Search Results for author:
Found 12 papers, 3 papers with code
Generative AI for Rapid Diffusion MRI with Improved Image Quality, Reliability and Generalizability
Swin UNeTR enables rapid diffusion MRI with unprecedented accuracy and reliability, especially for probing biological tissues for scientific and clinical applications.
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
To our knowledge, this is the first application of a combined imitation and reinforcement learning approach in autonomous driving that utilizes large amounts of real-world human driving data.
Deep Reinforcement Learning in a Monetary Model
We propose using deep reinforcement learning to solve dynamic stochastic general equilibrium models.
Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects
Through experimentation and comparative study, we demonstrate the effectiveness of our approach in discovering robust and cost-efficient hand morphologies for grasping novel objects.
Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation
Here, we propose a zero-shot imitation learning approach for training a visual navigation policy on legged robots from human (third-person perspective) demonstrations, enabling high-quality navigation and cost-effective data collection.
Characterizing Attacks on Deep Reinforcement Learning
To the best of our knowledge, we are the first to apply adversarial attacks on DRL systems to physical robots.
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning
To the best of our knowledge, this is the first work to investigate privacy leakage in DRL settings and we show that DRL-based agents do potentially leak privacy-sensitive information from the trained policies.
Risk Averse Robust Adversarial Reinforcement Learning
In this paper we introduce risk-averse robust adversarial reinforcement learning (RARARL), using a risk-averse protagonist and a risk-seeking adversary.
Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data
First, we note that an optimal subset (relative to all the objects encountered and labeled) of labeled objects in images can be obtained by importance sampling using gradients of the recognition network.
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning
The learning process is interactive, with a human expert first providing input in the form of full demonstrations along with some subgoal states.
Virtual to Real Reinforcement Learning for Autonomous Driving
To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data.
An Efficient Minibatch Acceptance Test for Metropolis-Hastings
We present a novel Metropolis-Hastings method for large datasets that uses small expected-size minibatches of data.
