Transformer with Cross Feature Attention
Recent advances in vision transformers (ViTs) have achieved great performance in visual recognition tasks.
Self-attention on Multi-Shifted Windows for Scene Segmentation
Scene segmentation in images is a fundamental yet challenging problem in visual content understanding, which is to learn a model to assign every image pixel to a categorical label.
Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting
This paper proposes a general unsupervised approach, named \textbf{ConClu}, to perform the learning of point-wise and global features by jointly leveraging point-level clustering and instance-level contrasting.
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control
Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem.
Efficient Pressure: Improving efficiency for signalized intersections
Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC) problem.
PTQ4ViT: Post-Training Quantization Framework for Vision Transformers with Twin Uniform Quantization
We observe the distributions of activation values after softmax and GELU functions are quite different from the Gaussian distribution.
InfoBehavior: Self-supervised Representation Learning for Ultra-long Behavior Sequence via Hierarchical Grouping
Typically, the risk can be identified by jointly considering product content (e. g., title and image) and seller behavior.
TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data
Among ubiquitous multimodal data in the real world, text is the modality generated by human, while image reflects the physical world honestly.
Clothing Status Awareness for Long-Term Person Re-Identification
This work argues that these approaches in fact are not aware of clothing status (i. e., change or no-change) of a pedestrian.
PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning
Second, the extra unlabeled samples are employed to transfer the knowledge from base classes to novel classes through contrastive learning.
Field-wise Learning for Multi-field Categorical Data
We present a model that utilizes linear models with variance and low-rank constraints, to help it generalize better and reduce the number of parameters.
Multi-layer Feature Aggregation for Deep Scene Parsing Models
The proposed module can auto-select the intermediate visual features to correlate the spatial and semantic information.
Ranked #47 on
Semantic Segmentation
on NYU Depth v2
Dual Attention on Pyramid Feature Maps for Image Captioning
In this paper, we propose to apply dual attention on pyramid image feature maps to fully explore the visual-semantic correlations and improve the quality of generated sentences.
A Framework of Learning Through Empirical Gain Maximization
Furthermore, it is shown that several well-known robust nonconvex regression paradigms, such as Tukey regression and truncated least square regression, can be reformulated into this new framework.
A Statistical Learning Assessment of Huber Regression
Third, with an adaptive choice of the scale parameter, we demonstrate that Huber regression estimators can be asymptotic mean regression calibrated under $(1+\epsilon)$-moment conditions ($\epsilon>0$).
Optimal Rates of Distributed Regression with Imperfect Kernels
Then we perform a leave one out analysis of the kernel ridge regression and bias corrected kernel ridge regression, which in combination with the aforementioned framework allows us to derive sharp error bounds and capacity independent optimal rates for the associated distributed kernel regression algorithms.
TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization with Few Labeled Samples
The challenges of high intra-class variance yet low inter-class fluctuations in fine-grained visual categorization are more severe with few labeled samples, \textit{i. e.,} Fine-Grained categorization problems under the Few-Shot setting (FGFS).
ShadowSync: Performing Synchronization in the Background for Highly Scalable Distributed Training
Recommendation systems are often trained with a tremendous amount of data, and distributed training is the workhorse to shorten the training time.
SBSGAN: Suppression of Inter-Domain Background Shift for Person Re-Identification
We observe that if backgrounds in the training and testing datasets are very different, it dramatically introduces difficulties to extract robust pedestrian features, and thus compromises the cross-domain person re-ID performance.
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification
A novel low-rank pairwise bilinear pooling operation is proposed to capture the nuanced differences between the support and query images for learning an effective distance metric.
Model Adaptation via Model Interpolation and Boosting for Web Search Ranking
This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm.
Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points
In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels.
Online Learning for Supervised Dimension Reduction
The purpose of this paper is to propose new online learning approaches for supervised dimension reduction.
Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning
Unlike traditional deep bilinear networks for fine-grained classification, which adopt the self-bilinear pooling to capture the subtle features of images, the proposed model uses a novel pairwise bilinear pooling to compare the nuanced differences between base images and query images for learning a deep distance metric.
Fast Registration for cross-source point clouds by using weak regional affinity and pixel-wise refinement
Accurate and fast registration of cross-source 3D point clouds from different sensors is an emerged research problem in computer vision.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Overlapping Sliced Inverse Regression for Dimension Reduction
The new algorithm, called overlapping sliced inverse regression (OSIR), is able to estimate the effective dimension reduction space and determine the number of effective factors more accurately.
Multi-pseudo Regularized Label for Generated Data in Person Re-Identification
Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work is a set of weight-based values each individual of which is a number in (0, 1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data.
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
Regularization kernel network is an effective and widely used method for nonlinear regression analysis.
Robust Guided Image Filtering
In this paper, we propose a general framework for Robust Guided Image Filtering (RGIF), which contains a data term and a smoothness term, to solve the two issues mentioned above.
A coarse-to-fine algorithm for registration in 3D street-view cross-source point clouds
In this paper, different from previous ICP-based methods, and from a statistic view, we propose a effective coarse-to-fine algorithm to detect and register a small scale SFM point cloud in a large scale Lidar point cloud.
A Systematic Approach for Cross-source Point Cloud Registration by Preserving Macro and Micro Structures
We propose a systematic approach for registering cross-source point clouds.
Bias Correction for Regularized Regression and its Application in Learning with Streaming Data
We propose an approach to reduce the bias of ridge regression and regularization kernel network.
Data Driven Robust Image Guided Depth Map Restoration
Depth maps captured by modern depth cameras such as Kinect and Time-of-Flight (ToF) are usually contaminated by missing data, noises and suffer from being of low resolution.
LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks
In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images.
Robust High Quality Image Guided Depth Upsampling
A popular solution is upsampling the obtained noisy low resolution depth map with the guidance of the companion high resolution color image.
A new approach for physiological time series
We developed a new approach for the analysis of physiological time series.
Multiple Authors Detection: A Quantitative Analysis of Dream of the Red Chamber
Inspired by the authorship controversy of Dream of the Red Chamber and the application of machine learning in the study of literary stylometry, we develop a rigorous new method for the mathematical analysis of authorship by testing for a so-called chrono-divide in writing styles.
Consistency Analysis of an Empirical Minimum Error Entropy Algorithm
The error entropy consistency, which requires the error entropy of the learned function to approximate the minimum error entropy, is shown to be always true if the bandwidth parameter tends to 0 at an appropriate rate.
Localized Sliced Inverse Regression
We developed localized sliced inverse regression for supervised dimension reduction.
