Paper Group NANR 28
A Classification-Based Perspective on GAN Distributions. Analyzing the Quality of Counseling Conversations: the Tell-Tale Signs of High-quality Counseling. PAC-learning in the presence of adversaries. Building Evaluation Datasets for Cultural Microblog Retrieval. Creating Large-Scale Argumentation Structures for Dialogue Systems. Deformable Pose Tr …
A Classification-Based Perspective on GAN Distributions
| Title | A Classification-Based Perspective on GAN Distributions |
| Authors | Shibani Santurkar, Ludwig Schmidt, Aleksander Madry |
| Abstract | A fundamental, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether GANs are actually able to capture the key characteristics of the datasets they are trained on. The current approaches to examining this issue require significant human supervision, such as visual inspection of sampled images, and often offer only fairly limited scalability. In this paper, we propose new techniques that employ classification-based perspective to evaluate synthetic GAN distributions and their capability to accurately reflect the essential properties of the training data. These techniques require only minimal human supervision and can easily be scaled and adapted to evaluate a variety of state-of-the-art GANs on large, popular datasets. They also indicate that GANs have significant problems in reproducing the more distributional properties of the training dataset. In particular, the diversity of such synthetic data is orders of magnitude smaller than that of the original data. |
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| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=S1FQEfZA- |
| https://openreview.net/pdf?id=S1FQEfZA- | |
| PWC | https://paperswithcode.com/paper/a-classification-based-perspective-on-gan |
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Analyzing the Quality of Counseling Conversations: the Tell-Tale Signs of High-quality Counseling
| Title | Analyzing the Quality of Counseling Conversations: the Tell-Tale Signs of High-quality Counseling |
| Authors | Ver{'o}nica P{'e}rez-Rosas, Xuetong Sun, Christy Li, Yuchen Wang, Kenneth Resnicow, Rada Mihalcea |
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| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1591/ |
| https://www.aclweb.org/anthology/L18-1591 | |
| PWC | https://paperswithcode.com/paper/analyzing-the-quality-of-counseling |
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PAC-learning in the presence of adversaries
| Title | PAC-learning in the presence of adversaries |
| Authors | Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal |
| Abstract | The existence of evasion attacks during the test phase of machine learning algorithms represents a significant challenge to both their deployment and understanding. These attacks can be carried out by adding imperceptible perturbations to inputs to generate adversarial examples and finding effective defenses and detectors has proven to be difficult. In this paper, we step away from the attack-defense arms race and seek to understand the limits of what can be learned in the presence of an evasion adversary. In particular, we extend the Probably Approximately Correct (PAC)-learning framework to account for the presence of an adversary. We first define corrupted hypothesis classes which arise from standard binary hypothesis classes in the presence of an evasion adversary and derive the Vapnik-Chervonenkis (VC)-dimension for these, denoted as the adversarial VC-dimension. We then show that sample complexity upper bounds from the Fundamental Theorem of Statistical learning can be extended to the case of evasion adversaries, where the sample complexity is controlled by the adversarial VC-dimension. We then explicitly derive the adversarial VC-dimension for halfspace classifiers in the presence of a sample-wise norm-constrained adversary of the type commonly studied for evasion attacks and show that it is the same as the standard VC-dimension, closing an open question. Finally, we prove that the adversarial VC-dimension can be either larger or smaller than the standard VC-dimension depending on the hypothesis class and adversary, making it an interesting object of study in its own right. |
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| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7307-pac-learning-in-the-presence-of-adversaries |
| http://papers.nips.cc/paper/7307-pac-learning-in-the-presence-of-adversaries.pdf | |
| PWC | https://paperswithcode.com/paper/pac-learning-in-the-presence-of-adversaries |
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Building Evaluation Datasets for Cultural Microblog Retrieval
| Title | Building Evaluation Datasets for Cultural Microblog Retrieval |
| Authors | Lorraine Goeuriot, Josiane Mothe, Philippe Mulhem, Eric SanJuan |
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| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1594/ |
| https://www.aclweb.org/anthology/L18-1594 | |
| PWC | https://paperswithcode.com/paper/building-evaluation-datasets-for-cultural |
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Creating Large-Scale Argumentation Structures for Dialogue Systems
| Title | Creating Large-Scale Argumentation Structures for Dialogue Systems |
| Authors | Kazuki Sakai, Akari Inago, Ryuichiro Higashinaka, Yuichiro Yoshikawa, Hiroshi Ishiguro, Junji Tomita |
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| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1627/ |
| https://www.aclweb.org/anthology/L18-1627 | |
| PWC | https://paperswithcode.com/paper/creating-large-scale-argumentation-structures |
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Deformable Pose Traversal Convolution for 3D Action and Gesture Recognition
| Title | Deformable Pose Traversal Convolution for 3D Action and Gesture Recognition |
| Authors | Junwu Weng, Mengyuan Liu, Xudong Jiang, Junsong Yuan |
| Abstract | The representation of 3D pose plays a critical role for 3D body action and hand gesture recognition. Rather than directly representing the 3D pose using its joint locations, in this paper, we propose Deformable Pose Traversal Convolution which applies one-dimensional convolution to traverse the 3D pose to represent it. Instead of fixing the reception field when performing traversal convolution, it optimizes the convolutional kernel for each joint, by considering contextual joints with various weights. This deformable convolution can better utilize contextual joints for action and gesture recognition and is more robust to noisy joints. Moreover, by feeding the learned pose feature to a LSTM, we can perform end-to-end training which jointly optimizes 3D pose representation and temporal sequence recognition. Experiments on three benchmark datasets validate the competitive performance of our proposed method, as well as its efficiency and robustness to handle noisy pose. |
| Tasks | Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Junwu_Weng_Deformable_Pose_Traversal_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Junwu_Weng_Deformable_Pose_Traversal_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/deformable-pose-traversal-convolution-for-3d |
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Adaptive Quantization of Neural Networks
| Title | Adaptive Quantization of Neural Networks |
| Authors | Soroosh Khoram, Jing Li |
| Abstract | Despite the state-of-the-art accuracy of Deep Neural Networks (DNN) in various classification problems, their deployment onto resource constrained edge computing devices remains challenging due to their large size and complexity. Several recent studies have reported remarkable results in reducing this complexity through quantization of DNN models. However, these studies usually do not consider the changes in the loss function when performing quantization, nor do they take the different importances of DNN model parameters to the accuracy into account. We address these issues in this paper by proposing a new method, called adaptive quantization, which simplifies a trained DNN model by finding a unique, optimal precision for each network parameter such that the increase in loss is minimized. The optimization problem at the core of this method iteratively uses the loss function gradient to determine an error margin for each parameter and assigns it a precision accordingly. Since this problem uses linear functions, it is computationally cheap and, as we will show, has a closed-form approximate solution. Experiments on MNIST, CIFAR, and SVHN datasets showed that the proposed method can achieve near or better than state-of-the-art reduction in model size with similar error rates. Furthermore, it can achieve compressions close to floating-point model compression methods without loss of accuracy. |
| Tasks | Model Compression, Quantization |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=SyOK1Sg0W |
| https://openreview.net/pdf?id=SyOK1Sg0W | |
| PWC | https://paperswithcode.com/paper/adaptive-quantization-of-neural-networks |
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Portable Spelling Corrector for a Less-Resourced Language: Amharic
| Title | Portable Spelling Corrector for a Less-Resourced Language: Amharic |
| Authors | Andargachew Mekonnen Gezmu, Andreas N{"u}rnberger, Binyam Ephrem Seyoum |
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| Tasks | Language Modelling, Spelling Correction, Transliteration |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1651/ |
| https://www.aclweb.org/anthology/L18-1651 | |
| PWC | https://paperswithcode.com/paper/portable-spelling-corrector-for-a-less |
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The loss surface and expressivity of deep convolutional neural networks
| Title | The loss surface and expressivity of deep convolutional neural networks |
| Authors | Quynh Nguyen, Matthias Hein |
| Abstract | We analyze the expressiveness and loss surface of practical deep convolutional neural networks (CNNs) with shared weights and max pooling layers. We show that such CNNs produce linearly independent features at a “wide” layer which has more neurons than the number of training samples. This condition holds e.g. for the VGG network. Furthermore, we provide for such wide CNNs necessary and sufficient conditions for global minima with zero training error. For the case where the wide layer is followed by a fully connected layer we show that almost every critical point of the empirical loss is a global minimum with zero training error. Our analysis suggests that both depth and width are very important in deep learning. While depth brings more representational power and allows the network to learn high level features, width smoothes the optimization landscape of the loss function in the sense that a sufficiently wide network has a well-behaved loss surface with almost no bad local minima. |
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| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=BJjquybCW |
| https://openreview.net/pdf?id=BJjquybCW | |
| PWC | https://paperswithcode.com/paper/the-loss-surface-and-expressivity-of-deep |
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Multi-View Harmonized Bilinear Network for 3D Object Recognition
| Title | Multi-View Harmonized Bilinear Network for 3D Object Recognition |
| Authors | Tan Yu, Jingjing Meng, Junsong Yuan |
| Abstract | View-based methods have achieved considerable success in $3$D object recognition tasks. Different from existing view-based methods pooling the view-wise features, we tackle this problem from the perspective of patches-to-patches similarity measurement. By exploiting the relationship between polynomial kernel and bilinear pooling, we obtain an effective $3$D object representation by aggregating local convolutional features through bilinear pooling. Meanwhile, we harmonize different components inherited in the pooled bilinear feature to obtain a more discriminative representation for a $3$D object. To achieve an end-to-end trainable framework, we incorporate the harmonized bilinear pooling operation as a layer of a network, constituting the proposed Multi-view Harmonized Bilinear Network (MHBN). Systematic experiments conducted on two public benchmark datasets demonstrate the efficacy of the proposed methods in $3$D object recognition. |
| Tasks | 3D Object Recognition, Object Recognition |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Yu_Multi-View_Harmonized_Bilinear_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_Multi-View_Harmonized_Bilinear_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/multi-view-harmonized-bilinear-network-for-3d |
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The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic
| Title | The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic |
| Authors | Ahmed Abdelali, Irina Temnikova, Samy Hedaya, Stephan Vogel |
| Abstract | |
| Tasks | Machine Translation, Speech Recognition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1336/ |
| https://www.aclweb.org/anthology/L18-1336 | |
| PWC | https://paperswithcode.com/paper/the-waw-corpus-the-first-corpus-of |
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AET: Web-based Adjective Exploration Tool for German
| Title | AET: Web-based Adjective Exploration Tool for German |
| Authors | Tatiana Bladier, Esther Seyffarth, Oliver Hellwig, Wiebke Petersen |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1343/ |
| https://www.aclweb.org/anthology/L18-1343 | |
| PWC | https://paperswithcode.com/paper/aet-web-based-adjective-exploration-tool-for |
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Building Universal Dependency Treebanks in Korean
| Title | Building Universal Dependency Treebanks in Korean |
| Authors | Jayeol Chun, Na-Rae Han, Jena D. Hwang, Jinho D. Choi |
| Abstract | |
| Tasks | Dependency Parsing |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1347/ |
| https://www.aclweb.org/anthology/L18-1347 | |
| PWC | https://paperswithcode.com/paper/building-universal-dependency-treebanks-in |
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Using context to identify the language of face-saving
| Title | Using context to identify the language of face-saving |
| Authors | Nona Naderi, Graeme Hirst |
| Abstract | We created a corpus of utterances that attempt to save face from parliamentary debates and use it to automatically analyze the language of reputation defence. Our proposed model that incorporates information regarding threats to reputation can predict reputation defence language with high confidence. Further experiments and evaluations on different datasets show that the model is able to generalize to new utterances and can predict the language of reputation defence in a new dataset. |
| Tasks | Argument Mining |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-5214/ |
| https://www.aclweb.org/anthology/W18-5214 | |
| PWC | https://paperswithcode.com/paper/using-context-to-identify-the-language-of |
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Spontaneous Symmetry Breaking in Deep Neural Networks
| Title | Spontaneous Symmetry Breaking in Deep Neural Networks |
| Authors | Ricky Fok, Aijun An, Xiaogang Wang |
| Abstract | We propose a framework to understand the unprecedented performance and robustness of deep neural networks using field theory. Correlations between the weights within the same layer can be described by symmetries in that layer, and networks generalize better if such symmetries are broken to reduce the redundancies of the weights. Using a two parameter field theory, we find that the network can break such symmetries itself towards the end of training in a process commonly known in physics as spontaneous symmetry breaking. This corresponds to a network generalizing itself without any user input layers to break the symmetry, but by communication with adjacent layers. In the layer decoupling limit applicable to residual networks (He et al., 2015), we show that the remnant symmetries that survive the non-linear layers are spontaneously broken based on empirical results. The Lagrangian for the non-linear and weight layers together has striking similarities with the one in quantum field theory of a scalar. Using results from quantum field theory we show that our framework is able to explain many experimentally observed phenomena, such as training on random labels with zero error (Zhang et al., 2017), the information bottleneck and the phase transition out of it (Shwartz-Ziv & Tishby, 2017), shattered gradients (Balduzzi et al., 2017), and many more. |
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| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=SkiCjzNTZ |
| https://openreview.net/pdf?id=SkiCjzNTZ | |
| PWC | https://paperswithcode.com/paper/spontaneous-symmetry-breaking-in-deep-neural |
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