Deep ReLU Networks Preserve Expected Length | 0 | 0.34 | 2022 |
Reverse-engineering deep ReLU networks | 0 | 0.34 | 2020 |
Randomized Experimental Design via Geographic Clustering. | 0 | 0.34 | 2019 |
Experience Replay for Continual Learning | 0 | 0.34 | 2019 |
Tackling Climate Change with Machine Learning. | 0 | 0.34 | 2019 |
Deep ReLU Networks Have Surprisingly Few Activation Patterns. | 1 | 0.36 | 2019 |
Complexity of Linear Regions in Deep Networks. | 3 | 0.37 | 2019 |
Measuring and regularizing networks in function space. | 1 | 0.35 | 2018 |
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique For 3-D Instance Segmentation In Connectomics | 0 | 0.34 | 2018 |
How to Start Training: The Effect of Initialization and Architecture. | 9 | 0.49 | 2018 |
Morphological Error Detection in 3D Segmentations. | 0 | 0.34 | 2017 |
The power of deeper networks for expressing natural functions. | 11 | 0.71 | 2017 |
Quantitative Combinatorial Geometry for Continuous Parameters. | 2 | 0.42 | 2017 |
Quantitative Tverberg Theorems Over Lattices and Other Discrete Sets. | 4 | 0.55 | 2017 |
On the classification of Stanley sequences | 1 | 0.38 | 2017 |
Deep Learning is Robust to Massive Label Noise. | 27 | 0.91 | 2017 |
Markov Transitions between Attractor States in a Recurrent Neural Network. | 0 | 0.34 | 2017 |
A Multi-Pass Approach to Large-Scale Connectomics. | 0 | 0.34 | 2016 |
GeoCUTS: Geographic Clustering Using Travel Statistics. | 0 | 0.34 | 2016 |
Algorithmic aspects of Tverberg's Theorem | 0 | 0.34 | 2016 |
Acyclic Subgraphs of Planar Digraphs | 0 | 0.34 | 2015 |
On the growth of Stanley sequences | 2 | 0.48 | 2015 |
Graph-Coloring Ideals: Nullstellensatz Certificates, Gröbner Bases for Chordal Graphs, and Hardness of Gröbner Bases | 2 | 0.37 | 2015 |
On the robust hardness of Gröbner basis computation. | 0 | 0.34 | 2015 |
The on-line degree Ramsey number of cycles. | 1 | 0.36 | 2013 |
Trees with an On-Line Degree Ramsey Number of Four. | 1 | 0.38 | 2011 |