Search Results for author: Varun Madhavan

Found 7 papers, 4 papers with code

ChiSquareX at TextGraphs 2020 Shared Task: Leveraging Pretrained Language Models for Explanation Regeneration

1 code implementation COLING (TextGraphs) 2020 Aditya Girish Pawate, Varun Madhavan, Devansh Chandak

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).

Unveiling the Power of Self-Attention for Shipping Cost Prediction: The Rate Card Transformer

1 code implementation20 Nov 2023 P Aditya Sreekar, Sahil Verma, Varun Madhavan, Abhishek Persad

Shipping cost of these packages are used on the day of shipping (day 0) to estimate profitability of sales.

Deep Learning-based Spatially Explicit Emulation of an Agent-Based Simulator for Pandemic in a City

no code implementations28 May 2022 Varun Madhavan, Adway Mitra, Partha Pratim Chakrabarti

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.

AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations

no code implementations23 Mar 2022 Ziming Liu, Varun Madhavan, Max Tegmark

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).

BIG-bench Machine Learning

Leveraging recent advances in Pre-Trained Language Models forEye-Tracking Prediction

1 code implementation9 Oct 2021 Varun Madhavan, Aditya Girish Pawate, Shraman Pal, Abhranil Chandra

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.

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