Search Results for author: Clayton T. Morrison

Found 10 papers, 3 papers with code

Federated Reconnaissance: Efficient, Distributed, Class-Incremental Learning

1 code implementation1 Sep 2021 Sean M. Hendryx, Dharma Raj KC, Bradley Walls, Clayton T. Morrison

We describe federated reconnaissance, a class of learning problems in which distributed clients learn new concepts independently and communicate that knowledge efficiently.

class-incremental learning Incremental Learning

AutoMATES: Automated Model Assembly from Text, Equations, and Software

1 code implementation21 Jan 2020 Adarsh Pyarelal, Marco A. Valenzuela-Escarcega, Rebecca Sharp, Paul D. Hein, Jon Stephens, Pratik Bhandari, HeuiChan Lim, Saumya Debray, Clayton T. Morrison

Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations.

Meta-Learning Initializations for Image Segmentation

1 code implementation13 Dec 2019 Sean M. Hendryx, Andrew B. Leach, Paul D. Hein, Clayton T. Morrison

We extend first-order model agnostic meta-learning algorithms (including FOMAML and Reptile) to image segmentation, present a novel neural network architecture built for fast learning which we call EfficientLab, and leverage a formal definition of the test error of meta-learning algorithms to decrease error on out of distribution tasks.

Few-Shot Image Classification Semantic Segmentation +1

Inter-sentence Relation Extraction for Associating Biological Context with Events in Biomedical Texts

no code implementations14 Dec 2018 Enrique Noriega-Atala, Paul D. Hein, Shraddha S. Thumsi, Zechy Wong, Xia Wang, Clayton T. Morrison

We present an analysis of the problem of identifying biological context and associating it with biochemical events in biomedical texts.

Relation Extraction

WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-Hop Inference

no code implementations LREC 2018 Peter A. Jansen, Elizabeth Wainwright, Steven Marmorstein, Clayton T. Morrison

Developing methods of automated inference that are able to provide users with compelling human-readable justifications for why the answer to a question is correct is critical for domains such as science and medicine, where user trust and detecting costly errors are limiting factors to adoption.

Question Answering

Composition by Conversation

no code implementations7 Sep 2017 Donya Quick, Clayton T. Morrison

Most musical programming languages are developed purely for coding virtual instruments or algorithmic compositions.

Information Retrieval Music Information Retrieval

Learning what to read: Focused machine reading

no code implementations EMNLP 2017 Enrique Noriega-Atala, Marco A. Valenzuela-Escarcega, Clayton T. Morrison, Mihai Surdeanu

In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible.

Reading Comprehension

An Infinite Hidden Markov Model With Similarity-Biased Transitions

no code implementations ICML 2017 Colin Reimer Dawson, Chaofan Huang, Clayton T. Morrison

We describe a generalization of the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) which is able to encode prior information that state transitions are more likely between "nearby" states.

Speaker Diarization

Bayesian Inference of Recursive Sequences of Group Activities from Tracks

no code implementations24 Apr 2016 Ernesto Brau, Colin Dawson, Alfredo Carrillo, David Sidi, Clayton T. Morrison

We present a probabilistic generative model for inferring a description of coordinated, recursively structured group activities at multiple levels of temporal granularity based on observations of individuals' trajectories.

Bayesian Inference

Blending Autonomous Exploration and Apprenticeship Learning

no code implementations NeurIPS 2011 Thomas J. Walsh, Daniel K. Hewlett, Clayton T. Morrison

We present theoretical and empirical results for a framework that combines the benefits of apprenticeship and autonomous reinforcement learning.

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