As machine learning becomes more widely used in practice, we need new methods to build complex intelligent systems that integrate learning with existing software, and with domain knowledge encoded as rules. As a case study, we present such a system that learns to parse Newtonian physics problems in textbooks. This system, Nuts&Bolts, learns a pipeline process that incorporates existing code, pre-learned machine learning models, and human engineered rules. It jointly trains the entire pipeline to prevent propagation of errors, using a combination of labelled and unlabelled data. Our approach achieves a good performance on the parsing task, outperforming the simple pipeline and its variants. Finally, we also show how Nuts&Bolts can be used to achieve improvements on a relation extraction task and on the end task of answering Newtonian physics problems.

As machine learning becomes more widely used in practice, we need new methods to build complex intelligent systems that integrate learning with existing software, and with domain knowledge encoded as rules. As a case study, we present such a system that learns to parse Newtonian physics problems in textbooks. This system, Nuts&Bolts, learns a pipeline process that incorporates existing code, pre-learned machine learning models, and human engineered rules. It jointly trains the entire pipeline to prevent propagation of errors, using a combination of labelled and unlabelled data. Our approach achieves a good performance on the parsing task, outperforming the simple pipeline and its variants.

The main idea is the use of PSL (probabilistic soft logic) as a framework to map partial estimates from multiple feedforward algorithms, along with domain specific logical rules, to parse visual diagrams from physics texts. Specifically, the pipelines use feature extractors for lines, arcs, corners, text elements, object elements (e.g.blocks in physics diagrams). These are combined along with human specified rules for groupings, high-level elements, text/figure labeling schemes along with the inference engine to produce the parse into a formal logical language. Experiments illustrate how the learned system: 1) is superior to state of the art diagram parsing scheme, 2) can utilize labelled as well as unlabelled data to achieve improved performance, 3) can handle various degrees of supervision in different parts of the pipeline and is robust, and 4) through integrative modeling of the stages in pipeline prevents error propagation. Quality, Clarity, originality, significance of the paper: The paper is well written and has extensive references to relevant literature, adequate experimentation.

You can check the code for this tutorial here. Once you finish your model experimentation it is time to roll things to production. Rolling Machine Learning to production is not just a question of wrapping the model binaries with a REST API and starting to serve it, but and making it possible to re-create (or update) and re-deploy your model. That means the steps from preprocessing data to training the model to roll it to production (we call this a Machine Learning Pipeline) should be deployed and able to be run as easily as possible while making it possible to track it and parameterize it (to use different data, for example). In this post, we will see how to build a Machine Learning Pipeline for a Deep Learning model using Tensorflow Extended (TFx), how to run and deploy it to Google Vertex AI and why should we use it.

As machine learning becomes more widely used in practice, we need new methods to build complex intelligent systems that integrate learning with existing software, and with domain knowledge encoded as rules. As a case study, we present such a system that learns to parse Newtonian physics problems in textbooks. This system, Nuts&Bolts, learns a pipeline process that incorporates existing code, pre-learned machine learning models, and human engineered rules. It jointly trains the entire pipeline to prevent propagation of errors, using a combination of labelled and unlabelled data. Our approach achieves a good performance on the parsing task, outperforming the simple pipeline and its variants.

ABSTRACT State-of-the-art speaker diarization systems utilize knowledge from external data, in the form of a pre-trained distance metric, to effectively determine relative speaker identities to unseen data. However, much of recent focus has been on choosing the appropriate feature extractor, ranging from pre-trained i vectors to representations learned via different sequence modeling architectures (e.g. In this paper, we argue that, regardless of the feature extractor, it is crucial to carefully design a metric learning pipeline, namely the loss function, the sampling strategy and the discrimnative margin parameter, for building robust diarization systems. Furthermore, we propose to adopt a fine-grained validation process to obtain a comprehensive evaluation of the generalization power of metric learning pipelines. Using empirical studies, we provide interesting insights into the effectiveness of different design choices and make recommendations.