Search Results for author: Su Wang

Found 16 papers, 3 papers with code

Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks

no code implementations7 Feb 2022 Seyyedali Hosseinalipour, Su Wang, Nicolo Michelusi, Vaneet Aggarwal, Christopher G. Brinton, David J. Love, Mung Chiang

PSL considers the realistic scenario where global aggregations are conducted with idle times in-between them for resource efficiency improvements, and incorporates data dispersion and model dispersion with local model condensation into FedL.

Federated Learning

DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications

1 code implementation23 Sep 2021 Dongqi Han, Zhiliang Wang, Wenqi Chen, Ying Zhong, Su Wang, Han Zhang, Jiahai Yang, Xingang Shi, Xia Yin

Experimental results show that DeepAID can provide high-quality interpretations for unsupervised DL models while meeting the special requirements of security domains.

Anomaly Detection

UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach

no code implementations29 Jun 2021 Su Wang, Seyyedali Hosseinalipour, Maria Gorlatova, Christopher G. Brinton, Mung Chiang

The presence of time-varying data heterogeneity and computational resource inadequacy among device clusters motivate four key parts of our methodology: (i) stratified UAV swarms of leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning (HN-PFL), a distributed ML framework for personalized model training across the worker-leader-core network hierarchy, (iii) cooperative UAV resource pooling to address computational inadequacy of devices by conducting model training among the UAV swarms, and (iv) model/concept drift to model time-varying data distributions.

Decision Making Personalized Federated Learning

On the Evaluation of Vision-and-Language Navigation Instructions

no code implementations EACL 2021 Ming Zhao, Peter Anderson, Vihan Jain, Su Wang, Alexander Ku, Jason Baldridge, Eugene Ie

Vision-and-Language Navigation wayfinding agents can be enhanced by exploiting automatically generated navigation instructions.

Vision and Language Navigation

Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation

no code implementations4 Jan 2021 Su Wang, Mengyuan Lee, Seyyedali Hosseinalipour, Roberto Morabito, Mung Chiang, Christopher G. Brinton

The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server.

Federated Learning Learning Theory

GenAD: General Representations of Multivariate Time Series for Anomaly Detection

no code implementations1 Jan 2021 Xiaolei Hua, Su Wang, Lin Zhu, Dong Zhou, Junlan Feng, Yiting Wang, Chao Deng, Shuo Wang, Mingtao Mei

However, due to complex correlations and various temporal patterns of large-scale multivariate time series, a general unsupervised anomaly detection model with higher F1-score and Timeliness remains a challenging task.

Time Series Unsupervised Anomaly Detection

Narrative Interpolation for Generating and Understanding Stories

no code implementations17 Aug 2020 Su Wang, Greg Durrett, Katrin Erk

We propose a method for controlled narrative/story generation where we are able to guide the model to produce coherent narratives with user-specified target endings by interpolation: for example, we are told that Jim went hiking and at the end Jim needed to be rescued, and we want the model to incrementally generate steps along the way.

Story Generation

Network-Aware Optimization of Distributed Learning for Fog Computing

no code implementations17 Apr 2020 Yuwei Tu, Yichen Ruan, Su Wang, Satyavrat Wagle, Christopher G. Brinton, Carlee Joe-Wong

Unlike traditional federated learning frameworks, our method enables devices to offload their data processing tasks to each other, with these decisions determined through a convex data transfer optimization problem that trades off costs associated with devices processing, offloading, and discarding data points.

Distributed, Parallel, and Cluster Computing

Query-Focused Scenario Construction

no code implementations IJCNLP 2019 Su Wang, Greg Durrett, Katrin Erk

The news coverage of events often contains not one but multiple incompatible accounts of what happened.

Picking Apart Story Salads

no code implementations EMNLP 2018 Su Wang, Eric Holgate, Greg Durrett, Katrin Erk

During natural disasters and conflicts, information about what happened is often confusing, messy, and distributed across many sources.

Modeling Semantic Plausibility by Injecting World Knowledge

1 code implementation NAACL 2018 Su Wang, Greg Durrett, Katrin Erk

Distributional data tells us that a man can swallow candy, but not that a man can swallow a paintball, since this is never attested.

Leveraging Discourse Information Effectively for Authorship Attribution

1 code implementation IJCNLP 2017 Su Wang, Elisa Ferracane, Raymond J. Mooney

We explore techniques to maximize the effectiveness of discourse information in the task of authorship attribution.

Distributional Modeling on a Diet: One-shot Word Learning from Text Only

no code implementations IJCNLP 2017 Su Wang, Stephen Roller, Katrin Erk

We test whether distributional models can do one-shot learning of definitional properties from text only.

One-Shot Learning

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