33 citations · 47 across the 13 of their papers we have counts for
15 papers
Reinforcement learning
Sarod Yatawatta
Observing celestial objects and advancing our scientific knowledge about them involves tedious planning, scheduling, data collection and data post-processing. Many of these operati…
Neural Machine Translation for Code Generation
Dharma KC, Clayton T. Morrison
Neural machine translation (NMT) methods developed for natural language processing have been shown to be highly successful in automating translation from one natural language to an…
Validity Assessment of Legal Will Statements as Natural Language Inference
Alice Saebom Kwak, Jacob O. Israelsen, Clayton T. Morrison +2
This work introduces a natural language inference (NLI) dataset that focuses on the validity of statements in legal wills. This dataset is unique because: (a) each entailment decis…
Texture Generation Using A Graph Generative Adversarial Network And Differentiable Rendering
Dharma KC, Clayton T. Morrison, Bradley Walls
Novel photo-realistic texture synthesis is an important task for generating novel scenes, including asset generation for 3D simulations. However, to date, these methods predominant…
Learning Open Domain Multi-hop Search Using Reinforcement Learning
Enrique Noriega-Atala, Mihai Surdeanu, Clayton T. Morrison
We propose a method to teach an automated agent to learn how to search for multi-hop paths of relations between entities in an open domain. The method learns a policy for directing…
Neural Architectures for Biological Inter-Sentence Relation Extraction
Enrique Noriega-Atala, Peter M. Lovett, Clayton T. Morrison +1
We introduce a family of deep-learning architectures for inter-sentence relation extraction, i.e., relations where the participants are not necessarily in the same sentence. We app…
Federated Reconnaissance: Efficient, Distributed, Class-Incremental Learning
Sean M. Hendryx, Dharma Raj KC, Bradley Walls +1
We describe federated reconnaissance, a class of learning problems in which distributed clients learn new concepts independently and communicate that knowledge efficiently. In part…
AutoMATES: Automated Model Assembly from Text, Equations, and Software
Adarsh Pyarelal, Marco A. Valenzuela-Escarcega, Rebecca Sharp +6
Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations. But to be of real…
Meta-Learning Initializations for Image Segmentation
Sean M. Hendryx, Andrew B. Leach, Paul D. Hein +1
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 learn…
Inter-sentence Relation Extraction for Associating Biological Context with Events in Biomedical Texts
Enrique Noriega-Atala, Paul D. Hein, Shraddha S. Thumsi +3
We present an analysis of the problem of identifying biological context and associating it with biochemical events in biomedical texts. This constitutes a non-trivial, inter-senten…
WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-Hop Inference
Peter A. Jansen, Elizabeth Wainwright, Steven Marmorstein +1
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 d…
Composition by Conversation
Donya Quick, Clayton T. Morrison
Most musical programming languages are developed purely for coding virtual instruments or algorithmic compositions. Although there has been some work in the domain of musical query…
Learning what to read: Focused machine reading
Enrique Noriega-Atala, Marco A. Valenzuela-Escarcega, Clayton T. Morrison +1
Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into…
An Infinite Hidden Markov Model With Similarity-Biased Transitions
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 be…
Bayesian Inference of Recursive Sequences of Group Activities from Tracks
Ernesto Brau, Colin Dawson, Alfredo Carrillo +2
We present a probabilistic generative model for inferring a description of coordinated, recursively structured group activities at multiple levels of temporal granularity based on…