Loading the SOTA2 catalog…
SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Adversarial Robustness on In- and Out-Distribution Improves Explainability |
|---|
| Maximilian Augustin, Alexander Meinke, Matthias Hein |
| 2020 |
| arxiv 2003.09461 |
| Unified Characterization Platform for Emerging NVM Technology: Neural Network Application Benchmarking Using off-the-shelf NVM Chips | Supriya Chakraborty, Abhishek Gupta, Manan Suri | 2020 | arxiv 2006.05696 |
|---|
| On Connections between Regularizations for Improving DNN Robustness | Yiwen Guo, Long Chen, Yurong Chen | 2020 | arxiv 2007.02209 |
|---|
| LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units | Guangli Li, Lei Liu, Xueying Wang | 2020 | arxiv 2003.08646 |
|---|
| HASeparator: Hyperplane-Assisted Softmax | Ioannis Kansizoglou, Nicholas Santavas, Loukas Bampis | 2020 | arxiv 2008.03539 |
|---|
| Hierarchical Expert Networks for Meta-Learning | Heinke Hihn, Daniel A. Braun | 2019 | arxiv 1911.00348 |
|---|
| Noisy Batch Active Learning with Deterministic Annealing | Gaurav Gupta, Anit Kumar Sahu, Wan-Yi Lin | 2019 | arxiv 1909.12473 |
|---|
| Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks | Ali Mirzaeian, Jana Kosecka, Houman Homayoun | 2020 | arxiv 2006.15127 |
|---|
| Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation | Chen-Yu Lee, Tanmay Batra, Mohammad Haris Baig | 2019 | arxiv 1903.04064 |
|---|
| On Expected Accuracy | Ozan \.Irsoy | 2019 | arxiv 1905.00448 |
|---|