Search Results for author:
Found 9 papers, 3 papers with code
Why Don't You Clean Your Glasses? Perception Attacks with Dynamic Optical Perturbations
We demonstrate that the dynamic nature of EvilEye enables attackers to adapt adversarial examples across a variety of objects with a significantly higher ASR compared to state-of-the-art physical world attack frameworks.
Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ Cameras
Existing approaches for autonomous control of pan-tilt-zoom (PTZ) cameras use multiple stages where object detection and localization are performed separately from the control of the PTZ mechanisms.
Automatic Concept Extraction for Concept Bottleneck-based Video Classification
CoDEx identifies a rich set of complex concept abstractions from natural language explanations of videos-obviating the need to predefine the amorphous set of concepts.
NS3: Neuro-Symbolic Semantic Code Search
We compare our model - NS3 (Neuro-Symbolic Semantic Search) - to a number of baselines, including state-of-the-art semantic code retrieval methods, and evaluate on two datasets - CodeSearchNet and Code Search and Question Answering.
PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings
Generative models such as the variational autoencoder (VAE) and the generative adversarial networks (GAN) have proven to be incredibly powerful for the generation of synthetic data that preserves statistical properties and utility of real-world datasets, especially in the context of image and natural language text.
Using DeepProbLog to perform Complex Event Processing on an Audio Stream
We also demonstrate that our approach is capable of training even with a dataset that has a moderate proportion of noisy data.
How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods
Explaining the inner workings of deep neural network models have received considerable attention in recent years.
An Experimentation Platform for Explainable Coalition Situational Understanding
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.
BIG-bench Machine Learning
Explainable artificial intelligence
A Hybrid Neuro-Symbolic Approach for Complex Event Processing
We demonstrate this comparing our approach against a pure neural network approach on a dataset based on Urban Sounds 8K.
