Search Results for author: Eric Eaton

Found 21 papers, 5 papers with code

Gap Minimization for Knowledge Sharing and Transfer

no code implementations26 Jan 2022 Boyu Wang, Jorge Mendez, Changjian Shui, Fan Zhou, Di wu, Christian Gagné, Eric Eaton

In this paper, we introduce the notion of \emph{performance gap}, an intuitive and novel measure of the distance between learning tasks.

Representation Learning Transfer Learning

Modular Lifelong Reinforcement Learning via Neural Composition

no code implementations ICLR 2022 Jorge A Mendez, Harm van Seijen, Eric Eaton

Empirically, we demonstrate that neural composition indeed captures the underlying structure of this space of problems.

reinforcement-learning

Mako: Semi-supervised continual learning with minimal labeled data via data programming

1 code implementation29 Sep 2021 Pengyuan Lu, Seungwon Lee, Amanda Watson, David Kent, Insup Lee, Eric Eaton, James Weimer

This tool achieves similar performance, in terms of per-task accuracy and resistance to catastrophic forgetting, as compared to fully labeled data.

Continual Learning Image Classification

Towards a theory of out-of-distribution learning

no code implementations29 Sep 2021 Ali Geisa, Ronak Mehta, Hayden S. Helm, Jayanta Dey, Eric Eaton, Jeffery Dick, Carey E. Priebe, Joshua T. Vogelstein

This assumption renders these theories inadequate for characterizing 21$^{st}$ century real world data problems, which are typically characterized by evaluation distributions that differ from the training data distributions (referred to as out-of-distribution learning).

Learning Theory

Sparse PointPillars: Maintaining and Exploiting Input Sparsity to Improve Runtime on Embedded Systems

1 code implementation12 Jun 2021 Kyle Vedder, Eric Eaton

Bird's Eye View (BEV) is a popular representation for processing 3D point clouds, and by its nature is fundamentally sparse.

Birds Eye View Object Detection Object Detection

Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer

no code implementations ICML Workshop LifelongML 2020 Seungwon Lee, Sima Behpour, Eric Eaton

In deep networks, transferring the appropriate granularity of knowledge is as important as the transfer mechanism, and must be driven by the relationships among tasks.

Lifelong Learning of Compositional Structures

1 code implementation ICLR 2021 Jorge A. Mendez, Eric Eaton

A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and adequately reuse them in novel combinations for solving different yet structurally related problems.

Continual Learning

Lifelong Learning of Factored Policies via Policy Gradients

no code implementations ICML Workshop LifelongML 2020 Jorge A Mendez, Eric Eaton

Policy gradient methods have shown success in learning continuous control policies for high-dimensional dynamical systems.

Continuous Control Policy Gradient Methods

Transfer Learning via Minimizing the Performance Gap Between Domains

1 code implementation NeurIPS 2019 Boyu Wang, Jorge Mendez, Mingbo Cai, Eric Eaton

We propose a new principle for transfer learning, based on a straightforward intuition: if two domains are similar to each other, the model trained on one domain should also perform well on the other domain, and vice versa.

Generalization Bounds Transfer Learning

Zero-Shot Image Classification Using Coupled Dictionary Embedding

no code implementations10 Jun 2019 Mohammad Rostami, Soheil Kolouri, Zak Murez, Yuri Owekcho, Eric Eaton, Kuyngnam Kim

Zero-shot learning (ZSL) is a framework to classify images belonging to unseen classes based on solely semantic information about these unseen classes.

Classification Dictionary Learning +4

Artificial Intelligence for Pediatric Ophthalmology

no code implementations6 Apr 2019 Julia E. Reid, Eric Eaton

KEYWORDS: pediatric ophthalmology, machine learning, artificial intelligence, deep learning

Image Generation

Lifelong Inverse Reinforcement Learning

no code implementations NeurIPS 2018 Jorge Armando Mendez Mendez, Shashank Shivkumar, Eric Eaton

Methods for learning from demonstration (LfD) have shown success in acquiring behavior policies by imitating a user.

reinforcement-learning

Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Trees

no code implementations18 Nov 2017 José Marcio Luna, Eric Eaton, Lyle H. Ungar, Eric Diffenderfer, Shane T. Jensen, Efstathios D. Gennatas, Mateo Wirth, Charles B. Simone II, Timothy D. Solberg, Gilmer Valdes

Additive models, such as produced by gradient boosting, and full interaction models, such as classification and regression trees (CART), are widely used algorithms that have been investigated largely in isolation.

Additive models General Classification

Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer

no code implementations10 Oct 2017 David Isele, Mohammad Rostami, Eric Eaton

Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of the inter-task relationships to identify the relevant knowledge to transfer.

Dictionary Learning Transfer Learning +1

Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition

no code implementations15 Sep 2017 Mohammad Rostami, Soheil Kolouri, Kyungnam Kim, Eric Eaton

Lifelong machine learning methods acquire knowledge over a series of consecutive tasks, continually building upon their experience.

Multi-Task Learning

Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program

no code implementations1 Feb 2017 Eric Eaton, Sven Koenig, Claudia Schulz, Francesco Maurelli, John Lee, Joshua Eckroth, Mark Crowley, Richard G. Freedman, Rogelio E. Cardona-Rivera, Tiago Machado, Tom Williams

The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia).

Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data

no code implementations18 Oct 2016 Decebal Constantin Mocanu, Maria Torres Vega, Eric Eaton, Peter Stone, Antonio Liotta

Conceived in the early 1990s, Experience Replay (ER) has been shown to be a successful mechanism to allow online learning algorithms to reuse past experiences.

online learning reinforcement-learning

Estimating 3D Trajectories from 2D Projections via Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machines

no code implementations20 Apr 2016 Decebal Constantin Mocanu, Haitham Bou Ammar, Luis Puig, Eric Eaton, Antonio Liotta

Estimation, recognition, and near-future prediction of 3D trajectories based on their two dimensional projections available from one camera source is an exceptionally difficult problem due to uncertainty in the trajectories and environment, high dimensionality of the specific trajectory states, lack of enough labeled data and so on.

Future prediction Time Series

Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret

no code implementations21 May 2015 Haitham Bou Ammar, Rasul Tutunov, Eric Eaton

Lifelong reinforcement learning provides a promising framework for developing versatile agents that can accumulate knowledge over a lifetime of experience and rapidly learn new tasks by building upon prior knowledge.

reinforcement-learning

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