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Found 13 papers, 6 papers with code
GAN-MPC: Training Model Predictive Controllers with Parameterized Cost Functions using Demonstrations from Non-identical Experts
We propose a novel approach that uses a generative adversarial network (GAN) to minimize the Jensen-Shannon divergence between the state-trajectory distributions of the demonstrator and the imitator.
A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations
Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object.
On Learning to Rank Long Sequences with Contextual Bandits
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 and losses.
Unlocking Pixels for Reinforcement Learning via Implicit Attention
There has recently been significant interest in training reinforcement learning (RL) agents in vision-based environments.
MADRaS : Multi Agent Driving Simulator
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.
ExTra: Transfer-guided Exploration
Given an optimal policy in a related task-environment, we show that its bisimulation distance from the current task-environment gives a lower bound on the optimal advantage of state-action pairs in the current task-environment.
PUNCH: Positive UNlabelled Classification based information retrieval in Hyperspectral images
In order to address these issues, we aim to develop a framework for material-agnostic information retrieval in hyperspectral images based on Positive-Unlabelled (PU) classification.
RAIL: Risk-Averse Imitation Learning
Generative Adversarial Imitation Learning (GAIL) is a state-of-the-art algorithm for learning policies when the expert's behavior is available as a fixed set of trajectories.
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
Deep learning based landcover classification algorithms have recently been proposed in literature.
Ranked #12 on
Hyperspectral Image Classification
on Indian Pines
(Overall Accuracy metric)
WEPSAM: Weakly Pre-Learnt Saliency Model
Recent thrust in saliency prediction research is to learn high level semantics using ground truth eye fixation datasets.
Visualization Regularizers for Neural Network based Image Recognition
The success of deep neural networks is mostly due their ability to learn meaningful features from the data.
Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images
In this work we present a computational imaging framework using deep and ensemble learning for reliable detection of blood vessels in fundus color images.
Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training
In this paper a synchronized parallel algorithm for pre-training deep networks on multi-core machines has been proposed.
