Search Results for author: Amit Chakraborty

Found 14 papers, 1 papers with code

EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles

no code implementations12 Sep 2021 Haoran Su, Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty

EMVLight extends Dijkstra's algorithm to efficiently update the optimal route for the EMVs in real time as it travels through the traffic network.

Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models

no code implementations12 Feb 2021 Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty

In this paper, we introduce a differentiable contact model, which can capture contact mechanics: frictionless/frictional, as well as elastic/inelastic.

Legged Robots

Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data

no code implementations3 Dec 2020 Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty

The last few years have witnessed an increased interest in incorporating physics-informed inductive bias in deep learning frameworks.

Time Series

Hamiltonian Q-Learning: Leveraging Importance-sampling for Data Efficient RL

no code implementations11 Nov 2020 Udari Madhushani, Biswadip Dey, Naomi Ehrich Leonard, Amit Chakraborty

By providing an efficient way to apply Q-learning in stochastic, high-dimensional problems, the proposed approach broadens the scope of RL algorithms for real-world applications, including classical control tasks and environmental monitoring.

Matrix Completion Q-Learning

Frequency-compensated PINNs for Fluid-dynamic Design Problems

no code implementations3 Nov 2020 Tongtao Zhang, Biswadip Dey, Pratik Kakkar, Arindam Dasgupta, Amit Chakraborty

We demonstrate this approach by predicting simulation results over out of range time interval and for novel design conditions.

Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning

no code implementations20 Feb 2020 Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty

In this work, we introduce Dissipative SymODEN, a deep learning architecture which can infer the dynamics of a physical system with dissipation from observed state trajectories.

A Conditional Generative Model for Predicting Material Microstructures from Processing Methods

no code implementations4 Oct 2019 Akshay Iyer, Biswadip Dey, Arindam Dasgupta, Wei Chen, Amit Chakraborty

Microstructures of a material form the bridge linking processing conditions - which can be controlled, to the material property - which is the primary interest in engineering applications.

Feature Engineering Image Generation

Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

2 code implementations ICLR 2020 Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty

In this paper, we introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system, given by an ordinary differential equation (ODE), from observed state trajectories.

Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra

no code implementations3 Apr 2019 Amit Chakraborty, Sung Hak Lim, Mihoko M. Nojiri

Here we propose an interpretable network trained on the jet spectrum $S_{2}(R)$ which is a two-point correlation function of the jet constituents.

Classification General Classification

Proximal gradient method for huberized support vector machine

no code implementations30 Nov 2015 Yangyang Xu, Ioannis Akrotirianakis, Amit Chakraborty

The Support Vector Machine (SVM) has been used in a wide variety of classification problems.

Alternating direction method of multipliers for regularized multiclass support vector machines

no code implementations30 Nov 2015 Yangyang Xu, Ioannis Akrotirianakis, Amit Chakraborty

A lot of effort has been put to generalize the binary SVM to multiclass SVM (MSVM) which are more complex problems.

HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection

no code implementations16 Nov 2014 Zhiwei Qin, Xiaocheng Tang, Ioannis Akrotirianakis, Amit Chakraborty

We consider classification tasks in the regime of scarce labeled training data in high dimensional feature space, where specific expert knowledge is also available.

Feature Selection General Classification

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