b'Daniel D. Lee'

research

∙ 05/17/2023

Sparsity-depth Tradeoff in Infinitely Wide Deep Neural Networks

We investigate how sparse neural activity affects the generalization per...

0 Chanwoo Chun, et al. ∙

research

∙ 04/14/2023

EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks

Event-based sensors have recently drawn increasing interest in robotic p...

3 ZiYun Wang, et al. ∙

research

∙ 01/27/2023

Policy-Value Alignment and Robustness in Search-based Multi-Agent Learning

Large-scale AI systems that combine search and learning have reached sup...

0 Niko A. Grupen, et al. ∙

research

∙ 06/14/2022

A theory of learning with constrained weight-distribution

A central question in computational neuroscience is how structure determ...

0 Weishun Zhong, et al. ∙

research

∙ 09/14/2021

Object Shell Reconstruction: Camera-centric Object Representation for Robotic Grasping

Robots can effectively grasp and manipulate objects using their 3D model...

0 Nikhil Chavan Dafle, et al. ∙

research

∙ 06/21/2021

Curriculum-Driven Multi-Agent Learning and the Role of Implicit Communication in Teamwork

We propose a curriculum-driven learning strategy for solving difficult m...

6 Niko A. Grupen, et al. ∙

research

∙ 06/10/2021

Cooperative Multi-Agent Fairness and Equivariant Policies

We study fairness through the lens of cooperative multi-agent learning. ...

0 Niko A. Grupen, et al. ∙

research

∙ 06/05/2021

Local Disentanglement in Variational Auto-Encoders Using Jacobian L_1 Regularization

There have been many recent advances in representation learning; however...

0 Travers Rhodes, et al. ∙

research

∙ 03/20/2021

Learning Continuous Cost-to-Go Functions for Non-holonomic Systems

This paper presents a supervised learning method to generate continuous ...

8 Jinwook Huh, et al. ∙

research

∙ 12/10/2020

Cost-to-Go Function Generating Networks for High Dimensional Motion Planning

This paper presents c2g-HOF networks which learn to generate cost-to-go ...

2 Jinwook Huh, et al. ∙

research

∙ 11/30/2020

Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems

In this work, we study emergent communication through the lens of cooper...

5 Niko A. Grupen, et al. ∙

research

∙ 11/18/2020

Fast Motion Understanding with Spatiotemporal Neural Networks and Dynamic Vision Sensors

This paper presents a Dynamic Vision Sensor (DVS) based system for reaso...

8 Anthony Bisulco, et al. ∙

research

∙ 10/27/2020

Learning to Generate Cost-to-Go Functions for Efficient Motion Planning

Traditional motion planning is computationally burdensome for practical ...

0 Jinwook Huh, et al. ∙

research

∙ 06/14/2020

Geodesic-HOF: 3D Reconstruction Without Cutting Corners

Single-view 3D object reconstruction is a challenging fundamental proble...

34 ZiYun Wang, et al. ∙

research

∙ 04/03/2020

Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection

This paper presents a novel end-to-end system for pedestrian detection u...

0 Anthony Bisulco, et al. ∙

research

∙ 12/18/2019

Surface HOF: Surface Reconstruction from a Single Image Using Higher Order Function Networks

We address the problem of generating a high-resolution surface reconstru...

17 ZiYun Wang, et al. ∙

research

∙ 10/23/2019

Learning Q-network for Active Information Acquisition

In this paper, we propose a novel Reinforcement Learning approach for so...

28 Heejin Jeong, et al. ∙

research

∙ 10/04/2019

Higher Order Function Networks for View Planning and Multi-View Reconstruction

We consider the problem of planning views for a robot to acquire images ...

6 Selim Engin, et al. ∙

research

∙ 07/24/2019

Higher-Order Function Networks for Learning Composable 3D Object Representations

We present a method to represent 3D objects using higher order functions...

0 Eric Mitchell, et al. ∙

research

∙ 01/01/2019

Probabilistically Safe Corridors to Guide Sampling-Based Motion Planning

In this paper, we introduce a new probabilistically safe local steering ...

0 Jinwook Huh, et al. ∙

research

∙ 09/18/2018

U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification

Periodical inspection and maintenance of critical infrastructure such as...

6 Ty Nguyen, et al. ∙

research

∙ 07/21/2018

Learning Optimal Resource Allocations in Wireless Systems

This paper considers the design of optimal resource allocation policies ...

0 Mark Eisen, et al. ∙

research

∙ 06/06/2018

Learning Implicit Sampling Distributions for Motion Planning

Sampling-based motion planners have experienced much success due to thei...

0 Clark Zhang, et al. ∙

research

∙ 05/22/2018

Nearest neighbor density functional estimation based on inverse Laplace transform

A general approach to L_2-consistent estimation of various density funct...

0 Shouvik Ganguly, et al. ∙

research

∙ 05/22/2018

Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients

In this paper, we explore using deep reinforcement learning for problems...

0 Arbaaz Khan, et al. ∙

research

∙ 12/09/2017

Bayesian Q-learning with Assumed Density Filtering

While off-policy temporal difference methods have been broadly used in r...

1 Heejin Jeong, et al. ∙

research

∙ 10/17/2017

Classification and Geometry of General Perceptual Manifolds

Perceptual manifolds arise when a neural population responds to an ensem...

0 SueYeon Chung, et al. ∙

research

∙ 09/17/2017

Memory Augmented Control Networks

Planning problems in partially observable environments cannot be solved ...

0 Arbaaz Khan, et al. ∙

research

∙ 05/28/2017

Learning Data Manifolds with a Cutting Plane Method

We consider the problem of classifying data manifolds where each manifol...

0 SueYeon Chung, et al. ∙

research

∙ 12/06/2015

Linear Readout of Object Manifolds

Objects are represented in sensory systems by continuous manifolds due t...

0 SueYeon Chung, et al. ∙

research

∙ 05/26/2015

Belief Flows of Robust Online Learning

This paper introduces a new probabilistic model for online learning whic...

0 Pedro A. Ortega, et al. ∙

research

∙ 04/22/2014

An Adversarial Interpretation of Information-Theoretic Bounded Rationality

Recently, there has been a growing interest in modeling planning with in...

0 Pedro A. Ortega, et al. ∙

research

∙ 09/19/2011

Learning Discriminative Metrics via Generative Models and Kernel Learning

Metrics specifying distances between data points can be learned in a dis...

0 Yuan Shi, et al. ∙

Daniel D. Lee

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