28 citations · 68 across the 8 of their papers we have counts for
8 papers
A Machine Learning Based Search for Lunar Anomalies
Cameron Kelahan, Daniel Angerhausen, Adam Lesnikowski +1
The Lunar Reconnaissance Orbiter (LRO) has been collecting high-resolution images (at around 0.5-2 meters per pixel linearly with its Narrow Angle Camera) of the Moon since 2009, a…
Automated Discovery of Anomalous Features in Ultra-Large Planetary Remote Sensing Datasets using Variational Autoencoders
Adam Lesnikowski, Valentin T. Bickel, Daniel Angerhausen
The NASA Lunar Reconnaissance Orbiter (LRO) has returned petabytes of lunar high spatial resolution surface imagery over the past decade, impractical for humans to fully review man…
Synthetic Data and Simulators for Recommendation Systems: Current State and Future Directions
Adam Lesnikowski, Gabriel de Souza Pereira Moreira, Sara Rabhi +1
Synthetic data and simulators have the potential to markedly improve the performance and robustness of recommendation systems. These approaches have already had a beneficial impact…
Unsupervised Distribution Learning for Lunar Surface Anomaly Detection
Adam Lesnikowski, Valentin T. Bickel, Daniel Angerhausen
In this work we show that modern data-driven machine learning techniques can be successfully applied on lunar surface remote sensing data to learn, in an unsupervised way, sufficie…
Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization
Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scalable technique that uses a regularized ensemble to approximate a deep Bayesian Neural Network (BNN). We do so…
The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning
Jiaming Zeng, Adam Lesnikowski, Jose M. Alvarez
One of the main challenges of deep learning tools is their inability to capture model uncertainty. While Bayesian deep learning can be used to tackle the problem, Bayesian neural n…
Large-Scale Visual Active Learning with Deep Probabilistic Ensembles
Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
Annotating the right data for training deep neural networks is an important challenge. Active learning using uncertainty estimates from Bayesian Neural Networks (BNNs) could provid…
How Much Did it Rain? Predicting Real Rainfall Totals Based on Radar Data
Adam Lesnikowski
We applied a variety of parametric and non-parametric machine learning models to predict the probability distribution of rainfall based on 1M training examples over a single year a…