Search Results for author:
Found 38 papers, 9 papers with code
A Brain-Inspired Perception-Decision Driving Model Based on Neural Pathway Anatomical Alignment
In the realm of autonomous driving, conventional approaches for vehicle perception and decision-making primarily rely on sensor input and rule-based algorithms.
BAN: Neuroanatomical Aligning in Auditory Recognition between Artificial Neural Network and Human Cortex
In conclusion, this study presents BAN as a recurrent, brain-inspired ANN, representing the first model that mirrors the cortical pathway of auditory recognition.
BCDDM: Branch-Corrected Denoising Diffusion Model for Black Hole Image Generation
The properties of black holes and accretion flows can be inferred by fitting Event Horizon Telescope (EHT) data to simulated images generated through general relativistic ray tracing (GRRT).
Efficient Vision Language Model Fine-tuning for Text-based Person Anomaly Search
This paper presents the HFUT-LMC team's solution to the WWW 2025 challenge on Text-based Person Anomaly Search (TPAS).
Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks
Recent studies have revealed the vulnerability of graph neural networks (GNNs) to adversarial poisoning attacks on node classification tasks.
Lean classical-quantum hybrid neural network model for image classification
We apply the LCQHNN to image classiffcation tasks on public datasets and achieve a classiffcation accuracy of 99. 02% on the dataset, marking a 5. 07% improvement over traditional deep learning methods.
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning
Efficient analysis of these graphs is critical for deriving insights in IoT scenarios such as smart cities, industrial IoT, and intelligent transportation systems.
Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent
In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens.
Flexible 3D Lane Detection by Hierarchical Shape MatchingFlexible 3D Lane Detection by Hierarchical Shape Matching
As one of the basic while vital technologies for HD map construction, 3D lane detection is still an open problem due to varying visual conditions, complex typologies, and strict demands for precision.
GraphPub: Generation of Differential Privacy Graph with High Availability
In recent years, with the rapid development of graph neural networks (GNN), more and more graph datasets have been published for GNN tasks.
Analog Beamforming for In-Band Full-Duplex Phased Arrays with Quantized Phase Shifters under a Per-Antenna Received Power Constraint
This letter develops a novel transmit beamforming (BF) design for canceling self-interference (SI) in analog in-band full-duplex phased arrays.
Towards Inductive Robustness: Distilling and Fostering Wave-induced Resonance in Transductive GCNs Against Graph Adversarial Attacks
We then prove that merely three MP iterations within GCNs can induce signal resonance between nodes and edges, manifesting as a coupling between nodes and their distillable surrounding local subgraph.
A Survey on Multimodal Large Language Models for Autonomous Driving
We first introduce the background of Multimodal Large Language Models (MLLMs), the multimodal models development using LLMs, and the history of autonomous driving.
Elastic Information Bottleneck
Comparing IB and DIB on these terms, we prove that DIB's SG bound is tighter than IB's while DIB's RD is larger than IB's.
Generative Data Augmentation for Aspect Sentiment Quad Prediction
Aspect sentiment quad prediction (ASQP) analyzes the aspect terms, opinion terms, sentiment polarity, and aspect categories in a text.
Ranked #3 on
Aspect-Based Sentiment Analysis (ABSA)
on ASQP
Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience
In this paper, we propose the Graph Agent Network (GAgN) to address the aforementioned vulnerabilities of GNNs.
FedCliP: Federated Learning with Client Pruning
The prevalent communication efficient federated learning (FL) frameworks usually take advantages of model gradient compression or model distillation.
THMA: Tencent HD Map AI System for Creating HD Map Annotations
THMA has been deployed by the Tencent Map team to provide services to downstream companies and users, serving over 1, 000 labeling workers and producing more than 30, 000 kilometers of HD map data per day at most.
Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction
Limited labeled data increase the risk of distribution shift between test data and training data.
Differentially Private Condorcet Voting
Designing private voting rules is an important and pressing problem for trustworthy democracy.
A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks
To address this problem, we propose a universal weight initialization paradigm, which generalizes Xavier and Kaiming methods and can be widely applicable to arbitrary TCNNs.
PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation
However, directly learning the logical inference knowledge from table-text pairs is very difficult for neural models because of the ambiguity of natural language and the scarcity of parallel data.
Semi-Supervised Formality Style Transfer with Consistency Training
In this work, we propose a simple yet effective semi-supervised framework to better utilize source-side unlabeled sentences based on consistency training.
Ranked #1 on
Formality Style Transfer
on GYAFC
(using extra training data)
Towards Effective Multi-Task Interaction for Entity-Relation Extraction: A Unified Framework with Selection Recurrent Network
Entity-relation extraction aims to jointly solve named entity recognition (NER) and relation extraction (RE).
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs, and various other document types, a flurry of table pre-training frameworks have been proposed following the success of text and images, and they have achieved new state-of-the-arts on various tasks such as table question answering, table type recognition, column relation classification, table search, formula prediction, etc.
Personalized Lane Change Decision Algorithm Using Deep Reinforcement Learning Approach
To develop driving automation technologies for human, a human-centered methodology should be adopted for ensured safety and satisfactory user experience.
Improving Logical-Level Natural Language Generation with Topic-Conditioned Data Augmentation and Logical Form Generation
We further introduce logical form generation (LG), a dual task of Logic2text that requires generating a valid logical form based on a text description of a table.
Personalized Highway Pilot Assist Considering Leading Vehicle's Lateral Behaviours
Drivers are clustered into two driving style groups referring to their driving Behaviours affected by the leading vehicle, and then the personalization parameters for every specific subject driver are optimized.
Safe, efficient and socially-compatible decision of automated vehicles: a case study of unsignalized intersection driving
By investigating the causes of 4, 300 video clips of traffic accidents, we find that the limited dynamic visual field of drivers is one leading factor in inter-vehicle interaction accidents, especially in those involving trucks.
Smoothed Differential Privacy
Differential privacy (DP) is a widely-accepted and widely-applied notion of privacy based on worst-case analysis.
Certifiably Robust Interpretation via Renyi Differential Privacy
The advantages of our Renyi-Robust-Smooth (RDP-based interpretation method) are three-folds.
Categorizing Online Shopping Behavior from Cosmetics to Electronics: An Analytical Framework
A success factor for modern companies in the age of Digital Marketing is to understand how customers think and behave based on their online shopping patterns.
Learning Mixtures of Random Utility Models with Features from Incomplete Preferences
We extend mixtures of RUMs with features to models that generate incomplete preferences and characterize their identifiability.
AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack
In this paper, we first generalize the formulation of edge-perturbing attacks and strictly prove the vulnerability of GCNs to such attacks in node classification tasks.
SFE-GACN: A Novel Unknown Attack Detection Method Using Intra Categories Generation in Embedding Space
In this paper, we propose a novel unknown attack detection method based on Intra Categories Generation in Embedding Space, namely SFE-GACN, which might be the solution of few-shot problem.
Differential Privacy for Eye-Tracking Data
As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community.
Towards Non-Parametric Learning to Rank
Next, we fix the problem by introducing a new algorithm with features constructed from "global information" of the data matrix.
Automatically Extracting Action Graphs from Materials Science Synthesis Procedures
In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds.
