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Recent Submissions

  • You can lead a horse to water...: Representing vs. Using Features in Neural NLP 

    Pavlick, Ellie (2021-03-24)
    A wave of recent work has sought to understand how pretrained language models work. Such analyses have resulted in two seemingly contradictory sets of results. On one hand, work based on "probing classifiers" generally ...
  • Compressed computation of good policies in large MDPs 

    Szepesvari, Csaba (2021-03-10)
    Markov decision processes (MDPs) is a minimalist framework to capture that many tasks require long-term plans and feedback due to noisy dynamics. Yet, as a result MDPs lack structure and as such planning and learning in ...
  • Learning Tree Models in Noise: Exact Asymptotics and Robust Algorithms 

    Tan, Vincent Y. F. (2021-02-10)
    We consider the classical problem of learning tree-structured graphical models but with the twist that the observations are corrupted in independent noise. For the case in which the noise is identically distributed, we ...
  • Interpretable latent space and inverse problem in deep generative models 

    Zhou, Bolei (2021-01-27)
    Recent progress in deep generative models such as Generative Adversarial Networks (GANs) has enabled synthesizing photo-realistic images, such as faces and scenes. However, it remains much less explored on what has been ...
  • Sound-Based Technologies and Strategies Used for Community Mobility by Adults with Vision Disability 

    Bruce, Carrie M.; Echt, Katherine (Georgia Institute of Technology, 2019)
    This investigation specifically examined adults’ self-reported use and usefulness of technologies (personal and public) and compensatory strategies for community mobility. The first study consisted of interviews with older ...
  • ML@GT Lab presents LAB LIGHTNING TALKS 2020 

    AlRegib, Ghassan; Chau, Duen Horng (Polo); Chava, Sudheer; Cohen, Morris; Davenport, Mark A.; Desai, Deven; Dovrolis, Constantine; Essa, Irfan A.; Gupta, Swati; Huo, Xiaoming; Kira, Zsolt; Li, Jing; Maguluri, Siva Theja; Pananjady, Ashwin; Prakash, B. Aditya; Riedl, Mark; Romberg, Justin K.; Xie, Yao; Zhang, Xiuwei (2020-12-04)
    Labs affiliated with the Machine Learning Center at Georgia Tech (ML@GT) will have the opportunity to share their research interests, work, and unique aspects of their lab in three minutes or less to interested graduate ...
  • Bringing Visual Memories to Life 

    Huang, Jia-Bin (2020-12-02)
    Photography allows us to capture and share memorable moments of our lives. However, 2D images appear flat due to the lack of depth perception and may suffer from poor imaging conditions such as taking photos through ...
  • Let’s Talk about Bias and Diversity in Data, Software, and Institutions 

    Deng, Tiffany; Desai, Deven; Gontijo Lopes, Raphael; Isbell, Charles L. (2020-11-20)
    Bias and lack of diversity have long been deep-rooted problems across industries. We discuss how these issues impact data, software, and institutions, and how we can improve moving forward. The panel will feature thought ...
  • Towards High Precision Text Generation 

    Parikh, Ankur (2020-11-11)
    Despite large advances in neural text generation in terms of fluency, existing generation techniques are prone to hallucination and often produce output that is unfaithful or irrelevant to the source text. In this talk, ...
  • Applying Emerging Technologies In Service of Journalism at The New York Times 

    Boonyapanachoti, Woraya (Mint); Dellaert, Frank; Essa, Irfan A.; Fleisher, Or; Kanazawa, Angjoo; Lavallee, Marc; McKeague, Mark; Porter, Lana Z. (2020-10-30)
    Emerging technologies, particularly within computer vision, photogrammetry, and spatial computing, are unlocking new forms of storytelling for journalists to help people understand the world around them. In this talk, ...
  • Reasoning about Complex Media from Weak Multi-modal Supervision 

    Kovashka, Adriana (2020-10-28)
    In a world of abundant information targeting multiple senses, and increasingly powerful media, we need new mechanisms to model content. Techniques for representing individual channels, such as visual data or textual data, ...
  • Active Learning: From Linear Classifiers to Overparameterized Neural Networks 

    Nowak, Robert (2020-10-07)
    The field of Machine Learning (ML) has advanced considerably in recent years, but mostly in well-defined domains using huge amounts of human-labeled training data. Machines can recognize objects in images and translate ...
  • Using rationales and influential training examples to (attempt to) explain neural predictions in NLP 

    Wallace, Byron (2020-09-09)
    Modern deep learning models for natural language processing (NLP) achieve state-of-the-art predictive performance but are notoriously opaque. I will discuss recent work looking to address this limitation. I will focus ...
  • A Review of the Research Literature on Evidence-Based Healthcare Design 

    Ulrich, Roger S.; Zimring, Craig M.; Zhu, Xuemei; DuBose, Jennifer R.; Seo, Hyun-Bo; Choi, Young-Seon; Quan, Xiaobo; Joseph, Anjali (Georgia Institute of Technology, 2008)
    This report surveys and evaluates the scientific research on evidence-based healthcare design and extracts its implications for designing better and safer hospitals.
  • Exploring the Concept of Healing Spaces 

    DuBose, Jennifer R.; MacAllister, Lorissa; Hadi, Khatereh; Sakallaris, Bonnie (Georgia Institute of Technology, 2018)
  • The Effect of Light on Sleep and Sleep-Related Physiological Factors Among Patients in Healthcare Facilities: A Systematic Review 

    Hadi, Khatereh; DuBose, Jennifer R.; Choi, Young-Seon (Georgia Institute of Technology, 2019)
    Lighting is one of the environmental factors which can improve patient sleep in healthcare environments. Many research studies have been published on this topic, but due to the high degree of variation in study designs and ...
  • Estratégias de Design para Unidades de Bio-conteção: Criando Ambientes Mais Seguros 

    Matić, Zorana; Humphreys, Benton; DuBose, Jennifer (Georgia Institute of Technology, 2020)
    Este artigo apresenta a síntese de uma pesquisa sobre o design de unidades de bio-contenção (UBC) que o SimTigrate Design Lab esteve engajado nos últimos 4 anos. Este documento propõe estratégias de design para projetar ...
  • 生物传染控制护理单元(Bio-Containment Unit): 从病人和医护人员的安全角度设计 

    Matić, Zorana; Humphreys, Benton; DuBose, Jennifer (Georgia Institute of Technology, 2020)
    本文对美国相关的生物传染控制护理单元的研究和设计进行了收集整理,希望可以对中国的医疗设计有所帮助。本文着重翻译并介绍佐治亚理工学院建筑系的SimTigrate医疗设计实验室与埃默里大学和乔治亚州立大学共同研究发布的生物传染控制护理单元(Bio-Containment Unit)设计白皮书 (Matić, Humphreys, DuBose, 2020),并结合几个案例来解释生物传染控制护理单元的核心设计要素。
  • Design Strategies for Biocontainment Units: Creating Safer Environments. Translated into Farsi. 

    Matić, Zorana; Humphreys, Benton; DuBose, Jennifer (Georgia Institute of Technology, 2020)
    This white paper presents a summary of the work on the design of biocontainment units (BCU) that the SimTigrate Design Lab has been engaged in for the past 4 years. This document outlines design strategies for designing a ...
  • 안전한 환경을 제공하기 위한 바이오 봉쇄병실 설계 전략 - Design Strategies for Biocontainment Units: Creating Safer Environments 

    Matić, Zorana; Humphreys, Benton; DuBose, Jennifer (Georgia Institute of Technology, 2020)
    본 공개 보고서는 SimTigrate Design Lab 에서 지난 4년간 바이오 봉쇄병실 (Bio-containment Unit, BCU) 설계에 대해 연구해온 결과를 요약한 것입니다. 본 문서는 디자이너들(건축가와 실내 건축가) 과 시설관리자, 그리고 설계 연구자들에게 보다 안전하고 효율적인 BCU을 디자인하기 위한 전략을제공하고자 합니다. 여기에서 제공되는 설계 전략들이 현재 그리고 ...

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