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Found 38 papers, 16 papers with code
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
In this work, we revisit the classical PointNet++ through a systematic study of model training and scaling strategies, and offer two major contributions.
Ranked #3 on
3D Semantic Segmentation
on OpenTrench3D
EDUKG: a Heterogeneous Sustainable K-12 Educational Knowledge Graph
To tackle these issues, we propose EDUKG, a heterogeneous sustainable K-12 Educational Knowledge Graph.
Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity
Extensive experiments on two large real-world datasets demonstrate the effectiveness of HyperMine and the utility of modeling context granularity.
3DCoMPaT$^{++}$: An improved Large-scale 3D Vision Dataset for Compositional Recognition
In this work, we present 3DCoMPaT$^{++}$, a multimodal 2D/3D dataset with 160 million rendered views of more than 10 million stylized 3D shapes carefully annotated at the part-instance level, alongside matching RGB point clouds, 3D textured meshes, depth maps, and segmentation masks.
Contrasting the landscape of contrastive and non-contrastive learning
Some recent works however have shown promising results for non-contrastive learning, which does not require negative samples.
Semi-Supervised Few-Shot Learning with Prototypical Random Walks
We develop a random walk semi-supervised loss that enables the network to learn representations that are compact and well-separated.
Efficient Sampling Algorithms for Approximate Temporal Motif Counting (Extended Version)
We first propose a generic edge sampling (ES) algorithm for estimating the number of instances of any temporal motif.
Variations on the Chebyshev-Lagrange Activation Function
We seek to improve the data efficiency of neural networks and present novel implementations of parameterized piece-wise polynomial activation functions.
A Fully Dynamic Algorithm for k-Regret Minimizing Sets
Selecting a small set of representatives from a large database is important in many applications such as multi-criteria decision making, web search, and recommendation.
Databases Data Structures and Algorithms
Coresets for Minimum Enclosing Balls over Sliding Windows
This paper investigates the problem of maintaining a coreset to preserve the minimum enclosing ball (MEB) for a sliding window of points that are continuously updated in a data stream.
Block majorization-minimization with diminishing radius for constrained nonconvex optimization
Block majorization-minimization (BMM) is a simple iterative algorithm for nonconvex constrained optimization that sequentially minimizes majorizing surrogates of the objective function in each block coordinate while the other coordinates are held fixed.
How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding
While the successes of transformers across many domains are indisputable, accurate understanding of the learning mechanics is still largely lacking.
Efficient Representative Subset Selection over Sliding Windows
By keeping much fewer checkpoints, KW$^{+}$ achieves higher efficiency than KW while still guaranteeing a $\frac{1-\varepsilon'}{2+2d}$-approximate solution for SMDK.
DeepConsensus: using the consensus of features from multiple layers to attain robust image classification
We consider a classifier whose test set is exposed to various perturbations that are not present in the training set.
ChainGAN: A sequential approach to GANs
Current approaches attempt to learn the transformation from a noise sample to a generated data sample in one shot.
Context-Sensitive Malicious Spelling Error Correction
Misspelled words of the malicious kind work by changing specific keywords and are intended to thwart existing automated applications for cyber-environment control such as harassing content detection on the Internet and email spam detection.
Real-Time Influence Maximization on Dynamic Social Streams
Influence maximization (IM), which selects a set of $k$ users (called seeds) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications such as viral marketing and network monitoring.
Social and Information Networks Data Structures and Algorithms
GRMR: Generalized Regret-Minimizing Representatives
Extracting a small subset of representative tuples from a large database is an important task in multi-criteria decision making.
Data Structures and Algorithms Databases
The Limitations of Limited Context for Constituency Parsing
Concretely, we ground this question in the sandbox of probabilistic context-free-grammars (PCFGs), and identify a key aspect of the representational power of these approaches: the amount and directionality of context that the predictor has access to when forced to make parsing decision.
Privacy Threats Analysis to Secure Federated Learning
Federated learning is emerging as a machine learning technique that trains a model across multiple decentralized parties.
Improving greedy core-set configurations for active learning with uncertainty-scaled distances
We scale perceived distances of the core-set algorithm by a factor of uncertainty and search for low-confidence configurations, finding significant improvements in sample efficiency across CIFAR10/100 and SVHN image classification, especially in larger acquisition sizes.
AutoMine: An Unmanned Mine Dataset
The main contributions of the AutoMine dataset are as follows: 1. The first autonomous driving dataset for perception and localization in mine scenarios.
SFF-DA: Sptialtemporal Feature Fusion for Detecting Anxiety Nonintrusively
To address these issues, we propose a framework with spatiotemporal feature fusion for detecting anxiety nonintrusively.
Balancing Utility and Fairness in Submodular Maximization (Technical Report)
Submodular function maximization is a fundamental combinatorial optimization problem with plenty of applications -- including data summarization, influence maximization, and recommendation.
Continuous-Time Monotone Mean-Variance Portfolio Selection
We study the continuous-time portfolio selection under monotone mean-variance (MMV) preferences in a jump-diffusion model and give an explicit solution different from that under classical mean-variance (MV) preferences for the first time.
CAT: Learning to Collaborate Channel and Spatial Attention from Multi-Information Fusion
Channel and spatial attention mechanism has proven to provide an evident performance boost of deep convolution neural networks (CNNs).
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value.
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys
Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits.
Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors
Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions.
Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning
Growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their promise for enhanced safety, efficiency, and economic benefits.
FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusion
Firstly, we propose a multi-task motion planning algorithm, called FusionPlanner, for autonomous mining trucks by the multi-sensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation.
Improvement and Enhancement of YOLOv5 Small Target Recognition Based on Multi-module Optimization
In this paper, the limitations of YOLOv5s model on small target detection task are deeply studied and improved.
Comparative study of microgrid optimal scheduling under multi-optimization algorithm fusion
As global attention on renewable and clean energy grows, the research and implementation of microgrids become paramount.
Web News Timeline Generation with Extended Task Prompting
The creation of news timeline is essential for a comprehensive and contextual understanding of events as they unfold over time.
Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars
Interpretability methods aim to understand the algorithm implemented by a trained model (e. g., a Transofmer) by examining various aspects of the model, such as the weight matrices or the attention patterns.
Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization
Block majorization-minimization (BMM) is a simple iterative algorithm for nonconvex optimization that sequentially minimizes a majorizing surrogate of the objective function in each block coordinate while the other block coordinates are held fixed.
Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective
Given the complexity and lack of transparency in deep neural networks (DNNs), extensive efforts have been made to make these systems more interpretable or explain their behaviors in accessible terms.
Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm
Based on our analytical results, we then propose a joint communication and computation resource management design to minimize an average squared gradient norm bound, subject to constraints on the transmit power, overall system energy consumption, and training delay.
