Ananthram Swami

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IEEE

Senior Research Scientist at Army Research Laboratory

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

A widely established set of unsupervised node embedding methods can be i...
05/19/2021 ∙ by Yu Zhu, et al. ∙ 0 ∙ share

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Additive Link Metrics Identification: Proof of Selected Lemmas and Propositions

This is a technical report, containing all the lemma and proposition pro...
12/18/2020 ∙ by Liang Ma, et al. ∙ 0 ∙ share

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Identification of Additive Link Metrics: Proof of Selected Theorems

This is a technical report, containing all the theorem proofs in the fol...
12/18/2020 ∙ by Liang Ma, et al. ∙ 0 ∙ share

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Efficient Identification of Additive Link Metrics: Theorem Proof and Evaluations

This is a technical report, containing all the theorem proofs and additi...
12/17/2020 ∙ by Liang Ma, et al. ∙ 0 ∙ share

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Partial Network Identifiability: Theorem Proof and Evaluation

This is a technical report, containing all the theorem proofs and additi...
12/17/2020 ∙ by Liang Ma, et al. ∙ 0 ∙ share

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Link Identifiability with Two Monitors: Proof of Selected Theorems

This is a technical report, containing all the theorem proofs in paper "...
12/17/2020 ∙ by Liang Ma, et al. ∙ 0 ∙ share

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Fundamental Theories in Node Failure Localization

This is a technical report, containing all the theorem proofs in paper "...
12/17/2020 ∙ by Liang Ma, et al. ∙ 0 ∙ share

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Failure Localization Capability: Theorem Proof and Evaluation

This is a technical report, containing all the theorem proofs and additi...
12/17/2020 ∙ by Liang Ma, et al. ∙ 0 ∙ share

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Node Failure Localization: Theorem Proof

This is a technical report, containing all the theorem proofs in paper "...
12/17/2020 ∙ by Liang Ma, et al. ∙ 0 ∙ share

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Distributed Scheduling using Graph Neural Networks

A fundamental problem in the design of wireless networks is to efficient...
11/18/2020 ∙ by Zhongyuan Zhao, et al. ∙ 0 ∙ share

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Adaptive Contention Window Design using Deep Q-learning

We study the problem of adaptive contention window (CW) design for rando...
11/18/2020 ∙ by Abhishek Kumar, et al. ∙ 0 ∙ share

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Resource Allocation in One-dimensional Distributed Service Networks with Applications

We consider assignment policies that allocate resources to users, where ...
11/09/2020 ∙ by Nitish K. Panigrahy, et al. ∙ 0 ∙ share

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On the Analysis of Spatially Constrained Power of Two Choice Policies

We consider a class of power of two choice based assignment policies for...
11/05/2020 ∙ by Nitish K. Panigrahy, et al. ∙ 0 ∙ share

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You Do (Not) Belong Here: Detecting DPI Evasion Attacks with Context Learning

As Deep Packet Inspection (DPI) middleboxes become increasingly popular,...
11/03/2020 ∙ by Shitong Zhu, et al. ∙ 0 ∙ share

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

Existing Graph Neural Network (GNN) methods that learn inductive unsuper...
10/08/2020 ∙ by Leonardo Cotta, et al. ∙ 0 ∙ share

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

Deep neural networks have been shown to be vulnerable to membership infe...
09/17/2020 ∙ by Sumit Kumar Jha, et al. ∙ 8 ∙ share

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Measurement-driven Security Analysis of Imperceptible Impersonation Attacks

The emergence of Internet of Things (IoT) brings about new security chal...
08/26/2020 ∙ by Shasha Li, et al. ∙ 5 ∙ share

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Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency

There has been a recent surge in research on adversarial perturbations t...
07/19/2020 ∙ by Shasha Li, et al. ∙ 10 ∙ share

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GraphCL: Contrastive Self-Supervised Learning of Graph Representations

We propose Graph Contrastive Learning (GraphCL), a general framework for...
07/15/2020 ∙ by Hakim Hafidi, et al. ∙ 56 ∙ share

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Topology Inference with Multivariate Cumulants: The Möbius Inference Algorithm

Many tasks regarding the monitoring, management, and design of communica...
05/16/2020 ∙ by Kevin D. Smith, et al. ∙ 0 ∙ share

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A Multifactorial Optimization Paradigm for Linkage Tree Genetic Algorithm

Linkage Tree Genetic Algorithm (LTGA) is an effective Evolutionary Algor...
05/06/2020 ∙ by Huynh Thi Thanh Binh, et al. ∙ 0 ∙ share

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Resource Sharing in the Edge: A Distributed Bargaining-Theoretic Approach

The growing demand for edge computing resources, particularly due to inc...
01/13/2020 ∙ by Faheem Zafari, et al. ∙ 0 ∙ share

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Let's Share: A Game-Theoretic Framework for Resource Sharing in Mobile Edge Clouds

Mobile edge computing seeks to provide resources to different delay-sens...
01/02/2020 ∙ by Faheem Zafari, et al. ∙ 0 ∙ share

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SENSE: Semantically Enhanced Node Sequence Embedding

Effectively capturing graph node sequences in the form of vector embeddi...
11/07/2019 ∙ by Swati Rallapalli, et al. ∙ 20 ∙ share

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Quickest Detection of Growing Dynamic Anomalies in Networks

The problem of quickest growing dynamic anomaly detection in sensor netw...
10/21/2019 ∙ by Georgios Rovatsos, et al. ∙ 0 ∙ share

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MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design

In distributed software-defined networks (SDN), multiple physical SDN co...
09/19/2019 ∙ by Ziyao Zhang, et al. ∙ 0 ∙ share

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A Game-Theoretic Framework for Resource Sharing in Clouds

Providing resources to different users or applications is fundamental to...
04/01/2019 ∙ by Faheem Zafari, et al. ∙ 0 ∙ share

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Attribution-driven Causal Analysis for Detection of Adversarial Examples

Attribution methods have been developed to explain the decision of a mac...
03/14/2019 ∙ by Susmit Jha, et al. ∙ 6 ∙ share

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Resource Allocation in One-dimensional Distributed Service Networks

We consider assignment policies that allocate resources to users, where ...
01/08/2019 ∙ by Nitish K. Panigrahy, et al. ∙ 0 ∙ share

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A queueing-theoretic model for resource allocation in one-dimensional distributed service network

We consider assignment policies that allocate resources to requesting us...
01/08/2019 ∙ by Nitish K. Panigrahy, et al. ∙ 0 ∙ share

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A Game-Theoretic Approach to Multi-Objective Resource Sharing and Allocation in Mobile Edge Clouds

Mobile edge computing seeks to provide resources to different delay-sens...
08/21/2018 ∙ by Faheem Zafari, et al. ∙ 0 ∙ share

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Adversarial Perturbations Against Real-Time Video Classification Systems

Recent research has demonstrated the brittleness of machine learning sys...
07/02/2018 ∙ by Shasha Li, et al. ∙ 6 ∙ share

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Multi-Armed Bandits on Unit Interval Graphs

An online learning problem with side information on the similarity and d...
02/12/2018 ∙ by Xiao Xu, et al. ∙ 0 ∙ share

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Joint Data Compression and Caching: Approaching Optimality with Guarantees

We consider the problem of optimally compressing and caching data across...
01/06/2018 ∙ by Jian Li, et al. ∙ 0 ∙ share

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The Internet of Battle Things

The battlefield of the future will be densely populated by a variety of ...
12/25/2017 ∙ by Alexander Kott, et al. ∙ 0 ∙ share

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Optimal Energy Consumption with Communication, Computation, Caching and QoI-Guarantee

Energy efficiency is a fundamental requirement of modern data communicat...
12/10/2017 ∙ by Faheem Zafari, et al. ∙ 0 ∙ share

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Optimal Energy Tradeoff among Communication, Computation and Caching with QoI-Guarantee

Many applications must ingest and analyze data that are continuously gen...
12/10/2017 ∙ by Faheem Zafari, et al. ∙ 0 ∙ share

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Outlier Detection from Network Data with Subnetwork Interpretation

Detecting a small number of outliers from a set of data observations is ...
09/30/2016 ∙ by Xuan-Hong Dang, et al. ∙ 0 ∙ share

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Modeling Group Dynamics Using Probabilistic Tensor Decompositions

We propose a probabilistic modeling framework for learning the dynamic p...
06/24/2016 ∙ by Lin Li, et al. ∙ 0 ∙ share

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Crafting Adversarial Input Sequences for Recurrent Neural Networks

Machine learning models are frequently used to solve complex security pr...
04/28/2016 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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Extending Detection with Forensic Information

For over a quarter century, security-relevant detection has been driven ...
03/31/2016 ∙ by Z. Berkay Celik, et al. ∙ 0 ∙ share

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Practical Black-Box Attacks against Machine Learning

Machine learning (ML) models, e.g., deep neural networks (DNNs), are vul...
02/08/2016 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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The Limitations of Deep Learning in Adversarial Settings

Deep learning takes advantage of large datasets and computationally effi...
11/24/2015 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks

Deep learning algorithms have been shown to perform extremely well on ma...
11/14/2015 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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Ananthram Swami

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