January 29, 2020

3182 words 15 mins read

Paper Group ANR 748

Learning Multi-Stage Sparsification for Maximum Clique Enumeration. A reaction network scheme which implements inference and learning for Hidden Markov Models. Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data. Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis. Blended Matc …

Learning Multi-Stage Sparsification for Maximum Clique Enumeration


Title	Learning Multi-Stage Sparsification for Maximum Clique Enumeration
Authors	Marco Grassia, Juho Lauri, Sourav Dutta, Deepak Ajwani
Abstract	We propose a multi-stage learning approach for pruning the search space of maximum clique enumeration, a fundamental computationally difficult problem arising in various network analysis tasks. In each stage, our approach learns the characteristics of vertices in terms of various neighborhood features and leverage them to prune the set of vertices that are likely not contained in any maximum clique. Furthermore, we demonstrate that our approach is domain independent – the same small set of features works well on graph instances from different domain. Compared to the state-of-the-art heuristics and preprocessing strategies, the advantages of our approach are that (i) it does not require any estimate on the maximum clique size at runtime and (ii) we demonstrate it to be effective also for dense graphs. In particular, for dense graphs, we typically prune around 30 % of the vertices resulting in speedups of up to 53 times for state-of-the-art solvers while generally preserving the size of the maximum clique (though some maximum cliques may be lost). For large real-world sparse graphs, we routinely prune over 99 % of the vertices resulting in several tenfold speedups at best, typically with no impact on solution quality.
Tasks
Published	2019-09-12
URL	https://arxiv.org/abs/1910.00517v1
PDF	https://arxiv.org/pdf/1910.00517v1.pdf
PWC	https://paperswithcode.com/paper/learning-multi-stage-sparsification-for
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A reaction network scheme which implements inference and learning for Hidden Markov Models


Title	A reaction network scheme which implements inference and learning for Hidden Markov Models
Authors	Abhinav Singh, Carsten Wiuf, Abhishek Behera, Manoj Gopalkrishnan
Abstract	With a view towards molecular communication systems and molecular multi-agent systems, we propose the Chemical Baum-Welch Algorithm, a novel reaction network scheme that learns parameters for Hidden Markov Models (HMMs). Each reaction in our scheme changes only one molecule of one species to one molecule of another. The reverse change is also accessible but via a different set of enzymes, in a design reminiscent of futile cycles in biochemical pathways. We show that every fixed point of the Baum-Welch algorithm for HMMs is a fixed point of our reaction network scheme, and every positive fixed point of our scheme is a fixed point of the Baum-Welch algorithm. We prove that the “Expectation” step and the “Maximization” step of our reaction network separately converge exponentially fast. We simulate mass-action kinetics for our network on an example sequence, and show that it learns the same parameters for the HMM as the Baum-Welch algorithm.
Tasks
Published	2019-06-22
URL	https://arxiv.org/abs/1906.09410v2
PDF	https://arxiv.org/pdf/1906.09410v2.pdf
PWC	https://paperswithcode.com/paper/a-reaction-network-scheme-which-implements
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Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data


Title	Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data
Authors	Fahad AlGhamdi, Mona Diab
Abstract	Linguistic Code Switching (CS) is a phenomenon that occurs when multilingual speakers alternate between two or more languages/dialects within a single conversation. Processing CS data is especially challenging in intra-sentential data given state-of-the-art monolingual NLP technologies since such technologies are geared toward the processing of one language at a time. In this paper, we address the problem of Part-of-Speech tagging (POS) in the context of linguistic code switching (CS). We explore leveraging multiple neural network architectures to measure the impact of different pre-trained embeddings methods on POS tagging CS data. We investigate the landscape in four CS language pairs, Spanish-English, Hindi-English, Modern Standard Arabic- Egyptian Arabic dialect (MSA-EGY), and Modern Standard Arabic- Levantine Arabic dialect (MSA-LEV). Our results show that multilingual embedding (e.g., MSA-EGY and MSA-LEV) helps closely related languages (EGY/LEV) but adds noise to the languages that are distant (SPA/HIN). Finally, we show that our proposed models outperform state-of-the-art CS taggers for MSA-EGY language pair.
Tasks	Part-Of-Speech Tagging, Word Embeddings
Published	2019-05-31
URL	https://arxiv.org/abs/1905.13359v1
PDF	https://arxiv.org/pdf/1905.13359v1.pdf
PWC	https://paperswithcode.com/paper/leveraging-pretrained-word-embeddings-for
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Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis


Title	Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis
Authors	Luyang Luo, Hao Chen, Xi Wang, Qi Dou, Huangjin Lin, Juan Zhou, Gongjie Li, Pheng-Ann Heng
Abstract	Accurate and automatic analysis of breast MRI plays an important role in early diagnosis and successful treatment planning for breast cancer. Due to the heterogeneity nature, accurate diagnosis of tumors remains a challenging task. In this paper, we propose to identify breast tumor in MRI by Cosine Margin Sigmoid Loss (CMSL) with deep learning (DL) and localize possible cancer lesion by COrrelation Attention Map (COAM) based on the learned features. The CMSL embeds tumor features onto a hypersphere and imposes a decision margin through cosine constraints. In this way, the DL model could learn more separable inter-class features and more compact intra-class features in the angular space. Furthermore, we utilize the correlations among feature vectors to generate attention maps that could accurately localize cancer candidates with only image-level label. We build the largest breast cancer dataset involving 10,290 DCE-MRI scan volumes for developing and evaluating the proposed methods. The model driven by CMSL achieved classification accuracy of 0.855 and AUC of 0.902 on the testing set, with sensitivity and specificity of 0.857 and 0.852, respectively, outperforming other competitive methods overall. In addition, the proposed COAM accomplished more accurate localization of the cancer center compared with other state-of-the-art weakly supervised localization method.
Tasks
Published	2019-06-07
URL	https://arxiv.org/abs/1906.02999v1
PDF	https://arxiv.org/pdf/1906.02999v1.pdf
PWC	https://paperswithcode.com/paper/deep-angular-embedding-and-feature
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Blended Matching Pursuit


Title	Blended Matching Pursuit
Authors	Cyrille W. Combettes, Sebastian Pokutta
Abstract	Matching pursuit algorithms are an important class of algorithms in signal processing and machine learning. We present a blended matching pursuit algorithm, combining coordinate descent-like steps with stronger gradient descent steps, for minimizing a smooth convex function over a linear space spanned by a set of atoms. We derive sublinear to linear convergence rates according to the smoothness and sharpness orders of the function and demonstrate computational superiority of our approach. In particular, we derive linear rates for a wide class of non-strongly convex functions, and we demonstrate in experiments that our algorithm enjoys very fast rates of convergence and wall-clock speed while maintaining a sparsity of iterates very comparable to that of the (much slower) orthogonal matching pursuit.
Tasks
Published	2019-04-28
URL	https://arxiv.org/abs/1904.12335v3
PDF	https://arxiv.org/pdf/1904.12335v3.pdf
PWC	https://paperswithcode.com/paper/blended-matching-pursuit
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PrivFT: Private and Fast Text Classification with Homomorphic Encryption


Title	PrivFT: Private and Fast Text Classification with Homomorphic Encryption
Authors	Ahmad Al Badawi, Luong Hoang, Chan Fook Mun, Kim Laine, Khin Mi Mi Aung
Abstract	The need for privacy-preserving analytics is higher than ever due to the severity of privacy risks and to comply with new privacy regulations leading to an amplified interest in privacy-preserving techniques that try to balance between privacy and utility. In this work, we present an efficient method for Text Classification while preserving the privacy of the content using Fully Homomorphic Encryption (FHE). Our system (named \textbf{Priv}ate \textbf{F}ast \textbf{T}ext (PrivFT)) performs two tasks: 1) making inference of encrypted user inputs using a plaintext model and 2) training an effective model using an encrypted dataset. For inference, we train a supervised model and outline a system for homomorphic inference on encrypted user inputs with zero loss to prediction accuracy. In the second part, we show how to train a model using fully encrypted data to generate an encrypted model. We provide a GPU implementation of the Cheon-Kim-Kim-Song (CKKS) FHE scheme and compare it with existing CPU implementations to achieve 1 to 2 orders of magnitude speedup at various parameter settings. We implement PrivFT in GPUs to achieve a run time per inference of less than 0.66 seconds. Training on a relatively large encrypted dataset is more computationally intensive requiring 5.04 days.
Tasks	Text Classification
Published	2019-08-19
URL	https://arxiv.org/abs/1908.06972v2
PDF	https://arxiv.org/pdf/1908.06972v2.pdf
PWC	https://paperswithcode.com/paper/privft-private-and-fast-text-classification
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Comparing two- and three-view Computer Vision


Title	Comparing two- and three-view Computer Vision
Authors	Zsolt Levente Kucsván
Abstract	To reconstruct the points in three dimensional space, we need at least two images. In this paper we compared two different methods: the first uses only two images, the second one uses three. During the research we measured how camera resolution, camera angles and camera distances influence the number of reconstructed points and the dispersion of them. The paper presents that using the two-view method, we can reconstruct significantly more points than using the other one, but the dispersion of points is smaller if we use the three-view method. Taking into consideration the different camera settings, we can say that both the two- and three-view method behaves the same, and the best parameters are also the same for both methods.
Tasks
Published	2019-06-03
URL	https://arxiv.org/abs/1906.01003v1
PDF	https://arxiv.org/pdf/1906.01003v1.pdf
PWC	https://paperswithcode.com/paper/comparing-two-and-three-view-computer-vision
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Adaptive Regularization of Labels


Title	Adaptive Regularization of Labels
Authors	Qianggang Ding, Sifan Wu, Hao Sun, Jiadong Guo, Shu-Tao Xia
Abstract	Recently, a variety of regularization techniques have been widely applied in deep neural networks, such as dropout, batch normalization, data augmentation, and so on. These methods mainly focus on the regularization of weight parameters to prevent overfitting effectively. In addition, label regularization techniques such as label smoothing and label disturbance have also been proposed with the motivation of adding a stochastic perturbation to labels. In this paper, we propose a novel adaptive label regularization method, which enables the neural network to learn from the erroneous experience and update the optimal label representation online. On the other hand, compared with knowledge distillation, which learns the correlation of categories using teacher network, our proposed method requires only a minuscule increase in parameters without cumbersome teacher network. Furthermore, we evaluate our method on CIFAR-10/CIFAR-100/ImageNet datasets for image recognition tasks and AGNews/Yahoo/Yelp-Full datasets for text classification tasks. The empirical results show significant improvement under all experimental settings.
Tasks	Data Augmentation, Text Classification
Published	2019-08-15
URL	https://arxiv.org/abs/1908.05474v1
PDF	https://arxiv.org/pdf/1908.05474v1.pdf
PWC	https://paperswithcode.com/paper/adaptive-regularization-of-labels
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Learning Active Spine Behaviors for Dynamic and Efficient Locomotion in Quadruped Robots


Title	Learning Active Spine Behaviors for Dynamic and Efficient Locomotion in Quadruped Robots
Authors	Shounak Bhattacharya, Abhik Singla, Abhimanyu, Dhaivat Dholakiya, Shalabh Bhatnagar, Bharadwaj Amrutur, Ashitava Ghosal, Shishir Kolathaya
Abstract	In this work, we provide a simulation framework to perform systematic studies on the effects of spinal joint compliance and actuation on bounding performance of a 16-DOF quadruped spined robot Stoch 2. Fast quadrupedal locomotion with active spine is an extremely hard problem, and involves a complex coordination between the various degrees of freedom. Therefore, past attempts at addressing this problem have not seen much success. Deep-Reinforcement Learning seems to be a promising approach, after its recent success in a variety of robot platforms, and the goal of this paper is to use this approach to realize the aforementioned behaviors. With this learning framework, the robot reached a bounding speed of 2.1 m/s with a maximum Froude number of 2. Simulation results also show that use of active spine, indeed, increased the stride length, improved the cost of transport, and also reduced the natural frequency to more realistic values.
Tasks
Published	2019-05-15
URL	https://arxiv.org/abs/1905.06077v2
PDF	https://arxiv.org/pdf/1905.06077v2.pdf
PWC	https://paperswithcode.com/paper/learning-active-spine-behaviors-for-dynamic
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Multivariate Medians for Image and Shape Analysis


Title	Multivariate Medians for Image and Shape Analysis
Authors	Martin Welk
Abstract	Having been studied since long by statisticians, multivariate median concepts found their way into the image processing literature in the course of the last decades, being used to construct robust and efficient denoising filters for multivariate images such as colour images but also matrix-valued images. Based on the similarities between image and geometric data as results of the sampling of continuous physical quantities, it can be expected that the understanding of multivariate median filters for images provides a starting point for the development of shape processing techniques. This paper presents an overview of multivariate median concepts relevant for image and shape processing. It focusses on their mathematical principles and discusses important properties especially in the context of image processing.
Tasks	Denoising
Published	2019-10-31
URL	https://arxiv.org/abs/1911.00143v1
PDF	https://arxiv.org/pdf/1911.00143v1.pdf
PWC	https://paperswithcode.com/paper/multivariate-medians-for-image-and-shape
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3D Shape Completion with Multi-view Consistent Inference


Title	3D Shape Completion with Multi-view Consistent Inference
Authors	Tao Hu, Zhizhong Han, Matthias Zwicker
Abstract	3D shape completion is important to enable machines to perceive the complete geometry of objects from partial observations. To address this problem, view-based methods have been presented. These methods represent shapes as multiple depth images, which can be back-projected to yield corresponding 3D point clouds, and they perform shape completion by learning to complete each depth image using neural networks. While view-based methods lead to state-of-the-art results, they currently do not enforce geometric consistency among the completed views during the inference stage. To resolve this issue, we propose a multi-view consistent inference technique for 3D shape completion, which we express as an energy minimization problem including a data term and a regularization term. We formulate the regularization term as a consistency loss that encourages geometric consistency among multiple views, while the data term guarantees that the optimized views do not drift away too much from a learned shape descriptor. Experimental results demonstrate that our method completes shapes more accurately than previous techniques.
Tasks
Published	2019-11-28
URL	https://arxiv.org/abs/1911.12465v1
PDF	https://arxiv.org/pdf/1911.12465v1.pdf
PWC	https://paperswithcode.com/paper/3d-shape-completion-with-multi-view
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Experiential AI


Title	Experiential AI
Authors	Drew Hemment, Ruth Aylett, Vaishak Belle, Dave Murray-Rust, Ewa Luger, Jane Hillston, Michael Rovatsos, Frank Broz
Abstract	Experiential AI is proposed as a new research agenda in which artists and scientists come together to dispel the mystery of algorithms and make their mechanisms vividly apparent. It addresses the challenge of finding novel ways of opening up the field of artificial intelligence to greater transparency and collaboration between human and machine. The hypothesis is that art can mediate between computer code and human comprehension to overcome the limitations of explanations in and for AI systems. Artists can make the boundaries of systems visible and offer novel ways to make the reasoning of AI transparent and decipherable. Beyond this, artistic practice can explore new configurations of humans and algorithms, mapping the terrain of inter-agencies between people and machines. This helps to viscerally understand the complex causal chains in environments with AI components, including questions about what data to collect or who to collect it about, how the algorithms are chosen, commissioned and configured or how humans are conditioned by their participation in algorithmic processes.
Tasks
Published	2019-08-06
URL	https://arxiv.org/abs/1908.02619v1
PDF	https://arxiv.org/pdf/1908.02619v1.pdf
PWC	https://paperswithcode.com/paper/experiential-ai
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Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy


Title	Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy
Authors	Petru Manescu, Lydia Neary- Zajiczek, Michael J. Shaw, Muna Elmi, Remy Claveau, Vijay Pawar, John Shawe-Taylor, Iasonas Kokkinos, Mandayam A. Srinivasan, Ikeoluwa Lagunju, Olugbemiro Sodeinde, Biobele J. Brown, Delmiro Fernandez-Reyes
Abstract	Fast accurate diagnosis of malaria is still a global health challenge for which automated digital-pathology approaches could provide scalable solutions amenable to be deployed in low-to-middle income countries. Here we address the problem of Extended Depth-of-Field (EDoF) in thick blood film microscopy for rapid automated malaria diagnosis. High magnification oil-objectives (100x) with large numerical aperture are usually preferred to resolve the fine structural details that help separate true parasites from distractors. However, such objectives have a very limited depth-of-field requiring the acquisition of a series of images at different focal planes per field of view (FOV). Current EDoF techniques based on multi-scale decompositions are time consuming and therefore not suited for high-throughput analysis of specimens. To overcome this challenge, we developed a new deep learning method based on Convolutional Neural Networks (EDoF-CNN) that is able to rapidly perform the extended depth-of-field while also enhancing the spatial resolution of the resulting fused image. We evaluated our approach using simulated low-resolution z-stacks from Giemsa-stained thick blood smears from patients presenting with Plasmodium falciparum malaria. The EDoF-CNN allows speed-up of our digital-pathology acquisition platform and significantly improves the quality of the EDoF compared to the traditional multi-scaled approaches when applied to lower resolution stacks corresponding to acquisitions with fewer focal planes, large camera pixel binning or lower magnification objectives (larger FOV). We use the parasite detection accuracy of a deep learning model on the EDoFs as a concrete, task-specific measure of performance of this approach.
Tasks
Published	2019-06-18
URL	https://arxiv.org/abs/1906.07496v1
PDF	https://arxiv.org/pdf/1906.07496v1.pdf
PWC	https://paperswithcode.com/paper/deep-learning-enhanced-extended-depth-of
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Framework

BooVAE: A scalable framework for continual VAE learning under boosting approach


Title	BooVAE: A scalable framework for continual VAE learning under boosting approach
Authors	Anna Kuzina, Evgenii Egorov, Evgeny Burnaev
Abstract	Variational Auto Encoders (VAE) are capable of generating realistic images, sounds and video sequences. From practitioners point of view, we are usually interested in solving problems where tasks are learned sequentially, in a way that avoids revisiting all previous data at each stage. We address this problem by introducing a conceptually simple and scalable end-to-end approach of incorporating past knowledge by learning prior directly from the data. We consider scalable boosting-like approximation for intractable theoretical optimal prior. We provide empirical studies on two commonly used benchmarks, namely MNIST and Fashion MNIST on disjoint sequential image generation tasks. For each dataset proposed method delivers the best results or comparable to SOTA, avoiding catastrophic forgetting in a fully automatic way.
Tasks	Image Generation
Published	2019-08-30
URL	https://arxiv.org/abs/1908.11853v1
PDF	https://arxiv.org/pdf/1908.11853v1.pdf
PWC	https://paperswithcode.com/paper/boovae-a-scalable-framework-for-continual-vae
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Quantum hardness of learning shallow classical circuits


Title	Quantum hardness of learning shallow classical circuits
Authors	Srinivasan Arunachalam, Alex B. Grilo, Aarthi Sundaram
Abstract	In this paper we study the quantum learnability of constant-depth classical circuits under the uniform distribution and in the distribution-independent framework of PAC learning. In order to attain our results, we establish connections between quantum learning and quantum-secure cryptosystems. We then achieve the following results. 1) Hardness of learning AC$^0$ and TC$^0$ under the uniform distribution. Our first result concerns the concept class TC$^0$ (resp. AC$^0$), the class of constant-depth and polynomial-sized circuits with unbounded fan-in majority gates (resp. AND, OR, NOT gates). We show that if there exists no quantum polynomial-time (resp. strong sub-exponential time) algorithm to solve the Ring Learning with Errors (RLWE) problem, then there exists no polynomial-time quantum learning algorithm for TC$^0$ (resp. AC$^0$) under the uniform distribution (even with access to quantum membership queries). The main technique in this result uses explicit pseudo-random functions that are believed to be quantum-secure to construct concept classes that are hard to learn quantumly under the uniform distribution. 2) Hardness of learning TC$^0_2$ in the PAC setting. Our second result shows that if there exists no quantum polynomial time algorithm for the LWE problem, then there exists no polynomial time quantum PAC learning algorithm for the class TC$^0_2$, i.e., depth-2 TC$^0$ circuits. The main technique in this result is to establish a connection between the quantum security of public-key cryptosystems and the learnability of a concept class that consists of decryption functions of the cryptosystem. This gives a strong (conditional) negative answer to one of the “Ten Semi-Grand Challenges for Quantum Computing Theory” raised by Aaronson [Aar05], who asked if AC$^0$ and TC$^0$ can be PAC-learned in quantum polynomial time.
Tasks
Published	2019-03-07
URL	https://arxiv.org/abs/1903.02840v2
PDF	https://arxiv.org/pdf/1903.02840v2.pdf
PWC	https://paperswithcode.com/paper/quantum-hardness-of-learning-shallow
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