Scholarships are a reflection of academic achievement for college students. We demonstrate that by using abductive learning, machines can learn to recognise numbers and resolve unknown mathematical operations simultaneously from images of simple handwritten equations. Perception and reasoning are two representative abilities of intelligence that are integrated seamlessly during problem-solving processes. Zhi-Hua Zhou, Perception and reasoning are two representative abilities of intelligence that are integrated seamlessly during human problem-solving processes. This paper is the first step of a work in progress aiming at a better mutual understanding of research in KRR and ML, and how they could cooperate. This allows the SSA model to focus on the most important images of a sneaker for use in identification. We adopt a neural network-based object detection model to simultaneously localize and classify textual information. NSL finally fills these discoveries of target diseases into a unified template, successfully achieving a comprehensive medical report generation. Most of the existing attribute reduction methods for ordinal decision tables are based on the dominance rough set theory or significance measures. A rich variety of different formalisms and learning techniques have been developed. To add evaluation results you first need to. ... Theoretically, it is innovative because this combination endows the superiority of the NSL framework that integrates the advantages of neural learning on noisy data processing and the logical reasoning on knowledge representation. In multi-dimensional classification (MDC), each training example is represented by a single instance (feature vector) while associated with multiple class variables, each of which specifies its class membership w.r.t. NSL secondly conducts human-like symbolic logical reasoning that realizes unsupervised causal effect analysis of detected entities of abnormalities through meta-interpretive learning. Prior methods learn the neural-symbolic models using reinforcement learning (RL) approaches, which ignore the error propagation in the symbolic reasoning module and thus converge slowly with sparse rewards. Based on this model, we then propose to exploit both uncertainty and diversity in the instance space as well as the label space, and actively query the instance-label pairs which can improve the classification model most. Our result suggests that gate in RNN is important but the less the better, which could be a guidance to design other RNNs. Thus, it is desired for machine learning techniques to work with weak supervision. However, in both scientific discovery and language learning potential applications exist in which OPL does not hold. Wang-Zhou Dai The case studies and expert interviews are conducted to demonstrate the effectiveness of CorVizor. This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developing quite separately in the last three decades. Moreover, the learned models can be generalised to longer equations and adapted to different tasks, which is beyond the capability of state-of-the-art deep learning models. This code is only tested in Linux environment. However, it has not been tested on extremely large-scale tasks. Bibliographic details on Bridging Machine Learning and Logical Reasoning by Abductive Learning. In this paper, we develop a Semi-Supervised Attention (SSA) model to work in conjunction with a large-scale multiple-source dataset named YSneaker, which consists of sneakers from various brands and their authentication results, to identify authentic sneakers. August 2019. To read the full-text of this research, you can request a copy directly from the author. Since logical reasoning and machine learning have almost been separately developed in the history of AI research, a fundamental idea to overcame beforementioned limitations is to unify them in a mutually beneficial way. Moreover, the learned models can be generalised to longer equations and adapted to different tasks, which is beyond the capability of state-of-the-art deep learning models. Existing studies mainly focus on layout algorithms that cluster related words, preserve temporal coherence, and optimize spatial shapes. The LASIN approach generates candidate hypotheses based on the abduction of first-order formulae, and then, the hypotheses are exploited as constraints for statistical induction. In this paper, we present the abductive learning targeted at unifying the two AI paradigms in a mutually beneficial way, where the machine learning model learns to perceive primitive logic facts from data, while logical reasoning can exploit symbolic domain knowledge and correct the wrongly perceived facts for improving the machine learning models. This work is a joint work with my PHD supervisor and colleagues in Nanjing University before my graduation. The second component is a visualization technique called CorView that implements a level-of-detail mechanism by integrating tailored visualizations to depict the extracted spatiotemporal co-occurrence patterns. Furthermore, we propose a novel approach to optimise the machine learning model and the logical reasoning model jointly. International Conference on Machine Learning… We develop a neuro-symbolic theorem prover that extracts multi-hop reasoning chains and apply it to this problem. In this work, we conjectures with theoretically support discussion, that, Following the recent successful examples of large technology companies, many modern enterprises seek to build knowledge graphs to provide a unified view of corporate knowledge and to draw deep insights using machine learning and logical reasoning. Such patterns present valuable implications for many urban applications, such as traffic management, pollution diagnosis, and transportation planning. Internet companies are facing the need for handling large-scale machine learning applications on a daily basis and distributed implementation of machine learning algorithms which can handle extra-large-scale tasks with great performance is widely needed. We first process the ribosome footprinting data to the training set. To validate the effectiveness of the proposed feature augmentation techniques, comprehensive comparative studies are conducted over fifteen benchmark data sets. Progol5.0 is tested on two different data-sets. The pivotal idea is to maximize the minimum margin of label pairs, which is extended from SVM. The experimental results show that DeepRibSt outperforms all other methods and achieves the state-of-the-art performance in accuracy, recall, specificity, F1-score, and the area under the receiver operating characteristic curve (AUC). In this paper, we explore animated word clouds that take advantage of storytelling strategies to present interactions between words and show the dynamic process of content changes, thus communicating the underlying stories. Bridging Machine Learning and Logical Reasoning by Abductive Learning; 用推理学习架起机器学习和逻辑推理的桥梁; 2019; 本人fork的git地址. To verify the validity of our proposed DeepRibSt, we compare DeepRibSt with four popular deep neural networks, i.e., AlexNet, LeNet, ResNet, and LSTM on human (i.e., Battle2015 and Stumpf13) and yeast (i.e., Pop2014, Young15, and Brar12) data. However, the crisp dominance relation is difficult in making full use of the information of attribute values; and the reducts based on significance measures are poor in interpretability and may contain unnecessary attributes. Yang Yu Extensive experiments on 20 datasets demonstrate the superiority of the proposed approach to state-of-the-art methods. In this work, we propose a reinforcement learning framework to train an AI agent that assists users in exploring the design space efficiently and generating well-optimized storylines. In this paper, we aim at developing a method of fusing ordinal decision trees with fuzzy rough set based attribute reduction. Therefore, active learning, which reduces the labeling cost by actively querying the labels of the most valuable data, becomes particularly important for multi-label learning. It will be beneficial if we can learn an interpretable structure from deep learning models. The advancement of deep learning techniques for fine-grained object recognition creates new possibilities for genuine product identification. In this study, we propose a new deep neural network model named DeepRibSt for the prediction of ribosome stalling sites. Machine Learning seminar. Concretely, we design an adversarial graph network that interpolates a symbolic graph reasoning module into a generative adversarial network through embedding prior domain knowledge, achieving semantic segmentation of spinal structures with high complexity and variability. At Man Group, we believe in the Python Ecosystem and have been trading Machine Learning based systems since early 2014. Enhancing Neural Mathematical Reasoning by Abductive Combination with Symbolic ... Wang, P. W., Donti, P. L., Wilder, B., & Kolter, Z. This article reviews some research progress of weakly supervised learning, focusing on three typical types of weak supervision: incomplete supervision where only a subset of training data are given with labels; inexact supervision where the training data are given with only coarse-grained labels; inaccurate supervision where the given labels are not always ground-truth. In this paper, we present the abductive learning targeted at unifying the two AI paradigms in a mutually beneficial way, where the machine learning model learns to perceive primitive logic facts from data, while logical reasoning can exploit symbolic domain knowledge and correct the wrongly perceived facts for improving the machine learning models. Based on the design space, we develop a prototype tool, DancingWords, which provides story-oriented interactions and automatic layouts for users to generate animated word clouds. Fig. Abductive learning: towards bridging machine learning and logical reasoning Zhi-Hua Zhou 1 Science China Information Sciences volume 62 , Article number: 76101 ( 2019 ) Cite this article We propose a novel and effective approach to handle concept drift via model reuse, that is, reusing models trained on previous data to tackle the changes. All rights reserved. In the area of artificial intelligence (AI), the two abilities are usually realised by machine learning and logic programming, respectively. Environment dependency. Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation, Bridging Machine Learning and Logical Reasoning by Abductive Learning *, Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning, Towards Better Detection and Analysis of Massive Spatiotemporal Co-Occurrence Patterns, Abductive Knowledge Induction From Raw Data, Conversational Neuro-Symbolic Commonsense Reasoning, An interactive feature selection method based on learning-from-crowds, PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning, Multi-label optimal margin distribution machine, Reverse-engineering Bar Charts Using Neural Networks, DeepRibSt: a multi-feature convolutional neural network for predicting ribosome stalling, A semi-supervised attention model for identifying authentic sneakers, From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group), Multi-Dimensional Classification via kNN Feature Augmentation, Learning With Interpretable Structure From Gated RNN, Design guidelines for augmenting short-form videos using animated data visualizations, Incremental Multi-Label Learning with Active Queries, You Are How You Behave – Spatiotemporal Representation Learning for College Student Academic Achievement, Cross-modal video moment retrieval based on visual-textual relationship alignment, DancingWords: exploring animated word clouds to tell stories, Distributed Deep Forest and its Application to Automatic Detection of Cash-Out Fraud, Theory Completion Using Inverse Entailment, Probabilistic Inductive Logic Programming, A Brief Introduction to Weakly Supervised Learning, Combining logic abduction and statistical induction: Discovering written primitives with human knowledge, Learnware: on the future of machine learning, Abductive cognition. The framework that is introduced is interesting and novel and combines deep learning for perception with abductive logical reasoning to provide weakly-labelled training data for the deep-learning perception component. Given the same amount of domain knowledge, we demonstrate that $Meta_{Abd}$ not only outperforms the compared end-to-end models in predictive accuracy and data efficiency but also induces logic programs that can be re-used as background knowledge in subsequent learning tasks. Specifically, simple counting statistics on the class membership of neighboring MDC examples as well as distance information between MDC examples and their k nearest neighbors are used to generate augmented feature vector. Bridging machine learning and logical reasoning by. Our initial implementation of the ABL framework shows that We evaluate the reinforcement learning model through qualitative and quantitative experiments and demonstrate the usage of PlotThread using a collection of use cases. OPL is ingrained within the theory and performance testing of Machine Learning. Abduction in machine learning means that it comes from a set of observations, and it tries to explain these observations with the best possible explanations. Based on the framework, we introduce PlotThread, an authoring tool that integrates a set of flexible interactions to support easy customization of storyline visualizations. Reverse-engineering bar charts extracts textual and numeric information from the visual representations of bar charts to support application scenarios that require the underlying information. This definition covers first-order logical inference or probabilistic inference. Abductive Learning for Handwritten Equation Decipherment. Short-form videos are an increasingly prevalent medium for storytelling in journalism and marketing, of which information can be greatly enhanced by animated data visualizations. However, the two categories of techniques were developed separately throughout most of the history of AI. Moreover, the learned models can be generalised to longer equations and adapted to different tasks, which is beyond the capability of state-of-the-art deep learning models. In multi-label learning, it is rather expensive to label instances since they are simultaneously associated with multiple labels. Synthetic and real-world datasets are used to evaluate the effectiveness of the method. The target of my research is to combine machine perception and machine reasoning, and make machine learning more powerful and interpretable. Extensive experiments in multiple multi-label evaluation metrics illustrate that mlODM outperforms SVM-style multi-label methods. Bridging Machine Learning and Logical Reasoning by Abductive Learning Speaker : Dr. Wang-Zhou Dai Abstract : Perception and reasoning are two representative abilities of intelligence that are integrated seamlessly during human problem-solving processes. Generally speaking, the NSL framework firstly employs deep neural learning to imitate human visual perception for detecting abnormalities of target spinal structures. In many real-world applications, data are often collected in the form of a stream, and thus the distribution usually changes in nature, which is referred to as concept drift in the literature. Bridging machine learning and logical reasoning by abductive learning. We initially create several exemplars of animated word clouds with designers through a structured iterative design process. To the best of our knowledge, this work takes the lead in constructing a complete neural network-based method of reverse-engineering bar charts. Motivation. In the area of artificial intelligence (AI), the two abilities are usually realised by machine learning and logic … We demonstrate that by using abductive learning, machines can learn to recognise numbers and resolve unknown mathematical operations simultaneously from images of simple hand-written equations. Bridging Machine Learning and Logical Reasoning by Abductive Learning The reviewer consensus was that, despite requiring some improvements in terms of presentation, with some areas flagged by reviewers as necessitating more detail, and the toy-ish nature of the experiments, that this paper addresses an important problem with the NeurIPS community in attempting to reconcile deep … Specifically, we first conduct feature engineering to generate a set of features to characterize the lifestyles patterns, learning patterns, and Internet usage patterns of students. To improve the performance of the algorithm in ribosome stalling prediction, we use two convolutional layers and three fully connected layers to design a new network architecture. However, the two categories of techniques were developed separately throughout most of the history of AI. Different from the previous works, ABL tries to bridge machine learning and logical reasoning in a mutually beneficial way. It can handle uncertainty in nominal or realvalued attributes and has been successfully applied to machine learning, logical reasoning, pattern recognition, intelligent information processing, and other fields [6]-. [14] Dai, Wang-Zhou, et al. Bridging Machine Learning and Logical Reasoning by Abductive Learning: Reviewer 1----- Comments after reading rebuttal: I've read the rebuttal and appreciate that the authors created a new … The traditional scholarship assignment is strictly based on final grades and cannot recognize students whose performance trend improves or declines during the semester. We further introduce an attention mechanism into the framework to achieve high accuracy and robustness. The objective of this work is to combine machine learning and logic-based reasoning with a new framework, which we call it Abductive Learning. However, the design of storyline visualizations is a difficult task as users need to balance between aesthetic goals and narrative constraints. As a result, they usually focus on learning the neural model with a sound and complete symbolic knowledge base while avoiding a crucial problem: where does the knowledge come from? Perception and reasoning are two representative abilities of intelligence that are integrated seamlessly during human problem-solving processes. CorVizor comprises two major components. We further develop an interactive conversational framework that evokes commonsense knowledge from humans for completing reasoning chains. Despite considerable efforts and successes witnessed in learning Boolean satisfiability (SAT), it remains an open question of learning GNN-based solvers for more complex predicate logic formulae. In this paper, we address these issues and close the loop of neural-symbolic learning by (1) introducing the \textbf{grammar} model as a \textit{symbolic prior} to bridge neural perception and symbolic reasoning, and (2) proposing a novel \textbf{back-search} algorithm which mimics the top-down human-like learning procedure to propagate the error through the symbolic reasoning module efficiently. In this paper, we integrate neural networks with Inductive Logic Programming (ILP) (Muggleton & de Raedt, 1994)-a general framework for symbolic machine learning-to enable first-order logic theory induction from raw data. In this paper, we present the abductive learning, where machine learning and logical reasoning can be entangled and mutually beneficial. Inspired by this idea, in this paper, we first introduce margin distribution to multi-label learning and propose multi-label Optimal margin Distribution Machine (mlODM), which optimizes the margin mean and variance of all label pairs efficiently. In this way, discriminative information from class space is expected to be brought into the feature space which would be helpful to the following MDC predictive model induction. In the area of artificial intelligence (AI), perception is usually realised by machine learning and reasoning … Finally, we conducted a crowd-sourcing study and a task-based evaluation to validate the effectiveness and usability of the guidelines. We tested the deep forest model on an extra-large-scale task, i.e., automatic detection of cash-out fraud, with more than 100 million training samples. © 2008-2020 ResearchGate GmbH. For many reasoning-heavy tasks, it is challenging to find an appropriate end-to-end differentiable approximation to domain-specific inference mechanisms. Tasks requiring joint perception and reasoning ability are difficult to accomplish autonomously and still demand human intervention.
2020 bridging machine learning and logical reasoning by abductive learning