| Index |
Papers |
Our Slides |
| 1 |
BIAS ALSO MATTERS: BIAS ATTRIBUTION FOR DEEP NEURAL NETWORK EXPLANATION |
Arsh Survey |
| 2 |
Data Shapley: Equitable Valuation of Data for Machine Learning |
Arsh Survey |
| |
What is your data worth? Equitable Valuation of Data |
Sanchit Survey |
| 3 |
Neural Network Attributions: A Causal Perspective |
Zhe Survey |
| 4 |
Defending Against Neural Fake News |
Eli Survey |
| 5 |
Interpretation of Neural Networks is Fragile |
Eli Survey |
| |
Interpretation of Neural Networks is Fragile |
Pan Survey |
| 6 |
Parsimonious Black-Box Adversarial Attacks Via Efficient Combinatorial Optimization |
Eli Survey |
| 7 |
Retrofitting Word Vectors to Semantic Lexicons |
Morris Survey |
| 8 |
On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models |
Morris Survey |
| 9 |
Towards Deep Learning Models Resistant to Adversarial Attacks |
Pan Survey |
| 10 |
Robust Attribution Regularization |
Pan Survey |
| 11 |
Sanity Checks for Saliency Maps |
Sanchit Survey |
| 12 |
Survey of data generation and evaluation in Interpreting DNN pipelines |
Sanchit Survey |
| 13 |
Think Architecture First: Benchmarking Deep Learning Interpretability in Time Series Predictions |
Sanchit Survey |
| 14 |
Universal Adversarial Triggers for Attacking and Analyzing NLP |
Sanchit Survey |
| 15 |
Apricot: Submodular selection for data summarization in Python |
Arsh Survey |