148 citations · 267 across the 12 of their papers we have counts for
13 papers
GAN-MPC: Training Model Predictive Controllers with Parameterized Cost Functions using Demonstrations from Non-identical Experts
Returaj Burnwal, Anirban Santara, Nirav P. Bhatt +2
Model predictive control (MPC) is a popular approach for trajectory optimization in practical robotics applications. MPC policies can optimize trajectory parameters under kinodynam…
A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations
Sohan Rudra, Saksham Goel, Anirban Santara +6
Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object. Object-nav has been extensively studied by the Embodied-AI community, but most…
On Learning to Rank Long Sequences with Contextual Bandits
Anirban Santara, Claudio Gentile, Gaurav Aggarwal +1
Motivated by problems of learning to rank long item sequences, we introduce a variant of the cascading bandit model that considers flexible length sequences with varying rewards an…
Unlocking Pixels for Reinforcement Learning via Implicit Attention
Krzysztof Marcin Choromanski, Deepali Jain, Wenhao Yu +9
There has recently been significant interest in training reinforcement learning (RL) agents in vision-based environments. This poses many challenges, such as high dimensionality an…
MADRaS : Multi Agent Driving Simulator
Anirban Santara, Sohan Rudra, Sree Aditya Buridi +4
In this work, we present MADRaS, an open-source multi-agent driving simulator for use in the design and evaluation of motion planning algorithms for autonomous driving. MADRaS prov…
ExTra: Transfer-guided Exploration
Anirban Santara, Rishabh Madan, Balaraman Ravindran +1
In this work we present a novel approach for transfer-guided exploration in reinforcement learning that is inspired by the human tendency to leverage experiences from similar encou…
PUNCH: Positive UNlabelled Classification based information retrieval in Hyperspectral images
Anirban Santara, Jayeeta Datta, Sourav Sarkar +3
Hyperspectral images of land-cover captured by airborne or satellite-mounted sensors provide a rich source of information about the chemical composition of the materials present in…
RAIL: Risk-Averse Imitation Learning
Anirban Santara, Abhishek Naik, Balaraman Ravindran +4
Imitation learning algorithms learn viable policies by imitating an expert's behavior when reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a s…
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
Anirban Santara, Kaustubh Mani, Pranoot Hatwar +4
Deep learning based landcover classification algorithms have recently been proposed in literature. In hyperspectral images (HSI) they face the challenges of large dimensionality, s…
WEPSAM: Weakly Pre-Learnt Saliency Model
Avisek Lahiri, Sourya Roy, Anirban Santara +2
Visual saliency detection tries to mimic human vision psychology which concentrates on sparse, important areas in natural image. Saliency prediction research has been traditionally…
Visualization Regularizers for Neural Network based Image Recognition
Biswajit Paria, Vikas Reddy, Anirban Santara +1
The success of deep neural networks is mostly due their ability to learn meaningful features from the data. Features learned in the hidden layers of deep neural networks trained in…
Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images
Debapriya Maji, Anirban Santara, Pabitra Mitra +1
Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large scale sc…
Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training
Anirban Santara, Debapriya Maji, DP Tejas +2
Deep neural networks are capable of modelling highly non-linear functions by capturing different levels of abstraction of data hierarchically. While training deep networks, first t…