Search Results for author:
Found 15 papers, 7 papers with code
Hyperspectral Reconstruction of Skin Through Fusion of Scattering Transform Features
Hyperspectral imagery (HSI) is an established technique with an array of applications, but its use is limited due to both practical and technical issues associated with spectral devices.
Frame Quantization of Neural Networks
We present a post-training quantization algorithm with error estimates relying on ideas originating from frame theory.
Emergence of the SVD as an interpretable factorization in deep learning for inverse problems
Within the framework of deep learning we demonstrate the emergence of the singular value decomposition (SVD) of the weight matrix as a tool for interpretation of neural networks (NN) when combined with the descrambling transformation--a recently-developed technique for addressing interpretability in noisy parameter estimation neural networks \cite{amey2021neural}.
Active Learning at the ImageNet Scale
Active learning (AL) algorithms aim to identify an optimal subset of data for annotation, such that deep neural networks (DNN) can achieve better performance when trained on this labeled subset.
Protecting Proprietary Data: Poisoning for Secure Dataset Release
These two behaviors can be in conflict as an organization wants to prevent competitors from using their own data to replicate the performance of their proprietary models.
Exploring the high dimensional geometry of HSI features
We explore feature space geometries induced by the 3-D Fourier scattering transform and deep neural network with extended attribute profiles on four standard hyperspectral images.
Maximal function pooling with applications
Inspired by the Hardy-Littlewood maximal function, we propose a novel pooling strategy which is called maxfun pooling.
Cortical Features for Defense Against Adversarial Audio Attacks
We propose using a computational model of the auditory cortex as a defense against adversarial attacks on audio.
A study of quality and diversity in K+1 GANs
We study the K+1 GAN paradigm which generalizes the canonical true/fake GAN by training a generator with a K+1-ary classifier instead of a binary discriminator.
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching
We consider a particularly malicious poisoning attack that is both "from scratch" and "clean label", meaning we analyze an attack that successfully works against new, randomly initialized models, and is nearly imperceptible to humans, all while perturbing only a small fraction of the training data.
cGANs with Multi-Hinge Loss
We propose a new algorithm to incorporate class conditional information into the critic of GANs via a multi-class generalization of the commonly used Hinge loss that is compatible with both supervised and semi-supervised settings.
Ranked #8 on
Conditional Image Generation
on CIFAR-10
Transport Model for Feature Extraction
We present a new feature extraction method for complex and large datasets, based on the concept of transport operators on graphs.
Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images
Recent developments in machine learning and signal processing have resulted in many new techniques that are able to effectively capture the intrinsic yet complex properties of hyperspectral imagery.
Adversarial Examples in Remote Sensing
This paper considers attacks against machine learning algorithms used in remote sensing applications, a domain that presents a suite of challenges that are not fully addressed by current research focused on natural image data such as ImageNet.
Superresolution of Noisy Remotely Sensed Images Through Directional Representations
We justify the use of shearlets mathematically, before presenting a denoising single-image superresolution algorithm that combines the shearlet transform with sparse mixing estimators (SME).
