Paper Group ANR 431
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems. Cryo-Electron Microscopy Image Analysis Using Multi-Frequency Vector Diffusion Maps. The Information Complexity of Learning Tasks, their Structure and their Distance. A New Benchmark Dataset for Texture Image Analysis and Surface Defect Detection. Pre …
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems
| Title | Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems |
| Authors | Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, Prasanna Balaprakash |
| Abstract | Quantum computing exploits basic quantum phenomena such as state superposition and entanglement to perform computations. The Quantum Approximate Optimization Algorithm (QAOA) is arguably one of the leading quantum algorithms that can outperform classical state-of-the-art methods in the near term. QAOA is a hybrid quantum-classical algorithm that combines a parameterized quantum state evolution with a classical optimization routine to approximately solve combinatorial problems. The quality of the solution obtained by QAOA within a fixed budget of calls to the quantum computer depends on the performance of the classical optimization routine used to optimize the variational parameters. In this work, we propose an approach based on reinforcement learning (RL) to train a policy network that can be used to quickly find high-quality variational parameters for unseen combinatorial problem instances. The RL agent is trained on small problem instances which can be simulated on a classical computer, yet the learned RL policy is generalizable and can be used to efficiently solve larger instances. Extensive simulations using the IBM Qiskit Aer quantum circuit simulator demonstrate that our trained RL policy can reduce the optimality gap by a factor up to 8.61 compared with other off-the-shelf optimizers tested. |
| Tasks | |
| Published | 2019-11-11 |
| URL | https://arxiv.org/abs/1911.04574v1 |
| https://arxiv.org/pdf/1911.04574v1.pdf | |
| PWC | https://paperswithcode.com/paper/reinforcement-learning-based-variational |
| Repo | |
| Framework | |
Cryo-Electron Microscopy Image Analysis Using Multi-Frequency Vector Diffusion Maps
| Title | Cryo-Electron Microscopy Image Analysis Using Multi-Frequency Vector Diffusion Maps |
| Authors | Yifeng Fan, Zhizhen Zhao |
| Abstract | Cryo-electron microscopy (EM) single particle reconstruction is an entirely general technique for 3D structure determination of macromolecular complexes. However, because the images are taken at low electron dose, it is extremely hard to visualize the individual particle with low contrast and high noise level. In this paper, we propose a novel approach called multi-frequency vector diffusion maps (MFVDM) to improve the efficiency and accuracy of cryo-EM 2D image classification and denoising. This framework incorporates different irreducible representations of the estimated alignment between similar images. In addition, we propose a graph filtering scheme to denoise the images using the eigenvalues and eigenvectors of the MFVDM matrices. Through both simulated and publicly available real data, we demonstrate that our proposed method is efficient and robust to noise compared with the state-of-the-art cryo-EM 2D class averaging and image restoration algorithms. |
| Tasks | Denoising, Image Classification, Image Restoration |
| Published | 2019-04-16 |
| URL | http://arxiv.org/abs/1904.07772v1 |
| http://arxiv.org/pdf/1904.07772v1.pdf | |
| PWC | https://paperswithcode.com/paper/cryo-electron-microscopy-image-analysis-using |
| Repo | |
| Framework | |
The Information Complexity of Learning Tasks, their Structure and their Distance
| Title | The Information Complexity of Learning Tasks, their Structure and their Distance |
| Authors | Alessandro Achille, Giovanni Paolini, Glen Mbeng, Stefano Soatto |
| Abstract | We introduce an asymmetric distance in the space of learning tasks, and a framework to compute their complexity. These concepts are foundational to the practice of transfer learning, ubiquitous in Deep Learning, whereby a parametric model is pre-trained for a task, and then used for another after fine-tuning. The framework we develop is intrinsically non-asymptotic, capturing the finite nature of the training dataset, yet it allows distinguishing learning from memorization. It encompasses, as special cases, classical notions from Kolmogorov complexity, Shannon, and Fisher Information. However, unlike some of those frameworks, it can be applied easily to large-scale models and real-world datasets. It is the first framework to explicitly account for the optimization scheme, which plays a crucial role in Deep Learning, in measuring complexity and information. |
| Tasks | Transfer Learning |
| Published | 2019-04-05 |
| URL | http://arxiv.org/abs/1904.03292v1 |
| http://arxiv.org/pdf/1904.03292v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-information-complexity-of-learning-tasks |
| Repo | |
| Framework | |
A New Benchmark Dataset for Texture Image Analysis and Surface Defect Detection
| Title | A New Benchmark Dataset for Texture Image Analysis and Surface Defect Detection |
| Authors | Shervan Fekri-Ershad |
| Abstract | Texture analysis plays an important role in many image processing applications to describe the image content or objects. On the other hand, visual surface defect detection is a highly research field in the computer vision. Surface defect refers to abnormalities in the texture of the surface. So, in this paper a dual purpose benchmark dataset is proposed for texture image analysis and surface defect detection titled stone texture image (STI dataset). The proposed benchmark dataset consist of 4 different class of stone texture images. The proposed benchmark dataset have some unique properties to make it very near to real applications. Local rotation, different zoom rates, unbalanced classes, variation of textures in size are some properties of the proposed dataset. In the result part, some descriptors are applied on this dataset to evaluate the proposed STI dataset in comparison with other state-of-the-art datasets. |
| Tasks | Texture Classification |
| Published | 2019-06-27 |
| URL | https://arxiv.org/abs/1906.11561v1 |
| https://arxiv.org/pdf/1906.11561v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-new-benchmark-dataset-for-texture-image |
| Repo | |
| Framework | |
Preventing Posterior Collapse in Sequence VAEs with Pooling
| Title | Preventing Posterior Collapse in Sequence VAEs with Pooling |
| Authors | Teng Long, Yanshuai Cao, Jackie Chi Kit Cheung |
| Abstract | Variational Autoencoders (VAEs) hold great potential for modelling text, as they could in theory separate high-level semantic and syntactic properties from local regularities of natural language. Practically, however, VAEs with autoregressive decoders often suffer from posterior collapse, a phenomenon where the model learns to ignore the latent variables, causing the sequence VAE to degenerate into a language model. Previous works attempt to solve this problem with complex architectural changes or costly optimization schemes. In this paper, we argue that posterior collapse is caused in part by the encoder network failing to capture the input variabilities. We verify this hypothesis empirically and propose a straightforward fix using pooling. This simple technique effectively prevents posterior collapse, allowing the model to achieve significantly better data log-likelihood than standard sequence VAEs. Compared to the previous SOTA on preventing posterior collapse, we are able to achieve comparable performances while being significantly faster. |
| Tasks | Language Modelling |
| Published | 2019-11-10 |
| URL | https://arxiv.org/abs/1911.03976v1 |
| https://arxiv.org/pdf/1911.03976v1.pdf | |
| PWC | https://paperswithcode.com/paper/preventing-posterior-collapse-in-sequence |
| Repo | |
| Framework | |
Algorithmic Probability-guided Supervised Machine Learning on Non-differentiable Spaces
| Title | Algorithmic Probability-guided Supervised Machine Learning on Non-differentiable Spaces |
| Authors | Santiago Hernández-Orozco, Hector Zenil, Jürgen Riedel, Adam Uccello, Narsis A. Kiani, Jesper Tegnér |
| Abstract | We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research. We show that this new approach requires less training data and is more generalizable as it shows greater resilience to random attacks. We investigate the shape of the discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not necessary to achieve results similar to those obtained using differentiable programming approaches such as deep learning. In doing so we use examples which enable the two approaches to be compared (small, given the computational power required for estimations of algorithmic complexity). We find and report that (i) machine learning can successfully be performed on a non-smooth surface using algorithmic complexity; (ii) that parameter solutions can be found using an algorithmic-probability classifier, establishing a bridge between a fundamentally discrete theory of computability and a fundamentally continuous mathematical theory of optimization methods; (iii) a formulation of an algorithmically directed search technique in non-smooth manifolds can be defined and conducted; (iv) exploitation techniques and numerical methods for algorithmic search to navigate these discrete non-differentiable spaces can be performed; in application of the (a) identification of generative rules from data observations; (b) solutions to image classification problems more resilient against pixel attacks compared to neural networks; (c) identification of equation parameters from a small data-set in the presence of noise in continuous ODE system problem, (d) classification of Boolean NK networks by (1) network topology, (2) underlying Boolean function, and (3) number of incoming edges. |
| Tasks | Image Classification |
| Published | 2019-10-07 |
| URL | https://arxiv.org/abs/1910.02758v2 |
| https://arxiv.org/pdf/1910.02758v2.pdf | |
| PWC | https://paperswithcode.com/paper/algorithmic-probability-guided-supervised |
| Repo | |
| Framework | |
Latent Multi-Criteria Ratings for Recommendations
| Title | Latent Multi-Criteria Ratings for Recommendations |
| Authors | Pan Li, Alexander Tuzhilin |
| Abstract | Multi-criteria recommender systems have been increasingly valuable for helping consumers identify the most relevant items based on different dimensions of user experiences. However, previously proposed multi-criteria models did not take into account latent embeddings generated from user reviews, which capture latent semantic relations between users and items. To address these concerns, we utilize variational autoencoders to map user reviews into latent embeddings, which are subsequently compressed into low-dimensional discrete vectors. The resulting compressed vectors constitute latent multi-criteria ratings that we use for the recommendation purposes via standard multi-criteria recommendation methods. We show that the proposed latent multi-criteria rating approach outperforms several baselines significantly and consistently across different datasets and performance evaluation measures. |
| Tasks | Recommendation Systems |
| Published | 2019-06-26 |
| URL | https://arxiv.org/abs/1906.10948v1 |
| https://arxiv.org/pdf/1906.10948v1.pdf | |
| PWC | https://paperswithcode.com/paper/latent-multi-criteria-ratings-for |
| Repo | |
| Framework | |
Interference Prediction in Wireless Networks: Stochastic Geometry meets Recursive Filtering
| Title | Interference Prediction in Wireless Networks: Stochastic Geometry meets Recursive Filtering |
| Authors | Jorge F. Schmidt, Udo Schilcher, Mahin K. Atiq, Christian Bettstetter |
| Abstract | This article proposes and evaluates a technique to predict the level of interference in wireless networks. We design a recursive predictor that computes future interference values at a given location by filtering measured interference at this location. The parametrization of the predictor is done offline by translating the autocorrelation of interference into an autoregressive moving average (ARMA) representation. This ARMA model is inserted into a steady-state Kalman filter enabling nodes to predict with low computational effort. Results show good performance in terms of accuracy between predicted and true values for relevant time horizons. Although the predictor is parametrized for the case of Poisson networks, Rayleigh fading, and fixed message lengths, a sensitivity analysis shows that it also works well in more general network scenarios. Numerical examples for underlay device-to-device communications and a common wireless sensor technology illustrate its broad applicability. The predictor can be applied as part of interference management to improve medium access, scheduling, and resource allocation. |
| Tasks | |
| Published | 2019-03-26 |
| URL | http://arxiv.org/abs/1903.10899v1 |
| http://arxiv.org/pdf/1903.10899v1.pdf | |
| PWC | https://paperswithcode.com/paper/interference-prediction-in-wireless-networks |
| Repo | |
| Framework | |
Modeling financial analysts’ decision making via the pragmatics and semantics of earnings calls
| Title | Modeling financial analysts’ decision making via the pragmatics and semantics of earnings calls |
| Authors | Katherine A. Keith, Amanda Stent |
| Abstract | Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts. After these calls, analysts often change their price target recommendations, which are used in equity research reports to help investors make decisions. In this paper, we examine analysts’ decision making behavior as it pertains to the language content of earnings calls. We identify a set of 20 pragmatic features of analysts’ questions which we correlate with analysts’ pre-call investor recommendations. We also analyze the degree to which semantic and pragmatic features from an earnings call complement market data in predicting analysts’ post-call changes in price targets. Our results show that earnings calls are moderately predictive of analysts’ decisions even though these decisions are influenced by a number of other factors including private communication with company executives and market conditions. A breakdown of model errors indicates disparate performance on calls from different market sectors. |
| Tasks | Decision Making |
| Published | 2019-06-07 |
| URL | https://arxiv.org/abs/1906.02868v2 |
| https://arxiv.org/pdf/1906.02868v2.pdf | |
| PWC | https://paperswithcode.com/paper/modeling-financial-analysts-decision-making |
| Repo | |
| Framework | |
On the Complexity of Approximating Multimarginal Optimal Transport
| Title | On the Complexity of Approximating Multimarginal Optimal Transport |
| Authors | Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan |
| Abstract | We study the complexity of approximating the multimarginal optimal transport (OT) problem, a generalization of the classical optimal transport distance, considered here between $m$ discrete probability distributions supported each on $n$ support points. First, we show that the multimarginal OT problem is not a minimum-cost flow problem when $m \geq 3$. This implies that many of the combinatorial algorithms developed for classical OT are not applicable to multimarginal OT, and therefore the standard interior-point algorithm bounds result in an intractable complexity bound of $\widetilde{\mathcal{O}}(n^{3m})$. Second, we propose and analyze two simple algorithms for approximating the multimarginal OT problem. The first algorithm, which we refer to as multimarginal Sinkhorn, improves upon previous multimarginal generalizations of the celebrated Sinkhorn algorithm. We show that it achieves a near-linear time complexity bound of $\widetilde{\mathcal{O}}(m^3 n^m / \varepsilon^2)$ for a tolerance $\varepsilon \in (0, 1)$. This matches the best known complexity bound for the Sinkhorn algorithm when $m = 2$ for approximating the classical OT distance. The second algorithm, which we refer to as multimarginal Randkhorn, accelerates the first algorithm by incorporating a randomized estimate sequence and achieves a complexity bound of $\widetilde{\mathcal{O}}(m^{8/3} n^{m+1/3}/\varepsilon)$. This improves on the complexity bound of the first algorithm by $1/\varepsilon$ and matches the best known complexity bound for the Randkhorn algorithm when $m=2$ for approximating the classical OT distance. |
| Tasks | |
| Published | 2019-09-30 |
| URL | https://arxiv.org/abs/1910.00152v1 |
| https://arxiv.org/pdf/1910.00152v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-complexity-of-approximating |
| Repo | |
| Framework | |
Evolving Boolean Functions with Conjunctions and Disjunctions via Genetic Programming
| Title | Evolving Boolean Functions with Conjunctions and Disjunctions via Genetic Programming |
| Authors | Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto |
| Abstract | Recently it has been proved that simple GP systems can efficiently evolve the conjunction of $n$ variables if they are equipped with the minimal required components. In this paper, we make a considerable step forward by analysing the behaviour and performance of the GP system for evolving a Boolean function with unknown components, i.e., the function may consist of both conjunctions and disjunctions. We rigorously prove that if the target function is the conjunction of $n$ variables, then the RLS-GP using the complete truth table to evaluate program quality evolves the exact target function in $O(\ell n \log^2 n)$ iterations in expectation, where $\ell \geq n$ is a limit on the size of any accepted tree. When, as in realistic applications, only a polynomial sample of possible inputs is used to evaluate solution quality, we show how RLS-GP can evolve a conjunction with any polynomially small generalisation error with probability $1 - O(\log^2(n)/n)$. To produce our results we introduce a super-multiplicative drift theorem that gives significantly stronger runtime bounds when the expected progress is only slightly super-linear in the distance from the optimum. |
| Tasks | |
| Published | 2019-03-28 |
| URL | http://arxiv.org/abs/1903.11936v2 |
| http://arxiv.org/pdf/1903.11936v2.pdf | |
| PWC | https://paperswithcode.com/paper/evolving-boolean-functions-with-conjunctions |
| Repo | |
| Framework | |
Reinforcement Learning in Non-Stationary Environments
| Title | Reinforcement Learning in Non-Stationary Environments |
| Authors | Sindhu Padakandla, Prabuchandran K. J, Shalabh Bhatnagar |
| Abstract | Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary environment. However, the stationary assumption on the environment is very restrictive. In many real world problems like traffic signal control, robotic applications, one often encounters situations with non-stationary environments and in these scenarios, RL methods yield sub-optimal decisions. In this paper, we thus consider the problem of developing RL methods that obtain optimal decisions in a non-stationary environment. The goal of this problem is to maximize the long-term discounted reward achieved when the underlying model of the environment changes over time. To achieve this, we first adapt a change point algorithm to detect change in the statistics of the environment and then develop an RL algorithm that maximizes the long-run reward accrued. We illustrate that our change point method detects change in the model of the environment effectively and thus facilitates the RL algorithm in maximizing the long-run reward. We further validate the effectiveness of the proposed solution on non-stationary random Markov decision processes, a sensor energy management problem and a traffic signal control problem. |
| Tasks | |
| Published | 2019-05-10 |
| URL | https://arxiv.org/abs/1905.03970v2 |
| https://arxiv.org/pdf/1905.03970v2.pdf | |
| PWC | https://paperswithcode.com/paper/reinforcement-learning-in-non-stationary |
| Repo | |
| Framework | |
On functions computed on trees
| Title | On functions computed on trees |
| Authors | Roozbeh Farhoodi, Khashayar Filom, Ilenna Simone Jones, Konrad Paul Kording |
| Abstract | Any function can be constructed using a hierarchy of simpler functions through compositions. Such a hierarchy can be characterized by a binary rooted tree. Each node of this tree is associated with a function which takes as inputs two numbers from its children and produces one output. Since thinking about functions in terms of computation graphs is getting popular we may want to know which functions can be implemented on a given tree. Here, we describe a set of necessary constraints in the form of a system of non-linear partial differential equations that must be satisfied. Moreover, we prove that these conditions are sufficient in both contexts of analytic and bit-valued functions. In the latter case, we explicitly enumerate discrete functions and observe that there are relatively few. Our point of view allows us to compare different neural network architectures in regard to their function spaces. Our work connects the structure of computation graphs with the functions they can implement and has potential applications to neuroscience and computer science. |
| Tasks | |
| Published | 2019-04-04 |
| URL | https://arxiv.org/abs/1904.02309v4 |
| https://arxiv.org/pdf/1904.02309v4.pdf | |
| PWC | https://paperswithcode.com/paper/on-functions-computed-on-trees |
| Repo | |
| Framework | |
An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients
| Title | An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients |
| Authors | Ya Lu, Thomai Stathopoulou, Maria F. Vasiloglou, Stergios Christodoulidis, Beat Blum, Thomas Walser, Vinzenz Meier, Zeno Stanga, Stavroula G. Mougiakakou |
| Abstract | Regular nutrient intake monitoring in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition (DRM). Although several methods to estimate nutrient intake have been developed, there is still a clear demand for a more reliable and fully automated technique, as this could improve the data accuracy and reduce both the participant burden and the health costs. In this paper, we propose a novel system based on artificial intelligence to accurately estimate nutrient intake, by simply processing RGB depth image pairs captured before and after a meal consumption. For the development and evaluation of the system, a dedicated and new database of images and recipes of 322 meals was assembled, coupled to data annotation using innovative strategies. With this database, a system was developed that employed a novel multi-task neural network and an algorithm for 3D surface construction. This allowed sequential semantic food segmentation and estimation of the volume of the consumed food, and permitted fully automatic estimation of nutrient intake for each food type with a 15% estimation error. |
| Tasks | |
| Published | 2019-06-07 |
| URL | https://arxiv.org/abs/1906.02990v2 |
| https://arxiv.org/pdf/1906.02990v2.pdf | |
| PWC | https://paperswithcode.com/paper/an-artificial-intelligence-based-system-for |
| Repo | |
| Framework | |
Multi-View Broad Learning System for Primate Oculomotor Decision Decoding
| Title | Multi-View Broad Learning System for Primate Oculomotor Decision Decoding |
| Authors | Zhenhua Shi, Xiaomo Chen, Changming Zhao, He He, Veit Stuphorn, Dongrui Wu |
| Abstract | Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source. This approach is suitable for primate brain state decoding using cortical neural signals. This is because the complementary components of simultaneously recorded neural signals, local field potentials (LFPs) and action potentials (spikes), can be treated as two views. In this paper, we extended broad learning system (BLS), a recently proposed wide neural network architecture, from single-view learning to multi-view learning, and validated its performance in decoding monkeys’ oculomotor decision from medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in non-human primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approach for decoding the oculomotor decision than several classical and state-of-the-art single-view and multi-view learning approaches. |
| Tasks | MULTI-VIEW LEARNING |
| Published | 2019-08-16 |
| URL | https://arxiv.org/abs/1908.06180v2 |
| https://arxiv.org/pdf/1908.06180v2.pdf | |
| PWC | https://paperswithcode.com/paper/multi-view-broad-learning-system-for-primate |
| Repo | |
| Framework | |
