Search Results for author: Su Wang

Found 26 papers, 4 papers with code

Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks

no code implementations7 Nov 2023 Su Wang, Roberto Morabito, Seyyedali Hosseinalipour, Mung Chiang, Christopher G. Brinton

Our optimization methodology aims to select the best combination of sampled nodes and data offloading configuration to maximize FedL training accuracy while minimizing data processing and D2D communication resource consumption subject to realistic constraints on the network topology and device capabilities.

Federated Learning

P2O-Calib: Camera-LiDAR Calibration Using Point-Pair Spatial Occlusion Relationship

no code implementations4 Nov 2023 Su Wang, Shini Zhang, Xuchong Qiu

Based on the extracted 2D-3D point pairs, we further propose an occlusion-guided point-matching method that improves the calibration accuracy and reduces computation costs.

Autonomous Driving

Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation

no code implementations27 Oct 2023 Jaemin Cho, Yushi Hu, Roopal Garg, Peter Anderson, Ranjay Krishna, Jason Baldridge, Mohit Bansal, Jordi Pont-Tuset, Su Wang

With extensive experimentation and human evaluation on a range of model configurations (LLM, VQA, and T2I), we empirically demonstrate that DSG addresses the challenges noted above.

Question Answering Question Generation +2

Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers

no code implementations8 Jun 2023 Su Wang, Rajeev Sahay, Adam Piaseczny, Christopher G. Brinton

In this work, we first reveal the susceptibility of FL-based signal classifiers to model poisoning attacks, which compromise the training process despite not observing data transmissions.

Federated Learning Model Poisoning

Multi-Source to Multi-Target Decentralized Federated Domain Adaptation

no code implementations24 Apr 2023 Su Wang, Seyyedali Hosseinalipour, Christopher G. Brinton

Our methodology, Source-Target Determination and Link Formation (ST-LF), optimizes both (i) classification of devices into sources and targets and (ii) source-target link formation, in a manner that considers the trade-off between ML model accuracy and communication energy efficiency.

Domain Adaptation Federated Learning

Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks

no code implementations15 Mar 2023 Su Wang, Seyyedali Hosseinalipour, Vaneet Aggarwal, Christopher G. Brinton, David J. Love, Weifeng Su, Mung Chiang

Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks.

Federated Learning

Scaling Robot Learning with Semantically Imagined Experience

no code implementations22 Feb 2023 Tianhe Yu, Ted Xiao, Austin Stone, Jonathan Tompson, Anthony Brohan, Su Wang, Jaspiar Singh, Clayton Tan, Dee M, Jodilyn Peralta, Brian Ichter, Karol Hausman, Fei Xia

Specifically, we make use of the state of the art text-to-image diffusion models and perform aggressive data augmentation on top of our existing robotic manipulation datasets via inpainting various unseen objects for manipulation, backgrounds, and distractors with text guidance.

Data Augmentation

How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?

no code implementations21 Jan 2023 Su Wang, Rajeev Sahay, Christopher G. Brinton

In this work, we reveal the susceptibility of FL-based signal classifiers to model poisoning attacks, which compromise the training process despite not observing data transmissions.

Federated Learning Model Poisoning

Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting

no code implementations CVPR 2023 Su Wang, Chitwan Saharia, Ceslee Montgomery, Jordi Pont-Tuset, Shai Noy, Stefano Pellegrini, Yasumasa Onoe, Sarah Laszlo, David J. Fleet, Radu Soricut, Jason Baldridge, Mohammad Norouzi, Peter Anderson, William Chan

Through extensive human evaluation on EditBench, we find that object-masking during training leads to across-the-board improvements in text-image alignment -- such that Imagen Editor is preferred over DALL-E 2 and Stable Diffusion -- and, as a cohort, these models are better at object-rendering than text-rendering, and handle material/color/size attributes better than count/shape attributes.

Image Inpainting text-guided-image-editing

A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning

no code implementations CVPR 2023 Aishwarya Kamath, Peter Anderson, Su Wang, Jing Yu Koh, Alexander Ku, Austin Waters, Yinfei Yang, Jason Baldridge, Zarana Parekh

Recent studies in Vision-and-Language Navigation (VLN) train RL agents to execute natural-language navigation instructions in photorealistic environments, as a step towards robots that can follow human instructions.

 Ranked #1 on Vision and Language Navigation on RxR (using extra training data)

Imitation Learning Instruction Following +1

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.

Management Time Series +2

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.

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