In this work, we describe the system developed by a group of undergraduates from the Indian Institutes of Technology for the Shared Task at TextGraphs-14 on Multi-Hop Inference Explanation Regeneration (Jansen and Ustalov, 2020).
An alternative is to develop an emulator, a surrogate model that can predict the Agent-Based Simulator's output based on its initial conditions and parameters.
We present a machine learning algorithm that discovers conservation laws from differential equations, both numerically (parametrized as neural networks) and symbolically, ensuring their functional independence (a non-linear generalization of linear independence).
We present the system description for our submission towards the Key Point Analysis Shared Task at ArgMining 2021.
Cognitively inspired Natural Language Pro-cessing uses human-derived behavioral datalike eye-tracking data, which reflect the seman-tic representations of language in the humanbrain to augment the neural nets to solve arange of tasks spanning syntax and semanticswith the aim of teaching machines about lan-guage processing mechanisms.