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1. "Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer

   Hundt, Andrew ; Murali, Aditya ; Hubli, Priyanka ; Liu, Ran ; Gopalan, Nakul ; Gombolay, Matthew ; Hager, Gregory D. ( October 2021 , Proceedings of Machine Learning Research)

   Aleksandra Faust, David Hsu (Ed.)

   Modern Reinforcement Learning (RL) algorithms are not sample efficient to train on multi-step tasks in complex domains, impeding their wider deployment in the real world. We address this problem by leveraging the insight that RL models trained to complete one set of tasks can be repurposed to complete related tasks when given just a handful of demonstrations. Based upon this insight, we propose See-SPOT-Run (SSR), a new computational approach to robot learning that enables a robot to complete a variety of real robot tasks in novel problem domains without task-specific training. SSR uses pretrained RL models to create vectors thatmore »represent model, task, and action relevance in demonstration and test scenes. SSR then compares these vectors via our Cycle Consistency Distance (CCD) metric to determine the next action to take. SSR completes 58% more task steps and 20% more trials than a baseline few-shot learning method that requires task-specific training. SSR also achieves a four order of magnitude improvement in compute efficiency and a 20% to three order of magnitude improvement in sample efficiency compared to the baseline and to training RL models from scratch. To our knowledge, we are the first to address multi-step tasks from demonstration on a real robot without task-specific training, where both the visual input and action space output are high dimensional. Code is available in the supplement.« less
2. Transfer Learning for Design-Space Exploration with High-Level Synthesis

   https://doi.org/10.1145/3380446.3430636  

   Kwon, Jihye ; Carloni, Luca P. ( November 2020 , ACM/IEEE Workshop on Machine Learning for CAD)

   High-level synthesis (HLS) raises the level of design abstraction, expedites the process of hardware design, and enriches the set of final designs by automatically translating a behavioral specification into a hardware implementation. To obtain different implementations, HLS users can apply a variety of knobs, such as loop unrolling or function inlining, to particular code regions of the specification. The applied knob configuration significantly affects the synthesized design's performance and cost, e.g., application latency and area utilization. Hence, HLS users face the design-space exploration (DSE) problem, i.e. determine which knob configurations result in Pareto-optimal implementations in this multi-objective space. Whereas itmore »can be costly in time and resources to run HLS flows with an enormous number of knob configurations, machine learning approaches can be employed to predict the performance and cost. Still, they require a sufficient number of sample HLS runs. To enhance the training performance and reduce the sample complexity, we propose a transfer learning approach that reuses the knowledge obtained from previously explored design spaces in exploring a new target design space. We develop a novel neural network model for mixed-sharing multi-domain transfer learning. Experimental results demonstrate that the proposed model outperforms both single-domain and hard-sharing models in predicting the performance and cost at early stages of HLS-driven DSE.« less
3. A methodological approach for researching online teacher professional development

   Johnson, M. M. ( April 2021 , Middle Atlantic ASEE Section Spring 2021 Conference)

   COVID-19 has altered the landscape of teaching and learning. For those in in-service teacher education, workshops have been suspended causing programs to adapt their professional development to a virtual space to avoid indefinite postponement or cancellation. This paradigm shift in the way we conduct learning experiences creates several logistical and pedagogical challenges but also presents an important opportunity to conduct research about how learning happens in these new environments. This paper describes the approach we took to conduct research in a series of virtual workshops aimed at teaching rural elementary teachers about engineering practices and how to teach a unitmore »from an engineering curriculum. Our work explores how engineering concepts and practices are socially constructed through interactions with teachers, students, and artifacts. This approach, called interactional ethnography has been used by the authors and others to learn about engineering teaching and learning in precollege classrooms. The approach relies on collecting data during instruction, such as video and audio recordings, interviews, and artifacts such as journal entries and photos of physical designs. Findings are triangulated by analyzing these data sources. This methodology was going to be applied in an in-person engineering education workshop for rural elementary teachers, however the pandemic forced us to conduct the workshops remotely. Teachers, working in pairs, were sent workshop supplies, and worked together during the training series that took place over Zoom over four days for four hours each session. The paper describes how we collected video and audio of teachers and the facilitators both in whole group and in breakout rooms. Class materials and submissions of photos and evaluations were managed using Google Classroom. Teachers took photos of their work and scanned written materials and submitted them all by email. Slide decks were shared by the users and their group responses were collected in real time. Workshop evaluations were collected after each meeting using Google Forms. Evaluation data suggest that the teachers were engaged by the experience, learned significantly about engineering concepts and the knowledge-producing practices of engineers, and feel confident about applying engineering activities in their classrooms. This methodology should be of interest to the membership for three distinct reasons. First, remote instruction is a reality in the near-term but will likely persist in some form. Although many of us prefer to teach in person, remote learning allows us to reach many more participants, including those living in remote and rural areas who cannot easily attend in-person sessions with engineering educators, so it benefits the field to learn how to teach effectively in this way. Second, it describes an emerging approach to engineering education research. Interactional ethnography has been applied in precollege classrooms, but this paper demonstrates how it can also be used in teacher professional development contexts. Third, based on our application of interactional ethnography to an education setting, readers will learn specifically about how to use online collaborative software and how to collect and organize data sources for research purposes.« less
4. Metric Learning For Simulation Analytics

   Laidler, Graham ; Morgan, Lucy E ; Nelson, Barry L ; Pavlidis, Nicos G ( December 2020 , Proceedings of the Winter Simulation Conference)

   Bae, K-H ; Feng, B ; Kim, S ; Lazarova-Molnar, S ; Zheng, Z ; Roeder, T ; Thiesing, R (Ed.)

   The sample path generated by a stochastic simulation often exhibits significant variability within each replication, revealing periods of good and poor performance alike. As such, traditional summaries of aggregate performance measures overlook the more fine-grained insights into the operational system behavior. In this paper, we take a simulation analytics view of output analysis, turning to machine learning methods to uncover key insights from the dynamic sample path. We present a k nearest neighbors model on system state information to facilitate real-time predictions of a stochastic performance measure. This model is built on the premise of a system-specific measure of similaritymore »between observations of the state, which we inform via metric learning. An evaluation of our approach is provided on a stochastic activity network and a wafer fabrication facility, both of which give us confidence in the ability of metric learning to provide interpretation and improved predictive performance.« less
5. BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation

   https://doi.org/10.1145/3468268  

   Wu, Qiong ; Hare, Adam ; Wang, Sirui ; Tu, Yuwei ; Liu, Zhenming ; Brinton, Christopher G. ; Li, Yanhua ( October 2021 , ACM Transactions on Intelligent Systems and Technology)

   Existing topic modeling and text segmentation methodologies generally require large datasets for training, limiting their capabilities when only small collections of text are available. In this work, we reexamine the inter-related problems of “topic identification” and “text segmentation” for sparse document learning, when there is a single new text of interest. In developing a methodology to handle single documents, we face two major challenges. First is sparse information : with access to only one document, we cannot train traditional topic models or deep learning algorithms. Second is significant noise : a considerable portion of words in any single document willmore »produce only noise and not help discern topics or segments. To tackle these issues, we design an unsupervised, computationally efficient methodology called Biclustering Approach to Topic modeling and Segmentation (BATS). BATS leverages three key ideas to simultaneously identify topics and segment text: (i) a new mechanism that uses word order information to reduce sample complexity, (ii) a statistically sound graph-based biclustering technique that identifies latent structures of words and sentences, and (iii) a collection of effective heuristics that remove noise words and award important words to further improve performance. Experiments on six datasets show that our approach outperforms several state-of-the-art baselines when considering topic coherence, topic diversity, segmentation, and runtime comparison metrics.« less