Search Results for author:
Found 12 papers, 4 papers with code
Detecting and Quantifying Malicious Activity with Simulation-based Inference
We propose the use of probabilistic programming techniques to tackle the malicious user identification problem in a recommendation algorithm.
Shape-Tailored Deep Neural Networks With PDEs
ST-DNN are deep networks formulated through the use of partial differential equations (PDE) to be defined on arbitrarily shaped regions.
Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation
For low-fidelity training data (incorrect class label) class-agnostic segmentation loss outperforms the state-of-the-art methods on salient object detection datasets by staggering margins of around 50%.
DeformRS: Certifying Input Deformations with Randomized Smoothing
Deep neural networks are vulnerable to input deformations in the form of vector fields of pixel displacements and to other parameterized geometric deformations e. g. translations, rotations, etc.
Shape-Tailored Deep Neural Networks
We stack multiple PDE layers to generalize a deep CNN to arbitrary regions, and apply it to segmentation.
Shape-Tailored Deep Neural Networks Using PDEs for Segmentation
We present Shape-Tailored Deep Neural Networks (ST-DNN).
Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation
For low-fidelity training data (incorrect class label) class-agnostic segmentation loss outperforms the state-of-the-art methods on salient object detection datasets by staggering margins of around 50%.
Continual Learning in Low-rank Orthogonal Subspaces
In continual learning (CL), a learner is faced with a sequence of tasks, arriving one after the other, and the goal is to remember all the tasks once the continual learning experience is finished.
Learned Shape-Tailored Descriptors for Segmentation
We formulate and optimize a joint optimization problem in the segmentation and descriptors to discriminate these base descriptors using the learned metric, equivalent to grouping learned descriptors.
Coarse-To-Fine Segmentation With Shape-Tailored Continuum Scale Spaces
We formulate an energy for segmentation that is designed to have preference for segmenting the coarse over fine structure of the image, without smoothing across boundaries of regions.
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces
We formulate a general energy and method for segmentation that is designed to have preference for segmenting the coarse structure over the fine structure of the data, without smoothing across boundaries of regions.
Shape-Tailored Local Descriptors and Their Application to Segmentation and Tracking
Given a region of arbitrary shape in an image, these descriptors are formed from shape-dependent scale spaces of oriented gradients.
