Papers by Yash Sharma
Attribute Diversity Determines the Systematicity Gap in VQA (2024.emnlp-main)
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| Challenge: | a systematicity gap exists between neural networks generalizing to new combinations of familiar concepts . conventionally trained neural networks struggle to generalize systematically . |
| Approach: | They propose to train a visual question answering model with CLEVR-HOPE as a diagnostic dataset to test this hypothesis. |
| Outcome: | The systematicity gap is reduced by increasing the diversity of training data, the authors show . the authors suggest that the more distinct attribute type combinations are seen during training, the more systematic the model will be. |
Generating Natural Language Adversarial Examples (D18-1)
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| Challenge: | Recent research has shown that deep neural networks are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. |
| Approach: | They propose to generate adversarial examples that fool well-trained sentiment analysis and textual entailment models by using a black-box population-based optimization algorithm. |
| Outcome: | The proposed model is able to fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively. |