Search Results for author: Kensuke Nakamura

Found 8 papers, 2 papers with code

Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy

no code implementations3 Sep 2023 Haimin Hu, Zixu Zhang, Kensuke Nakamura, Andrea Bajcsy, Jaime F. Fisac

An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance.

Autonomous Vehicles Reinforcement Learning (RL)

Emergent Coordination through Game-Induced Nonlinear Opinion Dynamics

1 code implementation5 Apr 2023 Haimin Hu, Kensuke Nakamura, Kai-Chieh Hsu, Naomi Ehrich Leonard, Jaime Fernández Fisac

We present a multi-agent decision-making framework for the emergent coordination of autonomous agents whose intents are initially undecided.

Decision Making

SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction

1 code implementation2 Oct 2021 Haimin Hu, Kensuke Nakamura, Jaime F. Fisac

Leveraging recent work on Bayesian human motion prediction, the resulting robot policy proactively balances nominal performance with the risk of high-cost emergency maneuvers triggered by low-probability human behaviors.

Human motion prediction Motion Planning +1

Generative Adversarial Networks via a Composite Annealing of Noise and Diffusion

no code implementations1 May 2021 Kensuke Nakamura, Simon Korman, Byung-Woo Hong

Based on these observations, we propose a data representation for the GAN training, called noisy scale-space (NSS), that recursively applies the smoothing with a balanced noise to data in order to replace the high-frequency information by random data, leading to a coarse-to-fine training of GANs.

Generative Adversarial Network

Regularization in network optimization via trimmed stochastic gradient descent with noisy label

no code implementations21 Dec 2020 Kensuke Nakamura, Bong-Soo Sohn, Kyoung-Jae Won, Byung-Woo Hong

The quantitative analysis is performed by comparing the behavior of the label noise, the example trimming, and the proposed algorithm.

Stochastic batch size for adaptive regularization in deep network optimization

no code implementations14 Apr 2020 Kensuke Nakamura, Stefano Soatto, Byung-Woo Hong

We propose a first-order stochastic optimization algorithm incorporating adaptive regularization applicable to machine learning problems in deep learning framework.

Image Classification Stochastic Optimization

Adaptive Weight Decay for Deep Neural Networks

no code implementations21 Jul 2019 Kensuke Nakamura, Byung-Woo Hong

Regularization in the optimization of deep neural networks is often critical to avoid undesirable over-fitting leading to better generalization of model.

Block-Cyclic Stochastic Coordinate Descent for Deep Neural Networks

no code implementations20 Nov 2017 Kensuke Nakamura, Stefano Soatto, Byung-Woo Hong

We present a stochastic first-order optimization algorithm, named BCSC, that adds a cyclic constraint to stochastic block-coordinate descent.

Cannot find the paper you are looking for? You can Submit a new open access paper.