Search Results for author:
Found 38 papers, 11 papers with code
AdaFV: Rethinking of Visual-Language alignment for VLM acceleration
The success of VLMs often relies on the dynamic high-resolution schema that adaptively augments the input images to multiple crops, so that the details of the images can be retained.
Sharper Error Bounds in Late Fusion Multi-view Clustering Using Eigenvalue Proportion
Multi-view clustering (MVC) aims to integrate complementary information from multiple views to enhance clustering performance.
k-HyperEdge Medoids for Clustering Ensemble
In this paper, the clustering ensemble is formulated as a k-HyperEdge Medoids discovery problem and a clustering ensemble method based on k-HyperEdge Medoids that considers the characteristics of the above two types of clustering ensemble methods is proposed.
Unsupervised Feature Selection Algorithm Based on Dual Manifold Re-ranking
By comparing DMRR with three original unsupervised feature selection algorithms and two unsupervised feature selection post-processing algorithms, experimental results confirm that the importance information of different samples and the dual relationship between sample and feature are beneficial for achieving better feature selection.
Hierarchical Multiple Kernel K-Means Algorithm Based on Sparse Connectivity
Multiple kernel learning (MKL) aims to find an optimal, consistent kernel function.
Multiple kernel concept factorization algorithm based on global fusion
Non-negative Matrix Factorization(NMF) algorithm can only be used to find low rank approximation of original non-negative data while Concept Factorization(CF) algorithm extends matrix factorization to single non-linear kernel space, improving learning ability and adaptability of matrix factorization.
Unsupervised feature selection algorithm framework based on neighborhood interval disturbance fusion
Feature selection technology is a key technology of data dimensionality reduction.
Multiple Kernel Clustering via Local Regression Integration
It captures the local structure of kernel data and employs kernel regression on the local region to predict the clustering results.
Symmetry Nonnegative Matrix Factorization Algorithm Based on Self-paced Learning
A symmetric nonnegative matrix factorization algorithm based on self-paced learning was proposed to improve the clustering performance of the model.
SLIM: Let LLM Learn More and Forget Less with Soft LoRA and Identity Mixture
To efficiently fine-tune the LLMs with less limitation to their downstream performance while mitigating the forgetting of general capabilities, we propose a novel mixture of expert (MoE) framework based on Soft LoRA and Identity Mixture (SLIM), that allows dynamic routing between LoRA adapters and skipping connection, enables the suppression of forgetting.
Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs
This paper introduces the Dynamic Neural Dowker Network (DNDN), a novel framework specifically designed to approximate the results of dynamic Dowker filtration, aiming to capture the high-order topological features of dynamic directed graphs.
Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations
Heterogeneous treatment effect (HTE) estimation is vital for understanding the change of treatment effect across individuals or subgroups.
Fast Asymmetric Factorization for Large Scale Multiple Kernel Clustering
In response, Multiple Kernel Clustering (MKC) has emerged as a solution, allowing the fusion of information from multiple base kernels for clustering.
Supervisory Prompt Training
In this system, one LLM, the generator, performs a task while the other, the corrector, provides feedback and generates improved prompts.
Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning
Large language models (LLMs) such as GPT-4 have exhibited remarkable performance in a variety of tasks, but this strong performance often comes with the high expense of using paid API services.
RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction
Click-through rate (CTR) prediction is a critical task in recommendation systems, serving as the ultimate filtering step to sort items for a user.
BatchPrompt: Accomplish more with less
We have two initial observations for prompting with batched data.
Fair Causal Feature Selection
Fair feature selection for classification decision tasks has recently garnered significant attention from researchers.
DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction
To tackle this problem, inspired by the Global Workspace Theory in conscious processing, which posits that only a specific subset of the product features are pertinent while the rest can be noisy and even detrimental to human-click behaviors, we propose a CTR model that enables Dynamic Embedding Learning with Truncated Conscious Attention for CTR prediction, termed DELTA.
MathPrompter: Mathematical Reasoning using Large Language Models
Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers.
Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning
In this work, we propose a novel complementary learning approach to enhance test-time adaptation (TTA), which has been proven to exhibit good performance on testing data with distribution shifts such as corruptions.
Repainting and Imitating Learning for Lane Detection
In this paper, we target at finding an enhanced feature space where the lane features are distinctive while maintaining a similar distribution of lanes in the wild.
AGO-Net: Association-Guided 3D Point Cloud Object Detection Network
A feasible method is investigated to construct conceptual scenes without external datasets.
The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection
Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory.
SGM3D: Stereo Guided Monocular 3D Object Detection
Monocular 3D object detection aims to predict the object location, dimension and orientation in 3D space alongside the object category given only a monocular image.
A Data-Driven Democratized Control Architecture for Regional Transmission Operators
As probably the most complicated and critical infrastructure system, U. S. power grids become increasingly vulnerable to extreme events such as cyber-attacks and severe weather, as well as higher DER penetrations and growing information mismatch among system operators, utilities (transmission or generation owners), and end-users.
Multilingual Molecular Representation Learning via Contrastive Pre-training
Molecular representation learning plays an essential role in cheminformatics.
Random vector functional link neural network based ensemble deep learning for short-term load forecasting
Electricity load forecasting is crucial for the power systems' planning and maintenance.
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection
The objective of this paper is to learn context- and depth-aware feature representation to solve the problem of monocular 3D object detection.
Ranked #14 on
Monocular 3D Object Detection
on KITTI Cars Moderate
Manifold Adaptive Multiple Kernel K-Means for Clustering
Multiple kernel methods based on k-means aims to integrate a group of kernels to improve the performance of kernel k-means clustering.
Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection
Object detection from 3D point clouds remains a challenging task, though recent studies pushed the envelope with the deep learning techniques.
3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentation via Coupled Feature Selection
We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation.
Visualization of Multi-Objective Switched Reluctance Machine Optimization at Multiple Operating Conditions with t-SNE
However, the number of objectives that would need to be optimized would significantly increase with the number of operating points considered in the optimization, thus posting a potential problem in regards to the visualization techniques currently in use, such as in the scatter plots of Pareto fronts, the parallel coordinates, and in the principal component analysis (PCA), inhibiting their ability to provide machine designers with intuitive and informative visualizations of all of the design candidates and their ability to pick a few for further fine-tuning with performance verification.
SSF-DAN: Separated Semantic Feature Based Domain Adaptation Network for Semantic Segmentation
Despite the great success achieved by supervised fully convolutional models in semantic segmentation, training the models requires a large amount of labor-intensive work to generate pixel-level annotations.
3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation
Semantic segmentation of 3D unstructured point clouds remains an open research problem.
Cross-Age Face Verification by Coordinating With Cross-Face Age Verification
As shown in our experiments, the algorithm effectively balances feature sharing and feature exclusion between the two tasks; and, for face verification, the algorithm effectively removes distracting features used in age verification.
Unsupervised Feature Selection with Adaptive Structure Learning
Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data.
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes.
