Search Results for author: Zachary Patterson

Found 11 papers, 5 papers with code

Semantic Segmentation Using Transfer Learning on Fisheye Images

no code implementations International Conference on Machine Learning and Applications (ICMLA) 2023 Sneha Paul, Zachary Patterson, Nizar Bouguila

This can be attributed to the fact that the models are not designed to handle fisheye images, and the available fisheye datasets are not sufficiently large to effectively train complex models.

Image Segmentation Segmentation +2

TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting

no code implementations10 Dec 2023 Naghmeh Shafiee Roudbari, Charalambos Poullis, Zachary Patterson, Ursula Eicker

Since water systems are interconnected and the connectivity information between the stations is implicit, the proposed model leverages a graph learning module to extract a sparse graph adjacency matrix adaptively based on the data.

Decoder Graph Learning

DualMLP: a two-stream fusion model for 3D point cloud classification

1 code implementation The Visual Computer 2023 Sneha Paul, Zachary Patterson, Nizar Bouguila

The SparseNet, a relatively larger network, samples a small number of points from the complete point cloud, while the DenseNet, a lightweight network, takes in a larger number of points as input.

3D Point Cloud Classification Point Cloud Classification +1

CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud

1 code implementation 20th Conference on Robots and Vision (CRV) 2023 Sneha Paul, Zachary Patterson, Nizar Bouguila

In this study, we introduce a novel selfsupervised method called CrossMoCo, which learns the representations of unlabelled point cloud data in a multi-modal setup that also utilizes the 2D rendered images of the point clouds.

3D Object Classification 3D Point Cloud Linear Classification +4

Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction

1 code implementation8 Sep 2022 Naghmeh Shafiee Roudbari, Zachary Patterson, Ursula Eicker, Charalambos Poullis

In recent years, graph neural networks (GNNs) combined with variants of recurrent neural networks (RNNs) have reached state-of-the-art performance in spatiotemporal forecasting tasks.

Traffic Prediction

Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories

no code implementations23 Oct 2021 Ali Yazdizadeh, Arash Kalatian, Zachary Patterson, Bilal Farooq

While there's an assumption of higher performance of multi-task over sing-task learners, the results of this study does not hold such an assumption and shows, in the context of mode and trip purpose inference from GPS trajectory data, a multi-task learning approach does not bring any considerable advantage over single-task learners.

Multi-Task Learning

A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis

no code implementations29 Dec 2020 Godwin Badu-Marfo, Bilal Farooq, Zachary Patterson

In this work, we develop a privacy-by-design generative model for synthesizing the activity diary of the travel population using state-of-art deep learning approaches.

Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey

no code implementations18 Apr 2019 Ali Yazdizadeh, Zachary Patterson, Bilal Farooq

In our final model, we combine the output of CNN models using "average voting", "majority voting" and "optimal weights" methods.

Semi-supervised GANs to Infer Travel Modes in GPS Trajectories

no code implementations27 Feb 2019 Ali Yazdizadeh, Zachary Patterson, Bilal Farooq

Semi-supervised Generative Adversarial Networks (GANs) are developed in the context of travel mode inference with uni-dimensional smartphone trajectory data.

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