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Found 17 papers, 4 papers with code
Learning with SASQuaTCh: a Novel Variational Quantum Transformer Architecture with Kernel-Based Self-Attention
The recent exploding growth in size of state-of-the-art machine learning models highlights a well-known issue where exponential parameter growth, which has grown to trillions as in the case of the Generative Pre-trained Transformer (GPT), leads to training time and memory requirements which limit their advancement in the near term.
Omitted Labels in Causality: A Study of Paradoxes
We explore what we call ``omitted label contexts,'' in which training data is limited to a subset of the possible labels.
kNN-Res: Residual Neural Network with kNN-Graph coherence for point cloud registration
In this paper, we present a method based on a residual neural network for point set registration that preserves the topological structure of the target point set.
k-Means Maximum Entropy Exploration
We introduce an artificial curiosity algorithm based on lower bounding an approximation to the entropy of the state visitation distribution.
Microtubule Tracking in Electron Microscopy Volumes
We present a method for microtubule tracking in electron microscopy volumes.
Outlier Detection through Null Space Analysis of Neural Networks
Specifically, many systems lack the ability to identify when outliers (e. g., samples that are distinct from and not represented in the training data distribution) are being presented to the system.
Efficient 2D neuron boundary segmentation with local topological constraints
Drawing inspiration from these human strategies, we formulate the segmentation task as an edge labeling problem on a graph with local topological constraints.
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian Processes
Serial section electron microscopy (ssEM) is a widely used technique for obtaining volumetric information of biological tissues at nanometer scale.
FireNet: Real-time Segmentation of Fire Perimeter from Aerial Video
In this paper, we share our approach to real-time segmentation of fire perimeter from aerial full-motion infrared video.
Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation
In this paper, we compare the segmentation performance of a semi-supervised approach using PFLICM and a supervised method using Possibilistic K-NN.
Synaptic partner prediction from point annotations in insect brains
High-throughput electron microscopy allows recording of lar- ge stacks of neural tissue with sufficient resolution to extract the wiring diagram of the underlying neural network.
Deep Learning in the Automotive Industry: Applications and Tools
In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision.
A wake-sleep algorithm for recurrent, spiking neural networks
We investigate a recently proposed model for cortical computation which performs relational inference.
Learning and Inferring Relations in Cortical Networks
We are at a loss to explain, simulate, or understand such a multi-functional homogeneous sheet-like computational structure - we do not have computational models which work in this way.
Adaptive coherence estimator (ACE) for explosive hazard detection using wideband electromagnetic induction (WEMI)
The adaptive coherence estimator (ACE) estimates the squared cosine of the angle between a known target vector and a sample vector in a whitened coordinate space.
Buried object detection using handheld WEMI with task-driven extended functions of multiple instances
The new algorithm, Task Driven Extended Functions of Multiple Instances, can overcome data that does not have very precise point-wise labels and still learn a highly discriminative dictionary.
TED: A Tolerant Edit Distance for Segmentation Evaluation
This measure, which we call Tolerant Edit Distance (TED), is motivated by two observations: (1) Some errors, like small boundary shifts, are tolerable in practice.
