Search Results for author: Sahil Manchanda

Found 10 papers, 3 papers with code

Domain Informed Neural Machine Translation: Developing Translation Services for Healthcare Enterprise

no code implementations EAMT 2020 Sahil Manchanda, Galina Grunin

Neural Machine Translation (NMT) is a deep learning based approach that has achieved outstanding results lately in the translation community.

Machine Translation NMT +1

StriderNET: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes

no code implementations29 Jan 2023 Vaibhav Bihani, Sahil Manchanda, Srikanth Sastry, Sayan Ranu, N. M. Anoop Krishnan

Optimization of atomic structures presents a challenging problem, due to their highly rough and non-convex energy landscape, with wide applications in the fields of drug design, materials discovery, and mechanics.

Lifelong Learning for Neural powered Mixed Integer Programming

no code implementations24 Aug 2022 Sahil Manchanda, Sayan Ranu

In this work, we study the hitherto unexplored paradigm of Lifelong Learning to Branch on Mixed Integer Programs.

Graph Attention Knowledge Distillation

On the Generalization of Neural Combinatorial Optimization Heuristics

no code implementations1 Jun 2022 Sahil Manchanda, Sofia Michel, Darko Drakulic, Jean-Marc Andreoli

Neural Combinatorial Optimization approaches have recently leveraged the expressiveness and flexibility of deep neural networks to learn efficient heuristics for hard Combinatorial Optimization (CO) problems.

Combinatorial Optimization Meta-Learning

NeuroMLR: Robust & Reliable Route Recommendation on Road Networks

1 code implementation NeurIPS 2021 Jayant Jain, Vrittika Bagadia, Sahil Manchanda, Sayan Ranu

First, our study reveals that a significant portion of the routes recommended by existing methods fail to reach the destination.

SUPAID: A Rule mining based method for automatic rollout decision aid for supervisors in fleet management systems

no code implementations10 Jan 2020 Sahil Manchanda, Arun Rajkumar, Simarjot Kaur, Narayanan Unny

The decision to rollout a vehicle is critical to fleet management companies as wrong decisions can lead to additional cost of maintenance and failures during journey.


Learning Heuristics over Large Graphs via Deep Reinforcement Learning

2 code implementations NeurIPS 2020 Sahil Manchanda, Akash Mittal, Anuj Dhawan, Sourav Medya, Sayan Ranu, Ambuj Singh

Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality.

Combinatorial Optimization Q-Learning +2

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