Paper Group ANR 927
Classical linear logic, cobordisms and categorical semantics of categorial grammars. Deep Learning Application in Security and Privacy – Theory and Practice: A Position Paper. Iterative Visual Reasoning Beyond Convolutions. Lévy Flights of the Collective Imagination. Regret Circuits: Composability of Regret Minimizers. Skeletracks: automatic separ …
Classical linear logic, cobordisms and categorical semantics of categorial grammars
| Title | Classical linear logic, cobordisms and categorical semantics of categorial grammars |
| Authors | Sergey Slavnov |
| Abstract | We propose a categorial grammar based on classical multiplicative linear logic. This can be seen as an extension of abstract categorial grammars (ACG) and is at least as expressive. However, constituents of {\it linear logic grammars (LLG)} are not abstract ${\lambda}$-terms, but simply tuples of words with labeled endpoints, we call them {\it multiwords}. At least, this gives a concrete and intuitive representation of ACG. A key observation is that the class of multiwords has a fundamental algebraic structure. Namely, multiwords can be organized in a category, very similar to the category of topological cobordisms. This category is symmetric monoidal closed and compact closed and thus is a model of linear $\lambda$-calculus and classical linear logic. We think that this category is interesting on its own right. In particular, it might provide categorical representation for other formalisms. On the other hand, many models of language semantics are based on commutative logic or, more generally, on symmetric monoidal closed categories. But the category of {\it word cobordisms} is a category of language elements, which is itself symmetric monoidal closed and independent of any grammar. Thus, it might prove useful in understanding language semantics as well. |
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| Published | 2018-10-04 |
| URL | http://arxiv.org/abs/1810.02047v6 |
| http://arxiv.org/pdf/1810.02047v6.pdf | |
| PWC | https://paperswithcode.com/paper/classical-linear-logic-cobordisms-and |
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Deep Learning Application in Security and Privacy – Theory and Practice: A Position Paper
| Title | Deep Learning Application in Security and Privacy – Theory and Practice: A Position Paper |
| Authors | Julia A. Meister, Raja Naeem Akram, Konstantinos Markantonakis |
| Abstract | Technology is shaping our lives in a multitude of ways. This is fuelled by a technology infrastructure, both legacy and state of the art, composed of a heterogeneous group of hardware, software, services and organisations. Such infrastructure faces a diverse range of challenges to its operations that include security, privacy, resilience, and quality of services. Among these, cybersecurity and privacy are taking the centre-stage, especially since the General Data Protection Regulation (GDPR) came into effect. Traditional security and privacy techniques are overstretched and adversarial actors have evolved to design exploitation techniques that circumvent protection. With the ever-increasing complexity of technology infrastructure, security and privacy-preservation specialists have started to look for adaptable and flexible protection methods that can evolve (potentially autonomously) as the adversarial actor changes its techniques. For this, Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) were put forward as saviours. In this paper, we look at the promises of AI, ML, and DL stated in academic and industrial literature and evaluate how realistic they are. We also put forward potential challenges a DL based security and privacy protection technique has to overcome. Finally, we conclude the paper with a discussion on what steps the DL and the security and privacy-preservation community have to take to ensure that DL is not just going to be hype, but an opportunity to build a secure, reliable, and trusted technology infrastructure on which we can rely on for so much in our lives. |
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| Published | 2018-12-01 |
| URL | http://arxiv.org/abs/1812.00190v1 |
| http://arxiv.org/pdf/1812.00190v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-learning-application-in-security-and |
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Iterative Visual Reasoning Beyond Convolutions
| Title | Iterative Visual Reasoning Beyond Convolutions |
| Authors | Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta |
| Abstract | We present a novel framework for iterative visual reasoning. Our framework goes beyond current recognition systems that lack the capability to reason beyond stack of convolutions. The framework consists of two core modules: a local module that uses spatial memory to store previous beliefs with parallel updates; and a global graph-reasoning module. Our graph module has three components: a) a knowledge graph where we represent classes as nodes and build edges to encode different types of semantic relationships between them; b) a region graph of the current image where regions in the image are nodes and spatial relationships between these regions are edges; c) an assignment graph that assigns regions to classes. Both the local module and the global module roll-out iteratively and cross-feed predictions to each other to refine estimates. The final predictions are made by combining the best of both modules with an attention mechanism. We show strong performance over plain ConvNets, \eg achieving an $8.4%$ absolute improvement on ADE measured by per-class average precision. Analysis also shows that the framework is resilient to missing regions for reasoning. |
| Tasks | Visual Reasoning |
| Published | 2018-03-29 |
| URL | http://arxiv.org/abs/1803.11189v1 |
| http://arxiv.org/pdf/1803.11189v1.pdf | |
| PWC | https://paperswithcode.com/paper/iterative-visual-reasoning-beyond |
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Lévy Flights of the Collective Imagination
| Title | Lévy Flights of the Collective Imagination |
| Authors | William H. W. Thompson, Zachary Wojtowicz, Simon DeDeo |
| Abstract | We present a structured random-walk model that captures key aspects of how people communicate in groups. Our model takes the form of a correlated L'{e}vy flight that quantifies the balance between focused discussion of an idea and long-distance leaps in semantic space. We apply our model to three cases of increasing structural complexity: philosophical texts by Aristotle, Hume, and Kant; four days of parliamentary debate during the French Revolution; and branching comment trees on the discussion website Reddit. In the philosophical and parliamentary cases, the model parameters that describe this balance converge under coarse-graining to limit regions that demonstrate the emergence of large-scale structure, a result which is robust to translation between languages. Meanwhile, we find that the political forum we consider on Reddit exhibits a debate-like pattern, while communities dedicated to the discussion of science and news show much less temporal order, and may make use of the emergent, tree-like topology of comment replies to structure their epistemic explorations. Our model allows us to quantify the ways in which social technologies such as parliamentary procedures and online commenting systems shape the joint exploration of ideas. |
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| Published | 2018-12-10 |
| URL | http://arxiv.org/abs/1812.04013v1 |
| http://arxiv.org/pdf/1812.04013v1.pdf | |
| PWC | https://paperswithcode.com/paper/levy-flights-of-the-collective-imagination |
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Regret Circuits: Composability of Regret Minimizers
| Title | Regret Circuits: Composability of Regret Minimizers |
| Authors | Gabriele Farina, Christian Kroer, Tuomas Sandholm |
| Abstract | Regret minimization is a powerful tool for solving large-scale problems; it was recently used in breakthrough results for large-scale extensive-form game solving. This was achieved by composing simplex regret minimizers into an overall regret-minimization framework for extensive-form game strategy spaces. In this paper we study the general composability of regret minimizers. We derive a calculus for constructing regret minimizers for composite convex sets that are obtained from convexity-preserving operations on simpler convex sets. We show that local regret minimizers for the simpler sets can be combined with additional regret minimizers into an aggregate regret minimizer for the composite set. As one application, we show that the CFR framework can be constructed easily from our framework. We also show ways to include curtailing (constraining) operations into our framework. For one, they enables the construction of CFR generalization for extensive-form games with general convex strategy constraints that can cut across decision points. |
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| Published | 2018-11-06 |
| URL | http://arxiv.org/abs/1811.02540v2 |
| http://arxiv.org/pdf/1811.02540v2.pdf | |
| PWC | https://paperswithcode.com/paper/regret-circuits-composability-of-regret |
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Skeletracks: automatic separation of overlapping fission tracks in apatite and muscovite using image processing
| Title | Skeletracks: automatic separation of overlapping fission tracks in apatite and muscovite using image processing |
| Authors | Alexandre Fioravante de Siqueira, Wagner Massayuki Nakasuga, Sandro Guedes |
| Abstract | One of the major difficulties of automatic track counting using photomicrographs is separating overlapped tracks. We address this issue combining image processing algorithms such as skeletonization, and we test our algorithm with several binarization techniques. The counting algorithm was successfully applied to determine the efficiency factor GQR, necessary for standardless fission-track dating, involving counting induced tracks in apatite and muscovite with superficial densities of about $6 \times 10^5$ tracks/$cm^2$. |
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| Published | 2018-06-13 |
| URL | http://arxiv.org/abs/1806.05199v2 |
| http://arxiv.org/pdf/1806.05199v2.pdf | |
| PWC | https://paperswithcode.com/paper/skeletracks-automatic-separation-of |
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Futuristic Classification with Dynamic Reference Frame Strategy
| Title | Futuristic Classification with Dynamic Reference Frame Strategy |
| Authors | Kumarjit Pathak, Jitin Kapila, Aasheesh Barvey |
| Abstract | Classification is one of the widely used analytical techniques in data science domain across different business to associate a pattern which contribute to the occurrence of certain event which is predicted with some likelihood. This Paper address a lacuna of creating some time window before the prediction actually happen to enable organizations some space to act on the prediction. There are some really good state of the art machine learning techniques to optimally identify the possible churners in either customer base or employee base, similarly for fault prediction too if the prediction does not come with some buffer time to act on the fault it is very difficult to provide a seamless experience to the user. New concept of reference frame creation is introduced to solve this problem in this paper |
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| Published | 2018-05-25 |
| URL | http://arxiv.org/abs/1805.10168v1 |
| http://arxiv.org/pdf/1805.10168v1.pdf | |
| PWC | https://paperswithcode.com/paper/futuristic-classification-with-dynamic |
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Exploiting Capacity of Sewer System Using Unsupervised Learning Algorithms Combined with Dimensionality Reduction
| Title | Exploiting Capacity of Sewer System Using Unsupervised Learning Algorithms Combined with Dimensionality Reduction |
| Authors | Duo Zhang, Geir Lindholm, Nicolas Martinez, Harsha Ratnaweera |
| Abstract | Exploiting capacity of sewer system using decentralized control is a cost effective mean of minimizing the overflow. Given the size of the real sewer system, exploiting all the installed control structures in the sewer pipes can be challenging. This paper presents a divide and conquer solution to implement decentralized control measures based on unsupervised learning algorithms. A sewer system is first divided into a number of subcatchments. A series of natural and built factors that have the impact on sewer system performance is then collected. Clustering algorithms are then applied to grouping subcatchments with similar hydraulic hydrologic characteristics. Following which, principal component analysis is performed to interpret the main features of sub-catchment groups and identify priority control locations. Overflows under different control scenarios are compared based on the hydraulic model. Simulation results indicate that priority control applied to the most suitable cluster could bring the most profitable result. |
| Tasks | Dimensionality Reduction |
| Published | 2018-11-09 |
| URL | http://arxiv.org/abs/1811.03883v1 |
| http://arxiv.org/pdf/1811.03883v1.pdf | |
| PWC | https://paperswithcode.com/paper/exploiting-capacity-of-sewer-system-using |
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Multi-directional Geodesic Neural Networks via Equivariant Convolution
| Title | Multi-directional Geodesic Neural Networks via Equivariant Convolution |
| Authors | Adrien Poulenard, Maks Ovsjanikov |
| Abstract | We propose a novel approach for performing convolution of signals on curved surfaces and show its utility in a variety of geometric deep learning applications. Key to our construction is the notion of directional functions defined on the surface, which extend the classic real-valued signals and which can be naturally convolved with with real-valued template functions. As a result, rather than trying to fix a canonical orientation or only keeping the maximal response across all alignments of a 2D template at every point of the surface, as done in previous works, we show how information across all rotations can be kept across different layers of the neural network. Our construction, which we call multi-directional geodesic convolution, or directional convolution for short, allows, in particular, to propagate and relate directional information across layers and thus different regions on the shape. We first define directional convolution in the continuous setting, prove its key properties and then show how it can be implemented in practice, for shapes represented as triangle meshes. We evaluate directional convolution in a wide variety of learning scenarios ranging from classification of signals on surfaces, to shape segmentation and shape matching, where we show a significant improvement over several baselines. |
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| Published | 2018-10-01 |
| URL | http://arxiv.org/abs/1810.02303v1 |
| http://arxiv.org/pdf/1810.02303v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-directional-geodesic-neural-networks |
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Continuous Learning in a Hierarchical Multiscale Neural Network
| Title | Continuous Learning in a Hierarchical Multiscale Neural Network |
| Authors | Thomas Wolf, Julien Chaumond, Clement Delangue |
| Abstract | We reformulate the problem of encoding a multi-scale representation of a sequence in a language model by casting it in a continuous learning framework. We propose a hierarchical multi-scale language model in which short time-scale dependencies are encoded in the hidden state of a lower-level recurrent neural network while longer time-scale dependencies are encoded in the dynamic of the lower-level network by having a meta-learner update the weights of the lower-level neural network in an online meta-learning fashion. We use elastic weights consolidation as a higher-level to prevent catastrophic forgetting in our continuous learning framework. |
| Tasks | Language Modelling, Meta-Learning |
| Published | 2018-05-15 |
| URL | http://arxiv.org/abs/1805.05758v1 |
| http://arxiv.org/pdf/1805.05758v1.pdf | |
| PWC | https://paperswithcode.com/paper/continuous-learning-in-a-hierarchical |
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Recent Advances in Deep Learning: An Overview
| Title | Recent Advances in Deep Learning: An Overview |
| Authors | Matiur Rahman Minar, Jibon Naher |
| Abstract | Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. It is also one of the most popular scientific research trends now-a-days. Deep learning methods have brought revolutionary advances in computer vision and machine learning. Every now and then, new and new deep learning techniques are being born, outperforming state-of-the-art machine learning and even existing deep learning techniques. In recent years, the world has seen many major breakthroughs in this field. Since deep learning is evolving at a huge speed, its kind of hard to keep track of the regular advances especially for new researchers. In this paper, we are going to briefly discuss about recent advances in Deep Learning for past few years. |
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| Published | 2018-07-21 |
| URL | http://arxiv.org/abs/1807.08169v1 |
| http://arxiv.org/pdf/1807.08169v1.pdf | |
| PWC | https://paperswithcode.com/paper/recent-advances-in-deep-learning-an-overview |
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A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images
| Title | A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images |
| Authors | Siddharth Mahendran, Haider Ali, Rene Vidal |
| Abstract | 3D pose estimation from a single 2D image is an important and challenging task in computer vision with applications in autonomous driving, robot manipulation and augmented reality. Since 3D pose is a continuous quantity, a natural formulation for this task is to solve a pose regression problem. However, since pose regression methods return a single estimate of the pose, they have difficulties handling multimodal pose distributions (e.g. in the case of symmetric objects). An alternative formulation, which can capture multimodal pose distributions, is to discretize the pose space into bins and solve a pose classification problem. However, pose classification methods can give large pose estimation errors depending on the coarseness of the discretization. In this paper, we propose a mixed classification-regression framework that uses a classification network to produce a discrete multimodal pose estimate and a regression network to produce a continuous refinement of the discrete estimate. The proposed framework can accommodate different architectures and loss functions, leading to multiple classification-regression models, some of which achieve state-of-the-art performance on the challenging Pascal3D+ dataset. |
| Tasks | 3D Pose Estimation, Autonomous Driving, Pose Estimation |
| Published | 2018-05-08 |
| URL | http://arxiv.org/abs/1805.03225v1 |
| http://arxiv.org/pdf/1805.03225v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-mixed-classification-regression-framework |
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Bianet: A Parallel News Corpus in Turkish, Kurdish and English
| Title | Bianet: A Parallel News Corpus in Turkish, Kurdish and English |
| Authors | Duygu Ataman |
| Abstract | We present a new open-source parallel corpus consisting of news articles collected from the Bianet magazine, an online newspaper that publishes Turkish news, often along with their translations in English and Kurdish. In this paper, we describe the collection process of the corpus and its statistical properties. We validate the benefit of using the Bianet corpus by evaluating bilingual and multilingual neural machine translation models in English-Turkish and English-Kurdish directions. |
| Tasks | Machine Translation |
| Published | 2018-05-14 |
| URL | http://arxiv.org/abs/1805.05095v1 |
| http://arxiv.org/pdf/1805.05095v1.pdf | |
| PWC | https://paperswithcode.com/paper/bianet-a-parallel-news-corpus-in-turkish |
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Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks
| Title | Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks |
| Authors | Mohamed Yousef, Khaled F. Hussain, Usama S. Mohammed |
| Abstract | Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy. Our proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner. We show the generality and superiority of our proposed text recognition architecture by achieving state of the art results on seven public benchmark datasets, covering a wide spectrum of text recognition tasks, namely: Handwriting Recognition, CAPTCHA recognition, OCR, License Plate Recognition, and Scene Text Recognition. Our proposed architecture has won the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset. |
| Tasks | License Plate Recognition, Optical Character Recognition, Scene Text Recognition |
| Published | 2018-12-31 |
| URL | http://arxiv.org/abs/1812.11894v1 |
| http://arxiv.org/pdf/1812.11894v1.pdf | |
| PWC | https://paperswithcode.com/paper/accurate-data-efficient-unconstrained-text |
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Cortical Microcircuits from a Generative Vision Model
| Title | Cortical Microcircuits from a Generative Vision Model |
| Authors | Dileep George, Alexander Lavin, J. Swaroop Guntupalli, David Mely, Nick Hay, Miguel Lazaro-Gredilla |
| Abstract | Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence. The theoretical setting of Bayesian inference has been suggested as a framework for understanding cortical computation. Based on a recently published generative model for visual inference (George et al., 2017), we derive a family of anatomically instantiated and functional cortical circuit models. In contrast to simplistic models of Bayesian inference, the underlying generative model’s representational choices are validated with real-world tasks that required efficient inference and strong generalization. The cortical circuit model is derived by systematically comparing the computational requirements of this model with known anatomical constraints. The derived model suggests precise functional roles for the feedforward, feedback and lateral connections observed in different laminae and columns, and assigns a computational role for the path through the thalamus. |
| Tasks | Bayesian Inference |
| Published | 2018-08-03 |
| URL | http://arxiv.org/abs/1808.01058v1 |
| http://arxiv.org/pdf/1808.01058v1.pdf | |
| PWC | https://paperswithcode.com/paper/cortical-microcircuits-from-a-generative |
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