pwc-archive/papers-with-abstracts huggingface dataset:
Supervised tensor decomposition with features on multiple modes
Authors: Jiaxin Hu, Chanwoo Lee, Miaoyan Wang
Date: 2019-10-21 00:00:00
Tasks: Dimensionality Reduction, Tensor Decomposition

Higher-order tensors have received increased attention across science and engineering. While most tensor decomposition methods are developed for a single tensor observation, scientific studies often collect side information, in the form of node features and interactions thereof, together with the tensor data. Such data problems are common in neuroimaging, network analysis, and spatial-temporal modeling. Identifying the relationship between a high-dimensional tensor and side information is important yet challenging. Here, we develop a tensor decomposition method that incorporates multiple feature matrices as side information. Unlike unsupervised tensor decomposition, our supervised decomposition captures the effective dimension reduction of the data tensor confined to feature space of interest. An efficient alternating optimization algorithm with provable spectral initialization is further developed. Our proposal handles a broad range of data types, including continuous, count, and binary observations. We apply the method to diffusion tensor imaging data from human connectome project and multi-relational political network data. We identify the key global connectivity pattern and pinpoint the local regions that are associated with available features. Our simulation code, R-package tensorregress, and datasets used in the paper are available at https://CRAN.R-project.org/package=tensorregress.
Policy Optimization for $\mathcal{H}_2$ Linear Control with $\mathcal{H}_\infty$ Robustness Guarantee: Implicit Regularization and Global Convergence
Authors: Kaiqing Zhang, Bin Hu, Tamer Başar
Date: 2019-10-21 00:00:00
Tasks: Policy Gradient Methods, Reinforcement Learning, Reinforcement Learning (RL)

Policy optimization (PO) is a key ingredient for reinforcement learning (RL). For control design, certain constraints are usually enforced on the policies to optimize, accounting for either the stability, robustness, or safety concerns on the system. Hence, PO is by nature a constrained (nonconvex) optimization in most cases, whose global convergence is challenging to analyze in general. More importantly, some constraints that are safety-critical, e.g., the $\mathcal{H}_\infty$-norm constraint that guarantees the system robustness, are difficult to enforce as the PO methods proceed. Recently, policy gradient methods have been shown to converge to the global optimum of linear quadratic regulator (LQR), a classical optimal control problem, without regularizing/projecting the control iterates onto the stabilizing set, its (implicit) feasible set. This striking result is built upon the coercive property of the cost, ensuring that the iterates remain feasible as the cost decreases. In this paper, we study the convergence theory of PO for $\mathcal{H}_2$ linear control with $\mathcal{H}_\infty$-norm robustness guarantee. One significant new feature of this problem is the lack of coercivity, i.e., the cost may have finite value around the feasible set boundary, breaking the existing analysis for LQR. Interestingly, we show that two PO methods enjoy the implicit regularization property, i.e., the iterates preserve the $\mathcal{H}_\infty$ robustness constraint as if they are regularized by the algorithms. Furthermore, despite the nonconvexity of the problem, we show that these algorithms converge to the globally optimal policies with globally sublinear rates, avoiding all suboptimal stationary points/local minima, and with locally (super-)linear rates under certain conditions.
S4NN: temporal backpropagation for spiking neural networks with one spike per neuron
Authors: Saeed Reza Kheradpisheh, Timothée Masquelier
Date: 2019-10-21 00:00:00

We propose a new supervised learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank-order-coding. With this coding scheme, all neurons fire exactly one spike per stimulus, but the firing order carries information. In particular, in the readout layer, the first neuron to fire determines the class of the stimulus. We derive a new learning rule for this sort of network, named S4NN, akin to traditional error backpropagation, yet based on latencies. We show how approximated error gradients can be computed backward in a feedforward network with any number of layers. This approach reaches state-of-the-art performance with supervised multi fully-connected layer SNNs: test accuracy of 97.4% for the MNIST dataset, and 99.2% for the Caltech Face/Motorbike dataset. Yet, the neuron model that we use, non-leaky integrate-and-fire, is much simpler than the one used in all previous works. The source codes of the proposed S4NN are publicly available at https://github.com/SRKH/S4NN.
Relative Interior Rule in Block-Coordinate Minimization
Authors: Tomáš Werner, Daniel Průša
Date: 2019-10-21 00:00:00

(Block-)coordinate minimization is an iterative optimization method which in every iteration finds a global minimum of the objective over a variable or a subset of variables, while keeping the remaining variables constant. While for some problems, coordinate minimization converges to a global minimum (e.g., convex differentiable objective), for general (non-differentiable) convex problems this may not be the case. Despite this drawback, (block-)coordinate minimization can be an acceptable option for large-scale non-differentiable convex problems; an example is methods to solve the linear programming relaxation of the discrete energy minimization problem (MAP inference in graphical models). When block-coordinate minimization is applied to a general convex problem, in every iteration the minimizer over the current coordinate block need not be unique and therefore a single minimizer must be chosen. We propose that this minimizer be chosen from the relative interior of the set of all minimizers over the current block. We show that this rule is not worse, in a certain precise sense, than any other rule.
Sampling random graph homomorphisms and applications to network data analysis
Authors: Hanbaek Lyu, Facundo Memoli, David Sivakoff
Date: 2019-10-21 00:00:00
Tasks: Clustering

A graph homomorphism is a map between two graphs that preserves adjacency relations. We consider the problem of sampling a random graph homomorphism from a graph into a large network. We propose two complementary MCMC algorithms for sampling random graph homomorphisms and establish bounds on their mixing times and the concentration of their time averages. Based on our sampling algorithms, we propose a novel framework for network data analysis that circumvents some of the drawbacks in methods based on independent and neighborhood sampling. Various time averages of the MCMC trajectory give us various computable observables, including well-known ones such as homomorphism density and average clustering coefficient and their generalizations. Furthermore, we show that these network observables are stable with respect to a suitably renormalized cut distance between networks. We provide various examples and simulations demonstrating our framework through synthetic networks. We also \commHL{demonstrate the performance of} our framework on the tasks of network clustering and subgraph classification on the Facebook100 dataset and on Word Adjacency Networks of a set of classic novels.
Toward automatic comparison of visualization techniques: Application to graph visualization
Authors: L. Giovannangeli, R. Bourqui, R. Giot, D. Auber
Date: 2019-10-21 00:00:00
Tasks: BIG-bench Machine Learning, Data Visualization

Many end-user evaluations of data visualization techniques have been run during the last decades. Their results are cornerstones to build efficient visualization systems. However, designing such an evaluation is always complex and time-consuming and may end in a lack of statistical evidence and reproducibility. We believe that modern and efficient computer vision techniques, such as deep convolutional neural networks (CNNs), may help visualization researchers to build and/or adjust their evaluation hypothesis. The basis of our idea is to train machine learning models on several visualization techniques to solve a specific task. Our assumption is that it is possible to compare the efficiency of visualization techniques based on the performance of their corresponding model. As current machine learning models are not able to strictly reflect human capabilities, including their imperfections, such results should be interpreted with caution. However, we think that using machine learning-based pre-evaluation, as a pre-process of standard user evaluations, should help researchers to perform a more exhaustive study of their design space. Thus, it should improve their final user evaluation by providing it better test cases. In this paper, we present the results of two experiments we have conducted to assess how correlated the performance of users and computer vision techniques can be. That study compares two mainstream graph visualization techniques: node-link (\NL) and adjacency-matrix (\MD) diagrams. Using two well-known deep convolutional neural networks, we partially reproduced user evaluations from Ghoniem \textit{et al.} and from Okoe \textit{et al.}. These experiments showed that some user evaluation results can be reproduced automatically.
A Logic-Based Framework Leveraging Neural Networks for Studying the Evolution of Neurological Disorders
Authors: Francesco Calimeri, Francesco Cauteruccio, Luca Cinelli, Aldo Marzullo, Claudio Stamile, Giorgio Terracina, Francoise Durand-Dubief, Dominique Sappey-Marinier
Date: 2019-10-21 00:00:00
Tasks: General Classification

Deductive formalisms have been strongly developed in recent years; among them, Answer Set Programming (ASP) gained some momentum, and has been lately fruitfully employed in many real-world scenarios. Nonetheless, in spite of a large number of success stories in relevant application areas, and even in industrial contexts, deductive reasoning cannot be considered the ultimate, comprehensive solution to AI; indeed, in several contexts, other approaches result to be more useful. Typical Bioinformatics tasks, for instance classification, are currently carried out mostly by Machine Learning (ML) based solutions. In this paper, we focus on the relatively new problem of analyzing the evolution of neurological disorders. In this context, ML approaches already demonstrated to be a viable solution for classification tasks; here, we show how ASP can play a relevant role in the brain evolution simulation task. In particular, we propose a general and extensible framework to support physicians and researchers at understanding the complex mechanisms underlying neurological disorders. The framework relies on a combined use of ML and ASP, and is general enough to be applied in several other application scenarios, which are outlined in the paper.
Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer
Authors: Rae Jeong, Jackie Kay, Francesco Romano, Thomas Lampe, Tom Rothorl, Abbas Abdolmaleki, Tom Erez, Yuval Tassa, Francesco Nori
Date: 2019-10-21 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning robotic control policies in the real world gives rise to challenges in data efficiency, safety, and controlling the initial condition of the system. On the other hand, simulations are a useful alternative as they provide an abundant source of data without the restrictions of the real world. Unfortunately, simulations often fail to accurately model complex real-world phenomena. Traditional system identification techniques are limited in expressiveness by the analytical model parameters, and usually are not sufficient to capture such phenomena. In this paper we propose a general framework for improving the analytical model by optimizing state dependent generalized forces. State dependent generalized forces are expressive enough to model constraints in the equations of motion, while maintaining a clear physical meaning and intuition. We use reinforcement learning to efficiently optimize the mapping from states to generalized forces over a discounted infinite horizon. We show that using only minutes of real world data improves the sim-to-real control policy transfer. We demonstrate the feasibility of our approach by validating it on a nonprehensile manipulation task on the Sawyer robot.
Object landmark discovery through unsupervised adaptation
Authors: Enrique Sanchez, Georgios Tzimiropoulos
Date: 2019-10-21 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Object, Unsupervised Landmark Detection

This paper proposes a method to ease the unsupervised learning of object landmark detectors. Similarly to previous methods, our approach is fully unsupervised in a sense that it does not require or make any use of annotated landmarks for the target object category. Contrary to previous works, we do however assume that a landmark detector, which has already learned a structured representation for a given object category in a fully supervised manner, is available. Under this setting, our main idea boils down to adapting the given pre-trained network to the target object categories in a fully unsupervised manner. To this end, our method uses the pre-trained network as a core which remains frozen and does not get updated during training, and learns, in an unsupervised manner, only a projection matrix to perform the adaptation to the target categories. By building upon an existing structured representation learned in a supervised manner, the optimization problem solved by our method is much more constrained with significantly less parameters to learn which seems to be important for the case of unsupervised learning. We show that our method surpasses fully unsupervised techniques trained from scratch as well as a strong baseline based on fine-tuning, and produces state-of-the-art results on several datasets. Code can be found at https://github.com/ESanchezLozano/SAIC-Unsupervised-landmark-detection-NeurIPS2019 .
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD
Authors: Rosa Candela, Giulio Franzese, Maurizio Filippone, Pietro Michiardi
Date: 2019-10-21 00:00:00
Tasks: Distributed Optimization

Large scale machine learning is increasingly relying on distributed optimization, whereby several machines contribute to the training process of a statistical model. In this work we study the performance of asynchronous, distributed settings, when applying sparsification, a technique used to reduce communication overheads. In particular, for the first time in an asynchronous, non-convex setting, we theoretically prove that, in presence of staleness, sparsification does not harm SGD performance: the ergodic convergence rate matches the known result of standard SGD, that is $\mathcal{O} \left( 1/\sqrt{T} \right)$. We also carry out an empirical study to complement our theory, and confirm that the effects of sparsification on the convergence rate are negligible, when compared to 'vanilla' SGD, even in the challenging scenario of an asynchronous, distributed system.
Learning to Learn by Zeroth-Order Oracle
Authors: Yangjun Ruan, Yuanhao Xiong, Sashank Reddi, Sanjiv Kumar, Cho-Jui Hsieh
Date: 2019-10-21 00:00:00
Proceeding: ICLR 2020 1
Tasks: Adversarial Attack

In the learning to learn (L2L) framework, we cast the design of optimization algorithms as a machine learning problem and use deep neural networks to learn the update rules. In this paper, we extend the L2L framework to zeroth-order (ZO) optimization setting, where no explicit gradient information is available. Our learned optimizer, modeled as recurrent neural network (RNN), first approximates gradient by ZO gradient estimator and then produces parameter update utilizing the knowledge of previous iterations. To reduce high variance effect due to ZO gradient estimator, we further introduce another RNN to learn the Gaussian sampling rule and dynamically guide the query direction sampling. Our learned optimizer outperforms hand-designed algorithms in terms of convergence rate and final solution on both synthetic and practical ZO optimization tasks (in particular, the black-box adversarial attack task, which is one of the most widely used tasks of ZO optimization). We finally conduct extensive analytical experiments to demonstrate the effectiveness of our proposed optimizer.
Improving Vehicle Re-Identification using CNN Latent Spaces: Metrics Comparison and Track-to-track Extension
Authors: Geoffrey Roman-Jimenez, Patrice Guyot, Thierry Malon, Sylvie Chambon, Vincent Charvillat, Alain Crouzil, André Péninou, Julien Pinquier, Florence Sedes, Christine Sénac
Date: 2019-10-21 00:00:00
Tasks: Retrieval, Vehicle Re-Identification

This paper addresses the problem of vehicle re-identification using distance comparison of images in CNN latent spaces. Firstly, we study the impact of the distance metrics, comparing performances obtained with different metrics: the minimal Euclidean distance (MED), the minimal cosine distance (MCD), and the residue of the sparse coding reconstruction (RSCR). These metrics are applied using features extracted from five different CNN architectures, namely ResNet18, AlexNet, VGG16, InceptionV3 and DenseNet201. We use the specific vehicle re-identification dataset VeRi to fine-tune these CNNs and evaluate results. In overall, independently of the CNN used, MCD outperforms MED, commonly used in the literature. These results are confirmed on other vehicle retrieval datasets. Secondly, we extend the state-of-the-art image-to-track process (I2TP) to a track-to-track process (T2TP). The three distance metrics are extended to measure distance between tracks, enabling T2TP. We compared T2TP with I2TP using the same CNN models. Results show that T2TP outperforms I2TP for MCD and RSCR. T2TP combining DenseNet201 and MCD-based metrics exhibits the best performances, outperforming the state-of-the-art I2TP-based models. Finally, experiments highlight two main results: i) the impact of metric choice in vehicle re-identification, and ii) T2TP improves the performances compared to I2TP, especially when coupled with MCD-based metrics.
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
Authors: Eyke Hüllermeier, Willem Waegeman
Date: 2019-10-21 00:00:00
Tasks: BIG-bench Machine Learning

The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this includes the importance of distinguishing between (at least) two different types of uncertainty, often referred to as aleatoric and epistemic. In this paper, we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular.
Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks
Authors: Yihui He, Jianing Qian, Jianren Wang, Cindy X. Le, Congrui Hetang, Qi Lyu, Wenping Wang, Tianwei Yue
Date: 2019-10-21 00:00:00
Tasks: Self-Driving Cars

Very deep convolutional neural networks (CNNs) have been firmly established as the primary methods for many computer vision tasks. However, most state-of-the-art CNNs are large, which results in high inference latency. Recently, depth-wise separable convolution has been proposed for image recognition tasks on computationally limited platforms such as robotics and self-driving cars. Though it is much faster than its counterpart, regular convolution, accuracy is sacrificed. In this paper, we propose a novel decomposition approach based on SVD, namely depth-wise decomposition, for expanding regular convolutions into depthwise separable convolutions while maintaining high accuracy. We show our approach can be further generalized to the multi-channel and multi-layer cases, based on Generalized Singular Value Decomposition (GSVD) [59]. We conduct thorough experiments with the latest ShuffleNet V2 model [47] on both random synthesized dataset and a large-scale image recognition dataset: ImageNet [10]. Our approach outperforms channel decomposition [73] on all datasets. More importantly, our approach improves the Top-1 accuracy of ShuffleNet V2 by ~2%.
HIGhER : Improving instruction following with Hindsight Generation for Experience Replay
Authors: Geoffrey Cideron, Mathieu Seurin, Florian Strub, Olivier Pietquin
Date: 2019-10-21 00:00:00
Tasks: Instruction Following, Language Acquisition, Reinforcement Learning

Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality. While these characterizations may foster instructing, conditioning or structuring interactive agent behavior, it remains an open-problem to correctly relate language understanding and reinforcement learning in even simple instruction following scenarios. This joint learning problem is alleviated through expert demonstrations, auxiliary losses, or neural inductive biases. In this paper, we propose an orthogonal approach called Hindsight Generation for Experience Replay (HIGhER) that extends the Hindsight Experience Replay (HER) approach to the language-conditioned policy setting. Whenever the agent does not fulfill its instruction, HIGhER learns to output a new directive that matches the agent trajectory, and it relabels the episode with a positive reward. To do so, HIGhER learns to map a state into an instruction by using past successful trajectories, which removes the need to have external expert interventions to relabel episodes as in vanilla HER. We show the efficiency of our approach in the BabyAI environment, and demonstrate how it complements other instruction following methods.
Tree-gated Deep Mixture-of-Experts For Pose-robust Face Alignment
Authors: Estephe Arnaud, Arnaud Dapogny, Kevin Bailly
Date: 2019-10-21 00:00:00
Tasks: Face Alignment, Mixture-of-Experts, regression, Robust Face Alignment

Face alignment consists of aligning a shape model on a face image. It is an active domain in computer vision as it is a preprocessing for a number of face analysis and synthesis applications. Current state-of-the-art methods already perform well on "easy" datasets, with moderate head pose variations, but may not be robust for "in-the-wild" data with poses up to 90{\deg}. In order to increase robustness to an ensemble of factors of variations (e.g. head pose or occlusions), a given layer (e.g. a regressor or an upstream CNN layer) can be replaced by a Mixture of Experts (MoE) layer that uses an ensemble of experts instead of a single one. The weights of this mixture can be learned as gating functions to jointly learn the experts and the corresponding weights. In this paper, we propose to use tree-structured gates which allows a hierarchical weighting of the experts (Tree-MoE). We investigate the use of Tree-MoE layers in different contexts in the frame of face alignment with cascaded regression, firstly for emphasizing relevant, more specialized feature extractors depending of a high-level semantic information such as head pose (Pose-Tree-MoE), and secondly as an overall more robust regression layer. We perform extensive experiments on several challenging face alignment datasets, demonstrating that our approach outperforms the state-of-the-art methods.