Search Results for author: Andrea L. Bertozzi

Found 29 papers, 10 papers with code

AutoKG: Efficient Automated Knowledge Graph Generation for Language Models

1 code implementation22 Nov 2023 Bohan Chen, Andrea L. Bertozzi

Traditional methods of linking large language models (LLMs) to knowledge bases via the semantic similarity search often fall short of capturing complex relational dynamics.

Graph Generation Retrieval +2

Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets

1 code implementation19 Jul 2023 James Chapman, Bohan Chen, Zheng Tan, Jeff Calder, Kevin Miller, Andrea L. Bertozzi

Active learning improves the performance of machine learning methods by judiciously selecting a limited number of unlabeled data points to query for labels, with the aim of maximally improving the underlying classifier's performance.

Active Learning Graph Learning +1

Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images

no code implementations17 Jun 2023 Bohan Chen, Kevin Miller, Andrea L. Bertozzi, Jon Schwenk

We develop a graph active learning pipeline (GAP) to detect surface water and in-river sediment pixels in satellite images.

Active Learning

Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs

no code implementations19 Apr 2022 Justin Baker, Hedi Xia, Yiwei Wang, Elena Cherkaev, Akil Narayan, Long Chen, Jack Xin, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang

Learning neural ODEs often requires solving very stiff ODE systems, primarily using explicit adaptive step size ODE solvers.

Computational Efficiency

Graph-based Active Learning for Semi-supervised Classification of SAR Data

1 code implementation31 Mar 2022 Kevin Miller, John Mauro, Jason Setiadi, Xoaquin Baca, Zhan Shi, Jeff Calder, Andrea L. Bertozzi

We use a Convolutional Neural Network Variational Autoencoder (CNNVAE) to embed SAR data into a feature space, and then construct a similarity graph from the embedded data and apply graph-based semi-supervised learning techniques.

Active Learning graph construction +1

Efficient and Reliable Overlay Networks for Decentralized Federated Learning

no code implementations12 Dec 2021 Yifan Hua, Kevin Miller, Andrea L. Bertozzi, Chen Qian, Bao Wang

As such, our proposed overlay networks accelerate convergence, improve generalization, and enhance robustness to clients failures in DFL with theoretical guarantees.

Federated Learning Generalization Bounds +2

Model-Change Active Learning in Graph-Based Semi-Supervised Learning

2 code implementations14 Oct 2021 Kevin Miller, Andrea L. Bertozzi

Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.

Active Learning

Heavy Ball Neural Ordinary Differential Equations

1 code implementation NeurIPS 2021 Hedi Xia, Vai Suliafu, Hangjie Ji, Tan M. Nguyen, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang

We propose heavy ball neural ordinary differential equations (HBNODEs), leveraging the continuous limit of the classical momentum accelerated gradient descent, to improve neural ODEs (NODEs) training and inference.

Image Classification

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application

no code implementations22 May 2021 Hangjie Ji, Kyle Lafata, Yvonne Mowery, David Brizel, Andrea L. Bertozzi, Fang-Fang Yin, Chunhao Wang

With break-down biological modeling components, the outcome image predictions could be used in adaptive radiotherapy decision-making to optimize personalized plans for the best outcome in the future.

Decision Making Time Series +1

Posterior Consistency of Semi-Supervised Regression on Graphs

no code implementations25 Jul 2020 Andrea L. Bertozzi, Bamdad Hosseini, Hao Li, Kevin Miller, Andrew M. Stuart

Graph-based semi-supervised regression (SSR) is the problem of estimating the value of a function on a weighted graph from its values (labels) on a small subset of the vertices.

Clustering regression

Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations

no code implementations21 Jul 2020 Kevin Miller, Hao Li, Andrea L. Bertozzi

We present a novel adaptation of active learning to graph-based semi-supervised learning (SSL) under non-Gaussian Bayesian models.

Active Learning

MomentumRNN: Integrating Momentum into Recurrent Neural Networks

2 code implementations NeurIPS 2020 Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang

Designing deep neural networks is an art that often involves an expensive search over candidate architectures.

Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities

no code implementations ICLR 2020 Baichuan Yuan, Xiaowei Wang, Jianxin Ma, Chang Zhou, Andrea L. Bertozzi, Hongxia Yang

To bridge this gap, we introduce a declustering based hidden variable model that leads to an efficient inference procedure via a variational autoencoder (VAE).

Collaborative Filtering Point Processes +1

Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets

no code implementations2 Mar 2020 Thu Dinh, Bao Wang, Andrea L. Bertozzi, Stanley J. Osher

In this paper, we focus on a co-design of efficient DNN compression algorithms and sparse neural architectures for robust and accurate deep learning.

Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent

1 code implementation24 Feb 2020 Bao Wang, Tan M. Nguyen, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher

Nesterov accelerated gradient (NAG) improves the convergence rate of gradient descent (GD) for convex optimization using a specially designed momentum; however, it accumulates error when an inexact gradient is used (such as in SGD), slowing convergence at best and diverging at worst.

General Classification Image Classification

Semi-Supervised First-Person Activity Recognition in Body-Worn Video

no code implementations19 Apr 2019 Honglin Chen, Hao Li, Alexander Song, Matt Haberland, Osman Akar, Adam Dhillon, Tiankuang Zhou, Andrea L. Bertozzi, P. Jeffrey Brantingham

Body-worn cameras are now commonly used for logging daily life, sports, and law enforcement activities, creating a large volume of archived footage.

Activity Recognition

A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting

2 code implementations13 Feb 2019 Zhijian Li, Xiyang Luo, Bao Wang, Andrea L. Bertozzi, Jack Xin

We study epidemic forecasting on real-world health data by a graph-structured recurrent neural network (GSRNN).

Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction

no code implementations15 Nov 2018 Baichuan Yuan, Hao Li, Andrea L. Bertozzi, P. Jeffrey Brantingham, Mason A. Porter

There is often latent network structure in spatial and temporal data and the tools of network analysis can yield fascinating insights into such data.

Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization

1 code implementation23 Sep 2018 Bao Wang, Alex T. Lin, Wei Zhu, Penghang Yin, Andrea L. Bertozzi, Stanley J. Osher

We improve the robustness of Deep Neural Net (DNN) to adversarial attacks by using an interpolating function as the output activation.

Adversarial Attack Adversarial Defense +1

Stochastic Block Models are a Discrete Surface Tension

1 code implementation7 Jun 2018 Zachary M. Boyd, Mason A. Porter, Andrea L. Bertozzi

Networks, which represent agents and interactions between them, arise in myriad applications throughout the sciences, engineering, and even the humanities.

Clustering Video Semantic Segmentation

Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data

no code implementations2 Apr 2018 Bao Wang, Xiyang Luo, Fangbo Zhang, Baichuan Yuan, Andrea L. Bertozzi, P. Jeffrey Brantingham

We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time.

Simplified Energy Landscape for Modularity Using Total Variation

no code implementations28 Jul 2017 Zachary Boyd, Egil Bae, Xue-Cheng Tai, Andrea L. Bertozzi

We show that modularity optimization is equivalent to minimizing a convex TV-based functional over a discrete domain, again, assuming the number of communities is known.

Math

Deep Learning for Real Time Crime Forecasting

no code implementations9 Jul 2017 Bao Wang, Duo Zhang, Duanhao Zhang, P. Jeffery Brantingham, Andrea L. Bertozzi

Experiments over a half year period in Los Angeles reveal highly accurate predictive power of our models.

Crime Prediction

Uncertainty quantification in graph-based classification of high dimensional data

no code implementations26 Mar 2017 Andrea L. Bertozzi, Xiyang Luo, Andrew M. Stuart, Konstantinos C. Zygalakis

In this paper we introduce, develop algorithms for, and investigate the properties of, a variety of Bayesian models for the task of binary classification; via the posterior distribution on the classification labels, these methods automatically give measures of uncertainty.

Binary Classification Classification +3

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