Search Results for author: Ananthram Swami

Found 37 papers, 18 papers with code

Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks

1 code implementation27 Mar 2022 Zhongyuan Zhao, Ananthram Swami, Santiago Segarra

Distributed scheduling algorithms for throughput or utility maximization in dense wireless multi-hop networks can have overwhelmingly high overhead, causing increased congestion, energy consumption, radio footprint, and security vulnerability.

Power Allocation for Wireless Federated Learning using Graph Neural Networks

2 code implementations15 Nov 2021 Boning Li, Ananthram Swami, Santiago Segarra

We propose a data-driven approach for power allocation in the context of federated learning (FL) over interference-limited wireless networks.

Federated Learning

Delay-Oriented Distributed Scheduling Using Graph Neural Networks

1 code implementation13 Nov 2021 Zhongyuan Zhao, Gunjan Verma, Ananthram Swami, Santiago Segarra

In wireless multi-hop networks, delay is an important metric for many applications.

Link Scheduling using Graph Neural Networks

1 code implementation12 Sep 2021 Zhongyuan Zhao, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

Test results on medium-sized wireless networks show that our centralized heuristic can reach a near-optimal solution quickly, and our distributed heuristic based on a shallow GCN can reduce by nearly half the suboptimality gap of the distributed greedy solver with minimal increase in complexity.

Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling

no code implementations19 May 2021 Yu Zhu, Ananthram Swami, Santiago Segarra

On the other hand, we propose a matrix factorization method based on a loss function that generalizes that of the skip-gram model with negative sampling to arbitrary similarity matrices.

Link Prediction Node Classification +1

Combining Physics and Machine Learning for Network Flow Estimation

no code implementations ICLR 2021 Arlei Lopes da Silva, Furkan Kocayusufoglu, Saber Jafarpour, Francesco Bullo, Ananthram Swami, Ambuj Singh

The flow estimation problem consists of predicting missing edge flows in a network (e. g., traffic, power and water) based on partial observations.

Bilevel Optimization

Identification of Additive Link Metrics: Proof of Selected Theorems

no code implementations18 Dec 2020 Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, Don Towsley

This is a technical report, containing all the theorem proofs in the following two papers: (1) Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley, "Identifiability of Link Metrics Based on End-to-end Path Measurements," in ACM IMC, 2013.

Networking and Internet Architecture

Node Failure Localization: Theorem Proof

no code implementations17 Dec 2020 Liang Ma, Ting He, Ananthram Swami, Don Towsley, Kin K. Leung

This is a technical report, containing all the theorem proofs in paper "On Optimal Monitor Placement for Localizing Node Failures via Network Tomography" by Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung, published in IFIP WG 7. 3 Performance, 2015.

Networking and Internet Architecture

Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models

no code implementations NeurIPS 2020 Leonardo Cotta, Carlos H. C. Teixeira, Ananthram Swami, Bruno Ribeiro

Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in the graph.

Node Classification

Adaptive Contention Window Design using Deep Q-learning

1 code implementation18 Nov 2020 Abhishek Kumar, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

We study the problem of adaptive contention window (CW) design for random-access wireless networks.

Q-Learning

Distributed Scheduling using Graph Neural Networks

1 code implementation18 Nov 2020 Zhongyuan Zhao, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

In small- to middle-sized wireless networks with tens of links, even a shallow GCN-based MWIS scheduler can leverage the topological information of the graph to reduce in half the suboptimality gap of the distributed greedy solver with good generalizability across graphs and minimal increase in complexity.

Unsupervised Joint $k$-node Graph Representations with Compositional Energy-Based Models

no code implementations8 Oct 2020 Leonardo Cotta, Carlos H. C. Teixeira, Ananthram Swami, Bruno Ribeiro

Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in the graph.

Node Classification

Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation

1 code implementation22 Sep 2020 Arindam Chowdhury, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

We study the problem of optimal power allocation in a single-hop ad hoc wireless network.

An Extension of Fano's Inequality for Characterizing Model Susceptibility to Membership Inference Attacks

no code implementations17 Sep 2020 Sumit Kumar Jha, Susmit Jha, Rickard Ewetz, Sunny Raj, Alvaro Velasquez, Laura L. Pullum, Ananthram Swami

We present a new extension of Fano's inequality and employ it to theoretically establish that the probability of success for a membership inference attack on a deep neural network can be bounded using the mutual information between its inputs and its activations.

Inference Attack Membership Inference Attack

Measurement-driven Security Analysis of Imperceptible Impersonation Attacks

no code implementations26 Aug 2020 Shasha Li, Karim Khalil, Rameswar Panda, Chengyu Song, Srikanth V. Krishnamurthy, Amit K. Roy-Chowdhury, Ananthram Swami

The emergence of Internet of Things (IoT) brings about new security challenges at the intersection of cyber and physical spaces.

Face Recognition

Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency

no code implementations ECCV 2020 Shasha Li, Shitong Zhu, Sudipta Paul, Amit Roy-Chowdhury, Chengyu Song, Srikanth Krishnamurthy, Ananthram Swami, Kevin S. Chan

There has been a recent surge in research on adversarial perturbations that defeat Deep Neural Networks (DNNs) in machine vision; most of these perturbation-based attacks target object classifiers.

GraphCL: Contrastive Self-Supervised Learning of Graph Representations

no code implementations15 Jul 2020 Hakim Hafidi, Mounir Ghogho, Philippe Ciblat, Ananthram Swami

We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner.

Contrastive Learning Node Classification +1

Topology Inference with Multivariate Cumulants: The Möbius Inference Algorithm

no code implementations16 May 2020 Kevin D. Smith, Saber Jafarpour, Ananthram Swami, Francesco Bullo

Many tasks regarding the monitoring, management, and design of communication networks rely on knowledge of the routing topology.

A Multifactorial Optimization Paradigm for Linkage Tree Genetic Algorithm

1 code implementation6 May 2020 Huynh Thi Thanh Binh, Pham Dinh Thanh, Tran Ba Trung, Le Cong Thanh, Le Minh Hai Phong, Ananthram Swami, Bui Thu Lam

Linkage Tree Genetic Algorithm (LTGA) is an effective Evolutionary Algorithm (EA) to solve complex problems using the linkage information between problem variables.

Transfer Learning

Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks

2 code implementations NeurIPS 2019 Gunjan Verma, Ananthram Swami

Modern machine learning systems are susceptible to adversarial examples; inputs which clearly preserve the characteristic semantics of a given class, but whose classification is (usually confidently) incorrect.

Adversarial Defense Adversarial Robustness +1

MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design

no code implementations19 Sep 2019 Ziyao Zhang, Liang Ma, Konstantinos Poularakis, Kin K. Leung, Jeremy Tucker, Ananthram Swami

In distributed software-defined networks (SDN), multiple physical SDN controllers, each managing a network domain, are implemented to balance centralised control, scalability, and reliability requirements.

reinforcement-learning

Adversarial Perturbations Against Real-Time Video Classification Systems

1 code implementation2 Jul 2018 Shasha Li, Ajaya Neupane, Sujoy Paul, Chengyu Song, Srikanth V. Krishnamurthy, Amit K. Roy Chowdhury, Ananthram Swami

We exploit recent advances in generative adversarial network (GAN) architectures to account for temporal correlations and generate adversarial samples that can cause misclassification rates of over 80% for targeted activities.

Classification Frame +2

Multi-Armed Bandits on Partially Revealed Unit Interval Graphs

no code implementations12 Feb 2018 Xiao Xu, Sattar Vakili, Qing Zhao, Ananthram Swami

Two settings of complete and partial side information based on whether the UIG is fully revealed are studied and a general two-step learning structure consisting of an offline reduction of the action space and online aggregation of reward observations from similar arms is proposed to fully exploit the topological structure of the side information.

Multi-Armed Bandits

Sparse Diffusion-Convolutional Neural Networks

no code implementations26 Oct 2017 James Atwood, Siddharth Pal, Don Towsley, Ananthram Swami

The predictive power and overall computational efficiency of Diffusion-convolutional neural networks make them an attractive choice for node classification tasks.

General Classification Node Classification

Modeling Group Dynamics Using Probabilistic Tensor Decompositions

no code implementations24 Jun 2016 Lin Li, Ananthram Swami, Anna Scaglione

We propose a probabilistic modeling framework for learning the dynamic patterns in the collective behaviors of social agents and developing profiles for different behavioral groups, using data collected from multiple information sources.

Crafting Adversarial Input Sequences for Recurrent Neural Networks

1 code implementation28 Apr 2016 Nicolas Papernot, Patrick McDaniel, Ananthram Swami, Richard Harang

Machine learning models are frequently used to solve complex security problems, as well as to make decisions in sensitive situations like guiding autonomous vehicles or predicting financial market behaviors.

Autonomous Vehicles General Classification

Detection under Privileged Information

no code implementations31 Mar 2016 Z. Berkay Celik, Patrick McDaniel, Rauf Izmailov, Nicolas Papernot, Ryan Sheatsley, Raquel Alvarez, Ananthram Swami

In this paper, we consider an alternate learning approach that trains models using "privileged" information--features available at training time but not at runtime--to improve the accuracy and resilience of detection systems.

Face Recognition Malware Classification +1

Practical Black-Box Attacks against Machine Learning

17 code implementations8 Feb 2016 Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami

Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN.

The Limitations of Deep Learning in Adversarial Settings

12 code implementations24 Nov 2015 Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami

In this work, we formalize the space of adversaries against deep neural networks (DNNs) and introduce a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between inputs and outputs of DNNs.

Adversarial Attack Adversarial Defense

Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks

1 code implementation14 Nov 2015 Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami

In this work, we introduce a defensive mechanism called defensive distillation to reduce the effectiveness of adversarial samples on DNNs.

Autonomous Vehicles

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