Search Results for author:
Found 21 papers, 2 papers with code
Discovering Image Usage Online: A Case Study With "Flatten the Curve''
We use five variants of the "Flatten the Curve" image as a case study for viewing the spread of an image online.
Semi-supervised Learning of Pushforwards For Domain Translation & Adaptation
We compare our method (parOT) to related optimal transport approaches in the context of domain adaptation and domain translation on benchmark data sets.
Generative structured normalizing flow Gaussian processes applied to spectroscopic data
In particular, in the physical sciences, limited training data may not adequately characterize future observed data; it is critical that models adequately indicate uncertainty, particularly when they may be asked to extrapolate.
Abstract Images Have Different Levels of Retrievability Per Reverse Image Search Engine
These results affect anyone applying common web search engines to search for technical documents that use abstract images.
Robustness to Label Noise Depends on the Shape of the Noise Distribution in Feature Space
However, for the special case of class-dependent label noise (independent of features given the class label), the tipping point can be as low as 50%.
DeepPatent: Large scale patent drawing recognition and retrieval
First, we introduce DeepPatent, a new large-scale dataset for recognition and retrieval of design patent drawings.
Ranked #3 on
Image Retrieval
on DeepPatent
On visual self-supervision and its effect on model robustness
We identify primary ways in which self-supervision can be added to adversarial training, and observe that using a self-supervised loss to optimize both network parameters and find adversarial examples leads to the strongest improvement in model robustness, as this can be viewed as a form of ensemble adversarial training.
SpectroscopyNet: Learning to pre-process Spectroscopy Signals without clean data
In this work we propose a deep learning approach to clean spectroscopy signals using only uncleaned data.
Transfer learning with fewer ImageNet classes
Though much previous work tried to uncover the best practices for transfer learning, much is left unexplored.
Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra
Constructing probability densities for inference in high-dimensional spectral data is often intractable.
Diff2Dist: Learning Spectrally Distinct Edge Functions, with Applications to Cell Morphology Analysis
Because all steps involved in calculating this modified GDD are differentiable, we demonstrate that it is possible for a small neural network model to learn edge weights which minimize loss.
Deep Spectral CNN for Laser Induced Breakdown Spectroscopy
This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i. e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i. e., calibrate).
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses.
Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
We then use our small set of manually labeled patent diagram images via transfer learning to adapt the image search from sketches of natural images to diagrams.
Learning Spatial Relationships between Samples of Patent Image Shapes
Binary image based classification and retrieval of documents of an intellectual nature is a very challenging problem.
TGGLines: A Robust Topological Graph Guided Line Segment Detector for Low Quality Binary Images
Line segment detection is an essential task in computer vision and image analysis, as it is the critical foundation for advanced tasks such as shape modeling and road lane line detection for autonomous driving.
Ranked #1 on
Line Segment Detection
on wireframe dataset
The ISTI Rapid Response on Exploring Cloud Computing 2018
This report describes eighteen projects that explored how commercial cloud computing services can be utilized for scientific computation at national laboratories.
Quantum Algorithm Implementations for Beginners
As quantum computers become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classical computer programs for most of their careers.
Emerging Technologies Quantum Physics
Learning Planar Ising Models
Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering.
Controlling the Precision-Recall Tradeoff in Differential Dependency Network Analysis
We then show that by imposing a bias towards learning similar dependency networks for each condition the false discovery rates can be reduced to acceptable levels, at the cost of finding a reduced number of differences.
Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning
Bayesian network structure learning algorithms with limited data are being used in domains such as systems biology and neuroscience to gain insight into the underlying processes that produce observed data.
