Paper Group NANR 208
Improved nearest neighbor search using auxiliary information and priority functions. The UIR Uncertainty Corpus for Chinese: Annotating Chinese Microblog Corpus for Uncertainty Identification from Social Media. Using Crowd Agreement for Wordnet Localization. Knowing the Author by the Company His Words Keep. Training with Growing Sets: A Simple Alte …
Improved nearest neighbor search using auxiliary information and priority functions
| Title | Improved nearest neighbor search using auxiliary information and priority functions |
| Authors | Omid Keivani, Kaushik Sinha |
| Abstract | Nearest neighbor search using random projection trees has recently been shown to achieve superior performance, in terms of better accuracy while retrieving less number of data points, compared to locality sensitive hashing based methods. However, to achieve acceptable nearest neighbor search accuracy for large scale applications, where number of data points and/or number of features can be very large, it requires users to maintain, store and search through large number of such independent random projection trees, which may be undesirable for many practical applications. To address this issue, in this paper we present different search strategies to improve nearest neighbor search performance of a single random projection tree. Our approach exploits properties of single and multiple random projections, which allows us to store meaningful auxiliary information at internal nodes of a random projection tree as well as to design priority functions to guide the search process that results in improved nearest neighbor search performance. Empirical results on multiple real world datasets show that our proposed method improves the search accuracy of a single tree compared to baseline methods. |
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| Published | 2018-07-01 |
| URL | https://icml.cc/Conferences/2018/Schedule?showEvent=2041 |
| http://proceedings.mlr.press/v80/keivani18a/keivani18a.pdf | |
| PWC | https://paperswithcode.com/paper/improved-nearest-neighbor-search-using |
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The UIR Uncertainty Corpus for Chinese: Annotating Chinese Microblog Corpus for Uncertainty Identification from Social Media
| Title | The UIR Uncertainty Corpus for Chinese: Annotating Chinese Microblog Corpus for Uncertainty Identification from Social Media |
| Authors | Binyang Li, Jun Xiang, Le Chen, Xu Han, Xiaoyan Yu, Ruifeng Xu, Tengjiao Wang, Kam-fai Wong |
| Abstract | |
| Tasks | Question Answering |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1078/ |
| https://www.aclweb.org/anthology/L18-1078 | |
| PWC | https://paperswithcode.com/paper/the-uir-uncertainty-corpus-for-chinese |
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Using Crowd Agreement for Wordnet Localization
| Title | Using Crowd Agreement for Wordnet Localization |
| Authors | Amarsanaa Ganbold, Altangerel Chagnaa, G{'a}bor Bella |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1074/ |
| https://www.aclweb.org/anthology/L18-1074 | |
| PWC | https://paperswithcode.com/paper/using-crowd-agreement-for-wordnet |
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Knowing the Author by the Company His Words Keep
| Title | Knowing the Author by the Company His Words Keep |
| Authors | Armin Hoenen, Niko Schenk |
| Abstract | |
| Tasks | Semantic Textual Similarity, Word Embeddings |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1083/ |
| https://www.aclweb.org/anthology/L18-1083 | |
| PWC | https://paperswithcode.com/paper/knowing-the-author-by-the-company-his-words |
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Training with Growing Sets: A Simple Alternative to Curriculum Learning and Self Paced Learning
| Title | Training with Growing Sets: A Simple Alternative to Curriculum Learning and Self Paced Learning |
| Authors | Melike Nur Mermer, Mehmet Fatih Amasyali |
| Abstract | Curriculum learning and Self paced learning are popular topics in the machine learning that suggest to put the training samples in order by considering their difficulty levels. Studies in these topics show that starting with a small training set and adding new samples according to difficulty levels improves the learning performance. In this paper we experimented that we can also obtain good results by adding the samples randomly without a meaningful order. We compared our method with classical training, Curriculum learning, Self paced learning and their reverse ordered versions. Results of the statistical tests show that the proposed method is better than classical method and similar with the others. These results point a new training regime that removes the process of difficulty level determination in Curriculum and Self paced learning and as successful as these methods. |
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| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=SJ1fQYlCZ |
| https://openreview.net/pdf?id=SJ1fQYlCZ | |
| PWC | https://paperswithcode.com/paper/training-with-growing-sets-a-simple |
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Network Iterative Learning for Dynamic Deep Neural Networks via Morphism
| Title | Network Iterative Learning for Dynamic Deep Neural Networks via Morphism |
| Authors | Tao Wei, Changhu Wang, Chang Wen Chen |
| Abstract | In this research, we present a novel learning scheme called network iterative learning for deep neural networks. Different from traditional optimization algorithms that usually optimize directly on a static objective function, we propose in this work to optimize a dynamic objective function in an iterative fashion capable of adapting its function form when being optimized. The optimization is implemented as a series of intermediate neural net functions that is able to dynamically grow into the targeted neural net objective function. This is done via network morphism so that the network knowledge is fully preserved with each network growth. Experimental results demonstrate that the proposed network iterative learning scheme is able to significantly alleviate the degradation problem. Its effectiveness is verified on diverse benchmark datasets. |
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| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=SkFvV0yC- |
| https://openreview.net/pdf?id=SkFvV0yC- | |
| PWC | https://paperswithcode.com/paper/network-iterative-learning-for-dynamic-deep |
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One event, many representations. Mapping action concepts through visual features.
| Title | One event, many representations. Mapping action concepts through visual features. |
| Authors | Aless Panunzi, ro, Lorenzo Gregori, Andrea Amelio Ravelli |
| Abstract | |
| Tasks | |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1095/ |
| https://www.aclweb.org/anthology/L18-1095 | |
| PWC | https://paperswithcode.com/paper/one-event-many-representations-mapping-action |
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Contextualized Usage-Based Material Selection
| Title | Contextualized Usage-Based Material Selection |
| Authors | Dirk De Hertog, Piet Desmet |
| Abstract | |
| Tasks | Language Acquisition, Semantic Textual Similarity |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1053/ |
| https://www.aclweb.org/anthology/L18-1053 | |
| PWC | https://paperswithcode.com/paper/contextualized-usage-based-material-selection |
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Learning Patch Reconstructability for Accelerating Multi-View Stereo
| Title | Learning Patch Reconstructability for Accelerating Multi-View Stereo |
| Authors | Alex Poms, Chenglei Wu, Shoou-I Yu, Yaser Sheikh |
| Abstract | We present an approach to accelerate multi-view stereo (MVS) by prioritizing computation on image patches that are likely to produce accurate 3D surface reconstructions. Our key insight is that the accuracy of the surface reconstruction from a given image patch can be predicted significantly faster than performing the actual stereo matching. The intuition is that non-specular, fronto-parallel, in-focus patches are more likely to produce accurate surface reconstructions than highly specular, slanted, blurry patches — and that these properties can be reliably predicted from the image itself. By prioritizing stereo matching on a subset of patches that are highly reconstructable and also cover the 3D surface, we are able to accelerate MVS with minimal reduction in accuracy and completeness. To predict the reconstructability score of an image patch from a single view, we train an image-to-reconstructability neural network: the I2RNet. This reconstructability score enables us to efficiently identify image patches that are likely to provide the most accurate surface estimates before performing stereo matching. We demonstrate that the I2RNet, when trained on the ScanNet dataset, generalizes to the DTU and Tanks and Temples MVS datasets. By using our I2RNet with an existing MVS implementation, we show that our method can achieve more than a 30x speed-up over the baseline with only an minimal loss in completeness. |
| Tasks | Stereo Matching, Stereo Matching Hand |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Poms_Learning_Patch_Reconstructability_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Poms_Learning_Patch_Reconstructability_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/learning-patch-reconstructability-for |
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SIC-GAN: A Self-Improving Collaborative GAN for Decoding Sketch RNNs
| Title | SIC-GAN: A Self-Improving Collaborative GAN for Decoding Sketch RNNs |
| Authors | Chi-Chun Chuang, Zheng-Xin Weng, Shan-Hung Wu |
| Abstract | Variational RNNs are proposed to output “creative” sequences. Ideally, a collection of sequences produced by a variational RNN should be of both high quality and high variety. However, existing decoders for variational RNNs suffer from a trade-off between quality and variety. In this paper, we seek to learn a variational RNN that decodes high-quality and high-variety sequences. We propose the Self-Improving Collaborative GAN (SIC-GAN), where there are two generators (variational RNNs) collaborating with each other to output a sequence and aiming to trick the discriminator into believing the sequence is of good quality. By deliberately weakening one generator, we can make another stronger in balancing quality and variety. We conduct experiments using the QuickDraw dataset and the results demonstrate the effectiveness of SIC-GAN empirically. |
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| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=r1nmx5l0W |
| https://openreview.net/pdf?id=r1nmx5l0W | |
| PWC | https://paperswithcode.com/paper/sic-gan-a-self-improving-collaborative-gan |
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Classifier-based Polarity Propagation in a WordNet
| Title | Classifier-based Polarity Propagation in a WordNet |
| Authors | Jan Koco{'n}, Arkadiusz Janz, Maciej Piasecki |
| Abstract | |
| Tasks | Emotion Recognition, Sentiment Analysis |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1665/ |
| https://www.aclweb.org/anthology/L18-1665 | |
| PWC | https://paperswithcode.com/paper/classifier-based-polarity-propagation-in-a |
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Lightweight Word-Level Confidence Estimation for Neural Interactive Translation Prediction
| Title | Lightweight Word-Level Confidence Estimation for Neural Interactive Translation Prediction |
| Authors | Rebecca Knowles, Philipp Koehn |
| Abstract | |
| Tasks | Automatic Post-Editing, Machine Translation |
| Published | 2018-03-01 |
| URL | https://www.aclweb.org/anthology/W18-2102/ |
| https://www.aclweb.org/anthology/W18-2102 | |
| PWC | https://paperswithcode.com/paper/lightweight-word-level-confidence-estimation |
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UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets
| Title | UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets |
| Authors | Pavel P{\v{r}}ib{'a}{\v{n}}, Tom{'a}{\v{s}} Hercig, Ladislav Lenc |
| Abstract | This paper describes our system created for the SemEval-2018 Task 1: Affect in Tweets (AIT-2018). We participated in both the regression and the ordinal classification subtasks for emotion intensity detection in English, Arabic, and Spanish. For the regression subtask we use the AffectiveTweets system with added features using various word embeddings, lexicons, and LDA. For the ordinal classification we additionally use our Brainy system with features using parse tree, POS tags, and morphological features. The most beneficial features apart from word and character n-grams include word embeddings, POS count and morphological features. |
| Tasks | Word Embeddings |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1018/ |
| https://www.aclweb.org/anthology/S18-1018 | |
| PWC | https://paperswithcode.com/paper/uwb-at-semeval-2018-task-1-emotion-intensity |
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Deep JSLC: A Multimodal Corpus Collection for Data-driven Generation of Japanese Sign Language Expressions
| Title | Deep JSLC: A Multimodal Corpus Collection for Data-driven Generation of Japanese Sign Language Expressions |
| Authors | Heike Brock, Kazuhiro Nakadai |
| Abstract | |
| Tasks | Data Augmentation, Motion Capture |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1670/ |
| https://www.aclweb.org/anthology/L18-1670 | |
| PWC | https://paperswithcode.com/paper/deep-jslc-a-multimodal-corpus-collection-for |
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MapNet: An Allocentric Spatial Memory for Mapping Environments
| Title | MapNet: An Allocentric Spatial Memory for Mapping Environments |
| Authors | João F. Henriques, Andrea Vedaldi |
| Abstract | Autonomous agents need to reason about the world beyond their instantaneous sensory input. Integrating information over time, however, requires switching from an egocentric representation of a scene to an allocentric one, expressed in the world reference frame. It must also be possible to update the representation dynamically, which requires localizing and registering the sensor with respect to it. In this paper, we develop a differentiable module that satisfies such requirements, while being robust, efficient, and suitable for integration in end-to-end deep networks. The module contains an allocentric spatial memory that can be accessed associatively by feeding to it the current sensory input, resulting in localization, and then updated using an LSTM or similar mechanism. We formulate efficient localization and registration of sensory information as a dual pair of convolution/deconvolution operators in memory space. The map itself is a 2.5D representation of an environment storing information that a deep neural network module learns to distill from RGBD input. The result is a map that contains multi-task information, different from classical approaches to mapping such as structure-from-motion. We present results using synthetic mazes, a dataset of hours of recorded gameplay of the classic game Doom, and the very recent Active Vision Dataset of real images captured from a robot. |
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| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Henriques_MapNet_An_Allocentric_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Henriques_MapNet_An_Allocentric_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/mapnet-an-allocentric-spatial-memory-for |
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