Search Results for author: Jonathan Larson

Found 10 papers, 5 papers with code

Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models

1 code implementation15 Dec 2023 Xin Jin, Jonathan Larson, Weiwei Yang, Zhiqiang Lin

Binary code summarization, while invaluable for understanding code semantics, is challenging due to its labor-intensive nature.

Benchmarking Code Summarization +2

Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics

no code implementations3 May 2023 Cencheng Shen, Jonathan Larson, Ha Trinh, Xihan Qin, Youngser Park, Carey E. Priebe

Analyzing large-scale time-series network data, such as social media and email communications, poses a significant challenge in understanding social dynamics, detecting anomalies, and predicting trends.

Time Series

Synergistic Graph Fusion via Encoder Embedding

1 code implementation31 Mar 2023 Cencheng Shen, Carey E. Priebe, Jonathan Larson, Ha Trinh

In this paper, we introduce a novel approach called graph fusion embedding, designed for multi-graph embedding with shared vertex sets.

Classification Graph Embedding +1

Learning without gradient descent encoded by the dynamics of a neurobiological model

no code implementations16 Mar 2021 Vivek Kurien George, Vikash Morar, Weiwei Yang, Jonathan Larson, Bryan Tower, Shweti Mahajan, Arkin Gupta, Christopher White, Gabriel A. Silva

The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss function.

BIG-bench Machine Learning

Multiple Network Embedding for Anomaly Detection in Time Series of Graphs

1 code implementation23 Aug 2020 Guodong Chen, Jesús Arroyo, Avanti Athreya, Joshua Cape, Joshua T. Vogelstein, Youngser Park, Chris White, Jonathan Larson, Weiwei Yang, Carey E. Priebe

We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally anomalous vertices.

Methodology

Omnidirectional Transfer for Quasilinear Lifelong Learning

1 code implementation27 Apr 2020 Joshua T. Vogelstein, Jayanta Dey, Hayden S. Helm, Will LeVine, Ronak D. Mehta, Ali Geisa, Haoyin Xu, Gido M. van de Ven, Emily Chang, Chenyu Gao, Weiwei Yang, Bryan Tower, Jonathan Larson, Christopher M. White, Carey E. Priebe

But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data.

Federated Learning Transfer Learning

Vertex Nomination, Consistent Estimation, and Adversarial Modification

no code implementations6 May 2019 Joshua Agterberg, Youngser Park, Jonathan Larson, Christopher White, Carey E. Priebe, Vince Lyzinski

Given a pair of graphs $G_1$ and $G_2$ and a vertex set of interest in $G_1$, the vertex nomination (VN) problem seeks to find the corresponding vertices of interest in $G_2$ (if they exist) and produce a rank list of the vertices in $G_2$, with the corresponding vertices of interest in $G_2$ concentrating, ideally, at the top of the rank list.

Graph Embedding

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