Search Results for author: Mani Srivastava

Found 17 papers, 6 papers with code

Using DeepProbLog to perform Complex Event Processing on an Audio Stream

no code implementations15 Oct 2021 Marc Roig Vilamala, Tianwei Xing, Harrison Taylor, Luis Garcia, Mani Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig, Federico Cerutti

We also demonstrate that our approach is capable of training even with a dataset that has a moderate proportion of noisy data.

Towards Imperceptible Query-limited Adversarial Attacks with Perceptual Feature Fidelity Loss

no code implementations31 Jan 2021 Pengrui Quan, Ruiming Guo, Mani Srivastava

Recently, there has been a large amount of work towards fooling deep-learning-based classifiers, particularly for images, via adversarial inputs that are visually similar to the benign examples.

An Experimentation Platform for Explainable Coalition Situational Understanding

no code implementations27 Oct 2020 Katie Barrett-Powell, Jack Furby, Liam Hiley, Marc Roig Vilamala, Harrison Taylor, Federico Cerutti, Alun Preece, Tianwei Xing, Luis Garcia, Mani Srivastava, Dave Braines

We present an experimentation platform for coalition situational understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML) and integration of symbolic and subsymbolic AI/ML approaches for event processing.

Explainable artificial intelligence

Towards human-agent knowledge fusion (HAKF) in support of distributed coalition teams

no code implementations23 Oct 2020 Dave Braines, Federico Cerutti, Marc Roig Vilamala, Mani Srivastava, Lance Kaplan Alun Preece, Gavin Pearson

Future coalition operations can be substantially augmented through agile teaming between human and machine agents, but in a coalition context these agents may be unfamiliar to the human users and expected to operate in a broad set of scenarios rather than being narrowly defined for particular purposes.

A Hybrid Neuro-Symbolic Approach for Complex Event Processing

no code implementations7 Sep 2020 Marc Roig Vilamala, Harrison Taylor, Tianwei Xing, Luis Garcia, Mani Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig, Federico Cerutti

We demonstrate this comparing our approach against a pure neural network approach on a dataset based on Urban Sounds 8K.

MANGO: A Python Library for Parallel Hyperparameter Tuning

1 code implementation22 May 2020 Sandeep Singh Sandha, Mohit Aggarwal, Igor Fedorov, Mani Srivastava

Tuning hyperparameters for machine learning algorithms is a tedious task, one that is typically done manually.

Distributed Computing Distributed Optimization +1

NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations

no code implementations18 Nov 2019 Xijie Huang, Moustafa Alzantot, Mani Srivastava

NeuronInspect first identifies the existence of backdoor attack targets by generating the explanation heatmap of the output layer.

Outlier Detection Traffic Sign Recognition

NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning

no code implementations5 Aug 2019 Moustafa Alzantot, Amy Widdicombe, Simon Julier, Mani Srivastava

When applied to image classification models, NeuroMask identifies the image parts that are most important to classifier results by applying a mask that hides/reveals different parts of the image, before feeding it back into the model.

Classification General Classification +1

GenAttack: Practical Black-box Attacks with Gradient-Free Optimization

2 code implementations28 May 2018 Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty, huan zhang, Cho-Jui Hsieh, Mani Srivastava

Our experiments on different datasets (MNIST, CIFAR-10, and ImageNet) show that GenAttack can successfully generate visually imperceptible adversarial examples against state-of-the-art image recognition models with orders of magnitude fewer queries than previous approaches.

Generating Natural Language Adversarial Examples

4 code implementations EMNLP 2018 Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, Kai-Wei Chang

Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify.

Natural Language Inference Sentiment Analysis

Did you hear that? Adversarial Examples Against Automatic Speech Recognition

no code implementations2 Jan 2018 Moustafa Alzantot, Bharathan Balaji, Mani Srivastava

Speech is a common and effective way of communication between humans, and modern consumer devices such as smartphones and home hubs are equipped with deep learning based accurate automatic speech recognition to enable natural interaction between humans and machines.

automatic-speech-recognition Object Detection +1

D-SLATS: Distributed Simultaneous Localization and Time Synchronization

no code implementations10 Nov 2017 Amr Alanwar, Henrique Ferraz, Kevin Hsieh, Rohit Thazhath, Paul Martin, Joao Hespanha, Mani Srivastava

Therefore, we propose D-SLATS, a framework comprised of three different and independent algorithms to jointly solve time synchronization and localization problems in a distributed fashion.

Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration

no code implementations15 Jul 2017 Jeng-Hau Lin, Tianwei Xing, Ritchie Zhao, Zhiru Zhang, Mani Srivastava, Zhuowen Tu, Rajesh K. Gupta

State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution.

Demo Abstract: NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets

2 code implementations20 Sep 2014 Jack Kelly, Nipun Batra, Oliver Parson, Haimonti Dutta, William Knottenbelt, Alex Rogers, Amarjeet Singh, Mani Srivastava

In this demonstration, we present an open source toolkit for evaluating non-intrusive load monitoring research; a field which aims to disaggregate a household's total electricity consumption into individual appliances.

Other Computer Science

NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring

2 code implementations15 Apr 2014 Nipun Batra, Jack Kelly, Oliver Parson, Haimonti Dutta, William Knottenbelt, Alex Rogers, Amarjeet Singh, Mani Srivastava

We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.


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