Search Results for author:
Found 12 papers, 2 papers with code
Kullback-Leibler Divergence-Guided Copula Statistics-Based Blind Source Separation of Dependent Signals
In this paper, we propose a blind source separation of a linear mixture of dependent sources based on copula statistics that measure the non-linear dependence between source component signals structured as copula density functions.
Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity
The deployment of machine learning models in safety-critical applications comes with the expectation that such models will perform well over a range of contexts (e. g., a vision model for classifying street signs should work in rural, city, and highway settings under varying lighting/weather conditions).
Machine Learning aided Crop Yield Optimization
We present a crop simulation environment with an OpenAI Gym interface, and apply modern deep reinforcement learning (DRL) algorithms to optimize yield.
SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks
Self-supervised learning (SSL) methods have resulted in broad improvements to neural network performance by leveraging large, untapped collections of unlabeled data to learn generalized underlying structure.
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers
In this paper, we propose a new data poisoning attack and apply it to deep reinforcement learning agents.
Speaker Diarization using Two-pass Leave-One-Out Gaussian PLDA Clustering of DNN Embeddings
Many modern systems for speaker diarization, such as the recently-developed VBx approach, rely on clustering of DNN speaker embeddings followed by resegmentation.
The TrojAI Software Framework: An OpenSource tool for Embedding Trojans into Deep Learning Models
In this paper, we introduce the TrojAI software framework, an open source set of Python tools capable of generating triggered (poisoned) datasets and associated deep learning (DL) models with trojans at scale.
An Empirical Assessment of the Complexity and Realism of Synthetic Social Contact Networks
We use multiple measures of graph complexity to evaluate the realism of synthetically-generated networks of human activity, in comparison with several stylized network models as well as a collection of empirical networks from the literature.
On the Effect of Suboptimal Estimation of Mutual Information in Feature Selection and Classification
This paper introduces a new property of estimators of the strength of statistical association, which helps characterize how well an estimator will perform in scenarios where dependencies between continuous and discrete random variables need to be rank ordered.
Learning Approximate Neural Estimators for Wireless Channel State Information
Estimation is a critical component of synchronization in wireless and signal processing systems.
Copula Index for Detecting Dependence and Monotonicity between Stochastic Signals
This paper introduces a nonparametric copula-based index for detecting the strength and monotonicity structure of linear and nonlinear statistical dependence between pairs of random variables or stochastic signals.
Learning to Communicate: Channel Auto-encoders, Domain Specific Regularizers, and Attention
We address the problem of learning efficient and adaptive ways to communicate binary information over an impaired channel.
