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      Utilizing Driving Context to Increase the Annotation Efficiency of Imbalanced Gaze Image Data
      Labeling and Crowdsourcing

      Utilizing Driving Context to Increase the Annotation Efficiency of Imbalanced Gaze Image Data

      Johannes Rehm, Odd Erik Gundersen, Kerstin Bach, Irina Reshodko Knowing where the driver of a car is looking, whether in a mirror or through the windshield, is important for advanced driver assistance systems and driving education applications. This problem can be addressed as a supervised classification task. However, in a typical dataset of driver video… Continue reading Utilizing Driving Context to Increase the Annotation Efficiency of Imbalanced Gaze Image Data

      Decreasing Annotation Burden of Pairwise Comparisons with Human-in-the-Loop Sorting: Application in Medical Image Artifact Rating
      Labeling and Crowdsourcing

      Decreasing Annotation Burden of Pairwise Comparisons with Human-in-the-Loop Sorting: Application in Medical Image Artifact Rating

      Ikbeom Jang, Garrison Danley, Ken Chang, Jayashree Kalpathy-Cramer Ranking by pairwise comparisons has shown improved reliability over ordinal classification. However, as the annotations of pairwise comparisons scale quadratically, this becomes less practical when the dataset is large. We propose a method for reducing the number of pairwise comparisons required to rank by a quantitative metric,… Continue reading Decreasing Annotation Burden of Pairwise Comparisons with Human-in-the-Loop Sorting: Application in Medical Image Artifact Rating

      Influence of human-expert labels on a neonatal seizure detector based on a convolutional neural network
      Labeling and Crowdsourcing

      Influence of human-expert labels on a neonatal seizure detector based on a convolutional neural network

      Ana Borovac, Thomas P Runarsson, Steinn Guðmundsson, Gardar Thorvardsson Neonatal seizures are common among infants and can be detected with an electroencephalogram (EEG). The EEG signals are complex time-series using multiple channels. Human domain experts are often in disagreement when labelling neonatal seizure data. Only few studies will include labels from multiple experts, as annotating… Continue reading Influence of human-expert labels on a neonatal seizure detector based on a convolutional neural network

      Finding Label Errors in Autonomous Vehicle Data With Learned Observation Assertions
      Data in Deployment

      Finding Label Errors in Autonomous Vehicle Data With Learned Observation Assertions

      Daniel Kang, Nikos Arechiga, Sudeep Pillai, Peter D Bailis, Matei Zaharia ML is being deployed in complex, real-world scenarios where errors have impactful consequences. As such, thorough testing of the ML pipelines is critical. A key component in ML deployment pipelines is the curation of labeled training data, which is assumed to be ground truth.… Continue reading Finding Label Errors in Autonomous Vehicle Data With Learned Observation Assertions

      Data Augmentation for Intent Classification
      Data Augmentation 

      Data Augmentation for Intent Classification

      Derek Chen, Claire Yin Training accurate intent classifiers requires labeled data, which can be costly to obtain. Data augmentation methods may ameliorate this issue, but the quality of the generated data varies significantly across techniques. We study the process of systematically producing pseudo-labeled data given a small seed set using a wide variety of data… Continue reading Data Augmentation for Intent Classification

      Comparing Data Augmentation and Annotation Standardization to Improve End-to-end Spoken Language Understanding Models
      Data Augmentation 

      Comparing Data Augmentation and Annotation Standardization to Improve End-to-end Spoken Language Understanding Models

      Leah Nicolich-Henkin, Taichi Nakatani , Zach Trozenski, Joel Whiteman, Nathan Susanj All-neural end-to-end (E2E) Spoken Language Understanding (SLU) models can improve performance over traditional compositional SLU models, but have the challenge of requiring high-quality training data with both audio and annotations. In particular they struggle with performance on “golden utterances”, which are essential for defining and supporting features, but may… Continue reading Comparing Data Augmentation and Annotation Standardization to Improve End-to-end Spoken Language Understanding Models

      Automatic Knowledge Augmentation for Generative Commonsense Reasoning
      Data Augmentation 

      Automatic Knowledge Augmentation for Generative Commonsense Reasoning

      Jaehyung Seo, Chanjun Park, Sugyeong Eo, Hyeonseok Moon, Heuiseok Lim Generative commonsense reasoning is the capability of a language model to generate a sentence with a given concept-set that is based on commonsense knowledge. However, generative language models still struggle to provide outputs, and the training set does not contain patterns that are sufficient for… Continue reading Automatic Knowledge Augmentation for Generative Commonsense Reasoning

      How Observability Uncovers the Effects of ML Technical Debt
      Data in Deployment

      How Observability Uncovers the Effects of ML Technical Debt

      Many teams test their machine learning models offline but conduct little to no online evaluation after initial deployment. These teams are flying blind—running production systems with no insight into their ongoing performance. Observability is the first step in measuring how this technical debt has come to bear on your business.

      Bernease Herman
      Danny D. Leybzon
      +1
      Bernease Herman, Danny D. Leybzon, Alessya Visnjic
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