Search Results for author:
Found 11 papers, 1 papers with code
Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping.
Cross-Modal ASR Post-Processing System for Error Correction and Utterance Rejection
Although modern automatic speech recognition (ASR) systems can achieve high performance, they may produce errors that weaken readers' experience and do harm to downstream tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation
In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests.
Adversarially Contrastive Estimation of Conditional Neural Processes
Conditional Neural Processes~(CNPs) formulate distributions over functions and generate function observations with exact conditional likelihoods.
Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT
To expand the ability of the current robot system in sequential understanding, this paper introduces RoboGPT, a novel system that leverages the advanced reasoning capabilities of ChatGPT, a large language model, for automated sequence planning in robot-based assembly applied to construction tasks.
Improved Trust in Human-Robot Collaboration with ChatGPT
A human-subject experiment showed that incorporating ChatGPT in robots significantly increased trust in human-robot collaboration, which can be attributed to the robot's ability to communicate more effectively with humans.
Support Vector Machine Guided Reproducing Kernel Particle Method for Image-Based Modeling of Microstructures
The proposed method modifies the smooth kernel functions with a regularized heavy-side function concerning the material interfaces to alleviate Gibb's oscillations.
Dynamic Clustering Transformer Network for Point Cloud Segmentation
Specifically, we propose novel semantic feature-based dynamic sampling and clustering methods in the encoder, which enables the model to be aware of local semantic homogeneity for local feature aggregation.
Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation
Next, we aim to build distributional implicit matchings between the domain-level preferences of two domains.
Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics
Drones are vital for urban emergency search and rescue (SAR) due to the challenges of navigating dynamic environments with obstacles like buildings and wind.
Joint Identifiability of Cross-Domain Recommendation via Hierarchical Subspace Disentanglement
Such a failure may overlook the conditionality between two domains and how it contributes to latent factor disentanglement, leading to negative transfer when domains are weakly correlated.
