Paper Group ANR 755
Conversation Generation with Concept Flow. Deep Tensor Factorization for Spatially-Aware Scene Decomposition. A Graph-Based Framework to Bridge Movies and Synopses. Hamming Sentence Embeddings for Information Retrieval. Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval. A Rule-Based System for Explainable Donor-Patient Matching i …
Conversation Generation with Concept Flow
| Title | Conversation Generation with Concept Flow |
| Authors | Houyu Zhang, Zhenghao Liu, Chenyan Xiong, Zhiyuan Liu |
| Abstract | Human conversations naturally evolve around related entities and connected concepts, while may also shift from topic to topic. This paper presents ConceptFlow, which leverages commonsense knowledge graphs to explicitly model such conversation flows for better conversation response generation. ConceptFlow grounds the conversation inputs to the latent concept space and represents the potential conversation flow as a concept flow along the commonsense relations. The concept is guided by a graph attention mechanism that models the possibility of the conversation evolving towards different concepts. The conversation response is then decoded using the encodings of both utterance texts and concept flows, integrating the learned conversation structure in the concept space. Our experiments on Reddit conversations demonstrate the advantage of ConceptFlow over previous commonsense aware dialog models and fine-tuned GPT-2 models, while using much fewer parameters but with explicit modeling of conversation structures. |
| Tasks | Knowledge Graphs |
| Published | 2019-11-07 |
| URL | https://arxiv.org/abs/1911.02707v1 |
| https://arxiv.org/pdf/1911.02707v1.pdf | |
| PWC | https://paperswithcode.com/paper/conversation-generation-with-concept-flow |
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Deep Tensor Factorization for Spatially-Aware Scene Decomposition
| Title | Deep Tensor Factorization for Spatially-Aware Scene Decomposition |
| Authors | Jonah Casebeer, Michael Colomb, Paris Smaragdis |
| Abstract | We propose a completely unsupervised method to understand audio scenes observed with random microphone arrangements by decomposing the scene into its constituent sources and their relative presence in each microphone. To this end, we formulate a neural network architecture that can be interpreted as a nonnegative tensor factorization of a multi-channel audio recording. By clustering on the learned network parameters corresponding to channel content, we can learn sources’ individual spectral dictionaries and their activation patterns over time. Our method allows us to leverage deep learning advances like end-to-end training, while also allowing stochastic minibatch training so that we can feasibly decompose realistic audio scenes that are intractable to decompose using standard methods. This neural network architecture is easily extensible to other kinds of tensor factorizations. |
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| Published | 2019-05-03 |
| URL | https://arxiv.org/abs/1905.01391v2 |
| https://arxiv.org/pdf/1905.01391v2.pdf | |
| PWC | https://paperswithcode.com/paper/deep-tensor-factorization-for-spatially-aware |
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A Graph-Based Framework to Bridge Movies and Synopses
| Title | A Graph-Based Framework to Bridge Movies and Synopses |
| Authors | Yu Xiong, Qingqiu Huang, Lingfeng Guo, Hang Zhou, Bolei Zhou, Dahua Lin |
| Abstract | Inspired by the remarkable advances in video analytics, research teams are stepping towards a greater ambition – movie understanding. However, compared to those activity videos in conventional datasets, movies are significantly different. Generally, movies are much longer and consist of much richer temporal structures. More importantly, the interactions among characters play a central role in expressing the underlying story. To facilitate the efforts along this direction, we construct a dataset called Movie Synopses Associations (MSA) over 327 movies, which provides a synopsis for each movie, together with annotated associations between synopsis paragraphs and movie segments. On top of this dataset, we develop a framework to perform matching between movie segments and synopsis paragraphs. This framework integrates different aspects of a movie, including event dynamics and character interactions, and allows them to be matched with parsed paragraphs, based on a graph-based formulation. Our study shows that the proposed framework remarkably improves the matching accuracy over conventional feature-based methods. It also reveals the importance of narrative structures and character interactions in movie understanding. |
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| Published | 2019-10-24 |
| URL | https://arxiv.org/abs/1910.11009v1 |
| https://arxiv.org/pdf/1910.11009v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-graph-based-framework-to-bridge-movies-and-1 |
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Hamming Sentence Embeddings for Information Retrieval
| Title | Hamming Sentence Embeddings for Information Retrieval |
| Authors | Felix Hamann, Nadja Kurz, Adrian Ulges |
| Abstract | In retrieval applications, binary hashes are known to offer significant improvements in terms of both memory and speed. We investigate the compression of sentence embeddings using a neural encoder-decoder architecture, which is trained by minimizing reconstruction error. Instead of employing the original real-valued embeddings, we use latent representations in Hamming space produced by the encoder for similarity calculations. In quantitative experiments on several benchmarks for semantic similarity tasks, we show that our compressed hamming embeddings yield a comparable performance to uncompressed embeddings (Sent2Vec, InferSent, Glove-BoW), at compression ratios of up to 256:1. We further demonstrate that our model strongly decorrelates input features, and that the compressor generalizes well when pre-trained on Wikipedia sentences. We publish the source code on Github and all experimental results. |
| Tasks | Information Retrieval, Semantic Similarity, Semantic Textual Similarity, Sentence Embeddings |
| Published | 2019-08-15 |
| URL | https://arxiv.org/abs/1908.05541v1 |
| https://arxiv.org/pdf/1908.05541v1.pdf | |
| PWC | https://paperswithcode.com/paper/hamming-sentence-embeddings-for-information |
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Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval
| Title | Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval |
| Authors | Svebor Karaman, Xudong Lin, Xuefeng Hu, Shih-Fu Chang |
| Abstract | We propose an unsupervised hashing method which aims to produce binary codes that preserve the ranking induced by a real-valued representation. Such compact hash codes enable the complete elimination of real-valued feature storage and allow for significant reduction of the computation complexity and storage cost of large-scale image retrieval applications. Specifically, we learn a neural network-based model, which transforms the input representation into a binary representation. We formalize the training objective of the network in an intuitive and effective way, considering each training sample as a query and aiming to obtain the same retrieval results using the produced hash codes as those obtained with the original features. This training formulation directly optimizes the hashing model for the target usage of the hash codes it produces. We further explore the addition of a decoder trained to obtain an approximated reconstruction of the original features. At test time, we retrieved the most promising database samples with an efficient graph-based search procedure using only our hash codes and perform re-ranking using the reconstructed features, thus without needing to access the original features at all. Experiments conducted on multiple publicly available large-scale datasets show that our method consistently outperforms all compared state-of-the-art unsupervised hashing methods and that the reconstruction procedure can effectively boost the search accuracy with a minimal constant additional cost. |
| Tasks | Image Retrieval |
| Published | 2019-03-04 |
| URL | http://arxiv.org/abs/1903.01545v1 |
| http://arxiv.org/pdf/1903.01545v1.pdf | |
| PWC | https://paperswithcode.com/paper/unsupervised-rank-preserving-hashing-for |
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A Rule-Based System for Explainable Donor-Patient Matching in Liver Transplantation
| Title | A Rule-Based System for Explainable Donor-Patient Matching in Liver Transplantation |
| Authors | Felicidad Aguado, Pedro Cabalar, Jorge Fandinno, Brais Muñiz, Gilberto Pérez, Francisco Suárez |
| Abstract | In this paper we present web-liver, a rule-based system for decision support in the medical domain, focusing on its application in a liver transplantation unit for implementing policies for donor-patient matching. The rule-based system is built on top of an interpreter for logic programs with partial functions, called lppf, that extends the paradigm of Answer Set Programming (ASP) adding two main features: (1) the inclusion of partial functions and (2) the computation of causal explanations for the obtained solutions. The final goal of web-liver is assisting the medical experts in the design of new donor-patient matching policies that take into account not only the patient severity but also the transplantation utility. As an example, we illustrate the tool behaviour with a set of rules that implement the utility index called SOFT. |
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| Published | 2019-09-18 |
| URL | https://arxiv.org/abs/1909.08248v1 |
| https://arxiv.org/pdf/1909.08248v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-rule-based-system-for-explainable-donor |
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DCT Maps: Compact Differentiable Lidar Maps Based on the Cosine Transform
| Title | DCT Maps: Compact Differentiable Lidar Maps Based on the Cosine Transform |
| Authors | Alexander Schaefer, Lukas Luft, Wolfram Burgard |
| Abstract | Most robot mapping techniques for lidar sensors tessellate the environment into pixels or voxels and assume uniformity of the environment within them. Although intuitive, this representation entails disadvantages: The resulting grid maps exhibit aliasing effects and are not differentiable. In the present paper, we address these drawbacks by introducing a novel mapping technique that does neither rely on tessellation nor on the assumption of piecewise uniformity of the space, without increasing memory requirements. Instead of representing the map in the position domain, we store the map parameters in the discrete frequency domain and leverage the continuous extension of the inverse discrete cosine transform to convert them to a continuously differentiable scalar field in the position domain, which we call DCT map. A DCT map assigns to each point in space a lidar decay rate, which models the local permeability of the space for laser rays. In this way, the map can describe objects of different laser permeabilities, from completely opaque to completely transparent. DCT maps represent lidar measurements significantly more accurate than grid maps, Gaussian process occupancy maps, and Hilbert maps, all with the same memory requirements, as demonstrated in our real-world experiments. |
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| Published | 2019-10-23 |
| URL | https://arxiv.org/abs/1910.11147v1 |
| https://arxiv.org/pdf/1910.11147v1.pdf | |
| PWC | https://paperswithcode.com/paper/dct-maps-compact-differentiable-lidar-maps |
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Viable Dependency Parsing as Sequence Labeling
| Title | Viable Dependency Parsing as Sequence Labeling |
| Authors | Michalina Strzyz, David Vilares, Carlos Gómez-Rodríguez |
| Abstract | We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BiLSTM-based model it is possible to obtain fast and accurate parsers. These parsers are conceptually simple, not needing traditional parsing algorithms or auxiliary structures. However, experiments on the PTB and a sample of UD treebanks show that they provide a good speed-accuracy tradeoff, with results competitive with more complex approaches. |
| Tasks | Dependency Parsing |
| Published | 2019-02-27 |
| URL | http://arxiv.org/abs/1902.10505v2 |
| http://arxiv.org/pdf/1902.10505v2.pdf | |
| PWC | https://paperswithcode.com/paper/viable-dependency-parsing-as-sequence |
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NIF: A Framework for Quantifying Neural Information Flow in Deep Networks
| Title | NIF: A Framework for Quantifying Neural Information Flow in Deep Networks |
| Authors | Brian Davis, Umang Bhatt, Kartikeya Bhardwaj, Radu Marculescu, Jose Moura |
| Abstract | In this paper, we present a new approach to interpreting deep learning models. More precisely, by coupling mutual information with network science, we explore how information flows through feed forward networks. We show that efficiently approximating mutual information via the dual representation of Kullback-Leibler divergence allows us to create an information measure that quantifies how much information flows between any two neurons of a deep learning model. To that end, we propose NIF, Neural Information Flow, a new metric for codifying information flow which exposes the internals of a deep learning model while providing feature attributions. |
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| Published | 2019-01-20 |
| URL | http://arxiv.org/abs/1901.08557v1 |
| http://arxiv.org/pdf/1901.08557v1.pdf | |
| PWC | https://paperswithcode.com/paper/nif-a-framework-for-quantifying-neural |
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Rumor Detection on Social Media: Datasets, Methods and Opportunities
| Title | Rumor Detection on Social Media: Datasets, Methods and Opportunities |
| Authors | Quanzhi Li, Qiong Zhang, Luo Si, Yingchi Liu |
| Abstract | Social media platforms have been used for information and news gathering, and they are very valuable in many applications. However, they also lead to the spreading of rumors and fake news. Many efforts have been taken to detect and debunk rumors on social media by analyzing their content and social context using machine learning techniques. This paper gives an overview of the recent studies in the rumor detection field. It provides a comprehensive list of datasets used for rumor detection, and reviews the important studies based on what types of information they exploit and the approaches they take. And more importantly, we also present several new directions for future research. |
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| Published | 2019-11-17 |
| URL | https://arxiv.org/abs/1911.07199v1 |
| https://arxiv.org/pdf/1911.07199v1.pdf | |
| PWC | https://paperswithcode.com/paper/rumor-detection-on-social-media-datasets-1 |
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Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI
| Title | Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI |
| Authors | Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, Alexander Gray |
| Abstract | The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and pre-processing data, engineering new features, and creating and scoring models based on a target objectives (e.g. accuracy or run-time efficiency). Though not yet widely adopted, we are interested in understanding how AutoAI will impact the practice of data science. We conducted interviews with 20 data scientists who work at a large, multinational technology company and practice data science in various business settings. Our goal is to understand their current work practices and how these practices might change with AutoAI. Reactions were mixed: while informants expressed concerns about the trend of automating their jobs, they also strongly felt it was inevitable. Despite these concerns, they remained optimistic about their future job security due to a view that the future of data science work will be a collaboration between humans and AI systems, in which both automation and human expertise are indispensable. |
| Tasks | AutoML |
| Published | 2019-09-05 |
| URL | https://arxiv.org/abs/1909.02309v1 |
| https://arxiv.org/pdf/1909.02309v1.pdf | |
| PWC | https://paperswithcode.com/paper/human-ai-collaboration-in-data-science |
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Multi-Objective Automatic Machine Learning with AutoxgboostMC
| Title | Multi-Objective Automatic Machine Learning with AutoxgboostMC |
| Authors | Florian Pfisterer, Stefan Coors, Janek Thomas, Bernd Bischl |
| Abstract | AutoML systems are currently rising in popularity, as they can build powerful models without human oversight. They often combine techniques from many different sub-fields of machine learning in order to find a model or set of models that optimize a user-supplied criterion, such as predictive performance. The ultimate goal of such systems is to reduce the amount of time spent on menial tasks, or tasks that can be solved better by algorithms while leaving decisions that require human intelligence to the end-user. In recent years, the importance of other criteria, such as fairness and interpretability, and many others have become more and more apparent. Current AutoML frameworks either do not allow to optimize such secondary criteria or only do so by limiting the system’s choice of models and preprocessing steps. We propose to optimize additional criteria defined by the user directly to guide the search towards an optimal machine learning pipeline. In order to demonstrate the need and usefulness of our approach, we provide a simple multi-criteria AutoML system and showcase an exemplary application. |
| Tasks | AutoML |
| Published | 2019-08-28 |
| URL | https://arxiv.org/abs/1908.10796v1 |
| https://arxiv.org/pdf/1908.10796v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-objective-automatic-machine-learning |
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AutoML: A Survey of the State-of-the-Art
| Title | AutoML: A Survey of the State-of-the-Art |
| Authors | Xin He, Kaiyong Zhao, Xiaowen Chu |
| Abstract | Deep-learning techniques have penetrated all aspects of our lives and brought us great convenience. However, the process of building a high-quality deep-learning system for a specific task is time-consuming, requires extensive resources and relies on human expertise, hindering the further development of deep learning applications in both industry and academia. To alleviate this problem, a growing number of research projects focus on automated machine learning (AutoML). In this paper, we provide a comprehensive and up-to-date study on the state-of-the-art (SOTA) in AutoML. First, we introduce the AutoML techniques in detail, in relation to the machine-learning pipeline. We then summarize existing research on neural architecture search (NAS), as this is one of the most popular topics in the field of AutoML. We also compare the performance of models generated by NAS algorithms with that of human-designed models. Finally, we present several open problems for future research. |
| Tasks | AutoML, Neural Architecture Search |
| Published | 2019-08-02 |
| URL | https://arxiv.org/abs/1908.00709v4 |
| https://arxiv.org/pdf/1908.00709v4.pdf | |
| PWC | https://paperswithcode.com/paper/automl-a-survey-of-the-state-of-the-art |
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3-D Feature and Acoustic Modeling for Far-Field Speech Recognition
| Title | 3-D Feature and Acoustic Modeling for Far-Field Speech Recognition |
| Authors | Anurenjan Purushothaman, Anirudh Sreeram, Sriram Ganapathy |
| Abstract | Automatic speech recognition in multi-channel reverberant conditions is a challenging task. The conventional way of suppressing the reverberation artifacts involves a beamforming based enhancement of the multi-channel speech signal, which is used to extract spectrogram based features for a neural network acoustic model. In this paper, we propose to extract features directly from the multi-channel speech signal using a multi variate autoregressive (MAR) modeling approach, where the correlations among all the three dimensions of time, frequency and channel are exploited. The MAR features are fed to a convolutional neural network (CNN) architecture which performs the joint acoustic modeling on the three dimensions. The 3-D CNN architecture allows the combination of multi-channel features that optimize the speech recognition cost compared to the traditional beamforming models that focus on the enhancement task. Experiments are conducted on the CHiME-3 and REVERB Challenge dataset using multi-channel reverberant speech. In these experiments, the proposed 3-D feature and acoustic modeling approach provides significant improvements over an ASR system trained with beamformed audio (average relative improvements of 10 % and 9 % in word error rates for CHiME-3 and REVERB Challenge datasets respectively. |
| Tasks | Speech Recognition |
| Published | 2019-11-13 |
| URL | https://arxiv.org/abs/1911.05504v2 |
| https://arxiv.org/pdf/1911.05504v2.pdf | |
| PWC | https://paperswithcode.com/paper/3-d-feature-and-acoustic-modeling-for-far |
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Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees
| Title | Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees |
| Authors | Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song |
| Abstract | We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces. Compared to more classical sampling-based methods like RRT, our approach achieves much better sample efficiency in high-dimensions and can benefit from prior experience of planning in similar environments. More specifically, NEXT exploits a novel neural architecture which can learn promising search directions from problem structures. The learned prior is then integrated into a UCB-type algorithm to achieve an online balance between \emph{exploration} and \emph{exploitation} when solving a new problem. We conduct thorough experiments to show that NEXT accomplishes new planning problems with more compact search trees and significantly outperforms state-of-the-art methods on several benchmarks. |
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| Published | 2019-02-28 |
| URL | https://arxiv.org/abs/1903.00070v4 |
| https://arxiv.org/pdf/1903.00070v4.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-plan-via-neural-exploration |
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