Congratulations! Sandeep, Pranav and Rishabh for getting your paper accepted @ ICMLA 2021.
Sandeep and Pranav
Self-Attention mechanism in GANs for molecule generation
Abstract In discrete sequence-based Generative Adversarial Networks (GANs), it is important to both land the samples in the initial distribution and drive the generation towards desirable properties. However, in the case of longer molecules, the existing models seem to underperform in producing new molecules. In this work, we propose the use of a Self-Attention mechanism for Generative Adversarial Networks to allow long-range dependencies. Self-Attention mechanism has produced improved rewards in novelty and promising results in generating molecules.
Rishabh
Retrieval Enhanced Ensemble Model Framework For Rumor Detection On Micro-blogging Platforms
Abstract Automatic rumor detection is the task of finding rumors on social networks. Previous techniques leveraged the propagation structure of tweets to detect the rumors, which makes the propagation of tweets necessary to detect rumors. However, current text-based works provide sub-optimal results as compared to propagation-based techniques. This work presents a retrieval-based framework that leverages the similar tweets from the given train set and chooses the best model from an ensemble of models to predict the test tweet label. Our proposed framework is based on transformers-based pre-trained models (PTM’s). Experiments on two public data sets used in previous works, show that our framework can detect the tweets with equivalent accuracy as propagation-based techniques. The primary advantage of this work is in early rumor detection. The proposed framework can detect rumors in few minutes compared to propagation-based works, which requires a significant amount of propagation of tweets that can take hours before they can be detected.