pwc-archive/papers-with-abstracts huggingface dataset:
Rehabilitating Isomap: Euclidean Representation of Geodesic Structure
Authors: Michael W. Trosset, Gokcen Buyukbas
Date: 2020-06-18 00:00:00
Tasks: Dimensionality Reduction

Manifold learning techniques for nonlinear dimension reduction assume that high-dimensional feature vectors lie on a low-dimensional manifold, then attempt to exploit manifold structure to obtain useful low-dimensional Euclidean representations of the data. Isomap, a seminal manifold learning technique, is an elegant synthesis of two simple ideas: the approximation of Riemannian distances with shortest path distances on a graph that localizes manifold structure, and the approximation of shortest path distances with Euclidean distances by multidimensional scaling. We revisit the rationale for Isomap, clarifying what Isomap does and what it does not. In particular, we explore the widespread perception that Isomap should only be used when the manifold is parametrized by a convex region of Euclidean space. We argue that this perception is based on an extremely narrow interpretation of manifold learning as parametrization recovery, and we submit that Isomap is better understood as constructing Euclidean representations of geodesic structure. We reconsider a well-known example that was previously interpreted as evidence of Isomap's limitations, and we re-examine the original analysis of Isomap's convergence properties, concluding that convexity is not required for shortest path distances to converge to Riemannian distances.
Image classification in frequency domain with 2SReLU: a second harmonics superposition activation function
Authors: Thomio Watanabe, Denis F. Wolf
Date: 2020-06-18 00:00:00
Tasks: General Classification, image-classification, Image Classification

Deep Convolutional Neural Networks are able to identify complex patterns and perform tasks with super-human capabilities. However, besides the exceptional results, they are not completely understood and it is still impractical to hand-engineer similar solutions. In this work, an image classification Convolutional Neural Network and its building blocks are described from a frequency domain perspective. Some network layers have established counterparts in the frequency domain like the convolutional and pooling layers. We propose the 2SReLU layer, a novel non-linear activation function that preserves high frequency components in deep networks. It is demonstrated that in the frequency domain it is possible to achieve competitive results without using the computationally costly convolution operation. A source code implementation in PyTorch is provided at: https://gitlab.com/thomio/2srelu
Deep Image Translation for Enhancing Simulated Ultrasound Images
Authors: Lin Zhang, Tiziano Portenier, Christoph Paulus, Orcun Goksel
Date: 2020-06-18 00:00:00
Tasks: Image-to-Image Translation, Translation

Ultrasound simulation based on ray tracing enables the synthesis of highly realistic images. It can provide an interactive environment for training sonographers as an educational tool. However, due to high computational demand, there is a trade-off between image quality and interactivity, potentially leading to sub-optimal results at interactive rates. In this work we introduce a deep learning approach based on adversarial training that mitigates this trade-off by improving the quality of simulated images with constant computation time. An image-to-image translation framework is utilized to translate low quality images into high quality versions. To incorporate anatomical information potentially lost in low quality images, we additionally provide segmentation maps to image translation. Furthermore, we propose to leverage information from acoustic attenuation maps to better preserve acoustic shadows and directional artifacts, an invaluable feature for ultrasound image interpretation. The proposed method yields an improvement of 7.2% in Fr\'{e}chet Inception Distance and 8.9% in patch-based Kullback-Leibler divergence.
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
Authors: Robin Tibor Schirrmeister, Yuxuan Zhou, Tonio Ball, Dan Zhang
Date: 2020-06-18 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Anomaly Detection

Deep generative networks trained via maximum likelihood on a natural image dataset like CIFAR10 often assign high likelihoods to images from datasets with different objects (e.g., SVHN). We refine previous investigations of this failure at anomaly detection for invertible generative networks and provide a clear explanation of it as a combination of model bias and domain prior: Convolutional networks learn similar low-level feature distributions when trained on any natural image dataset and these low-level features dominate the likelihood. Hence, when the discriminative features between inliers and outliers are on a high-level, e.g., object shapes, anomaly detection becomes particularly challenging. To remove the negative impact of model bias and domain prior on detecting high-level differences, we propose two methods, first, using the log likelihood ratios of two identical models, one trained on the in-distribution data (e.g., CIFAR10) and the other one on a more general distribution of images (e.g., 80 Million Tiny Images). We also derive a novel outlier loss for the in-distribution network on samples from the more general distribution to further improve the performance. Secondly, using a multi-scale model like Glow, we show that low-level features are mainly captured at early scales. Therefore, using only the likelihood contribution of the final scale performs remarkably well for detecting high-level feature differences of the out-of-distribution and the in-distribution. This method is especially useful if one does not have access to a suitable general distribution. Overall, our methods achieve strong anomaly detection performance in the unsupervised setting, and only slightly underperform state-of-the-art classifier-based methods in the supervised setting. Code can be found at https://github.com/boschresearch/hierarchical_anomaly_detection.
SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic
Authors: Qiang Yang, Hind Alamro, Somayah Albaradei, Adil Salhi, Xiaoting Lv, Changsheng Ma, Manal Alshehri, Inji Jaber, Faroug Tifratene, Wei Wang, Takashi Gojobori, Carlos M. Duarte, Xin Gao, Xiangliang Zhang
Date: 2020-06-18 00:00:00
Tasks: Language Modelling, Sentiment Analysis

Since the first alert launched by the World Health Organization (5 January, 2020), COVID-19 has been spreading out to over 180 countries and territories. As of June 18, 2020, in total, there are now over 8,400,000 cases and over 450,000 related deaths. This causes massive losses in the economy and jobs globally and confining about 58% of the global population. In this paper, we introduce SenWave, a novel sentimental analysis work using 105+ million collected tweets and Weibo messages to evaluate the global rise and falls of sentiments during the COVID-19 pandemic. To make a fine-grained analysis on the feeling when we face this global health crisis, we annotate 10K tweets in English and 10K tweets in Arabic in 10 categories, including optimistic, thankful, empathetic, pessimistic, anxious, sad, annoyed, denial, official report, and joking. We then utilize an integrated transformer framework, called simpletransformer, to conduct multi-label sentimental classification by fine-tuning the pre-trained language model on the labeled data. Meanwhile, in order for a more complete analysis, we also translate the annotated English tweets into different languages (Spanish, Italian, and French) to generated training data for building sentiment analysis models for these languages. SenWave thus reveals the sentiment of global conversation in six different languages on COVID-19 (covering English, Spanish, French, Italian, Arabic and Chinese), followed the spread of the epidemic. The conversation showed a remarkably similar pattern of rapid rise and slow decline over time across all nations, as well as on special topics like the herd immunity strategies, to which the global conversation reacts strongly negatively. Overall, SenWave shows that optimistic and positive sentiments increased over time, foretelling a desire to seek, together, a reset for an improved COVID-19 world.
Learning non-rigid surface reconstruction from spatio-temporal image patches
Authors: Matteo Pedone, Abdelrahman Mostafa, Janne Heikkilä
Date: 2020-06-18 00:00:00
Tasks: Surface Reconstruction

We present a method to reconstruct a dense spatio-temporal depth map of a non-rigidly deformable object directly from a video sequence. The estimation of depth is performed locally on spatio-temporal patches of the video, and then the full depth video of the entire shape is recovered by combining them together. Since the geometric complexity of a local spatio-temporal patch of a deforming non-rigid object is often simple enough to be faithfully represented with a parametric model, we artificially generate a database of small deforming rectangular meshes rendered with different material properties and light conditions, along with their corresponding depth videos, and use such data to train a convolutional neural network. We tested our method on both synthetic and Kinect data and experimentally observed that the reconstruction error is significantly lower than the one obtained using other approaches like conventional non-rigid structure from motion.
Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping
Authors: Nicole Mücke, Enrico Reiss
Date: 2020-06-18 00:00:00

Stochastic Gradient Descent (SGD) has become the method of choice for solving a broad range of machine learning problems. However, some of its learning properties are still not fully understood. We consider least squares learning in reproducing kernel Hilbert spaces (RKHSs) and extend the classical SGD analysis to a learning setting in Hilbert scales, including Sobolev spaces and Diffusion spaces on compact Riemannian manifolds. We show that even for well-specified models, violation of a traditional benchmark smoothness assumption has a tremendous effect on the learning rate. In addition, we show that for miss-specified models, preconditioning in an appropriate Hilbert scale helps to reduce the number of iterations, i.e. allowing for "earlier stopping".
An Integer Linear Programming Framework for Mining Constraints from Data
Authors: Tao Meng, Kai-Wei Chang
Date: 2020-06-18 00:00:00
Tasks: Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Structured output prediction problems (e.g., sequential tagging, hierarchical multi-class classification) often involve constraints over the output label space. These constraints interact with the learned models to filter infeasible solutions and facilitate in building an accountable system. However, although constraints are useful, they are often based on hand-crafted rules. This raises a question -- \emph{can we mine constraints and rules from data based on a learning algorithm?} In this paper, we present a general framework for mining constraints from data. In particular, we consider the inference in structured output prediction as an integer linear programming (ILP) problem. Then, given the coefficients of the objective function and the corresponding solution, we mine the underlying constraints by estimating the outer and inner polytopes of the feasible set. We verify the proposed constraint mining algorithm in various synthetic and real-world applications and demonstrate that the proposed approach successfully identifies the feasible set at scale. In particular, we show that our approach can learn to solve 9x9 Sudoku puzzles and minimal spanning tree problems from examples without providing the underlying rules. Our algorithm can also integrate with a neural network model to learn the hierarchical label structure of a multi-label classification task. Besides, we provide a theoretical analysis about the tightness of the polytopes and the reliability of the mined constraints.
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
Authors: Sindy Löwe, David Madras, Richard Zemel, Max Welling
Date: 2020-06-18 00:00:00
Tasks: Causal Discovery, Time Series, Time Series Analysis

On time-series data, most causal discovery methods fit a new model whenever they encounter samples from a new underlying causal graph. However, these samples often share relevant information which is lost when following this approach. Specifically, different samples may share the dynamics which describe the effects of their causal relations. We propose Amortized Causal Discovery, a novel framework that leverages such shared dynamics to learn to infer causal relations from time-series data. This enables us to train a single, amortized model that infers causal relations across samples with different underlying causal graphs, and thus leverages the shared dynamics information. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance, and show how it can be extended to perform well under added noise and hidden confounding.
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula
Authors: Yuxuan Zhao, Madeleine Udell
Date: 2020-06-18 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Imputation, Matrix Completion, Missing Values, Recommendation Systems

Modern large scale datasets are often plagued with missing entries. For tabular data with missing values, a flurry of imputation algorithms solve for a complete matrix which minimizes some penalized reconstruction error. However, almost none of them can estimate the uncertainty of its imputations. This paper proposes a probabilistic and scalable framework for missing value imputation with quantified uncertainty. Our model, the Low Rank Gaussian Copula, augments a standard probabilistic model, Probabilistic Principal Component Analysis, with marginal transformations for each column that allow the model to better match the distribution of the data. It naturally handles Boolean, ordinal, and real-valued observations and quantifies the uncertainty in each imputation. The time required to fit the model scales linearly with the number of rows and the number of columns in the dataset. Empirical results show the method yields state-of-the-art imputation accuracy across a wide range of data types, including those with high rank. Our uncertainty measure predicts imputation error well: entries with lower uncertainty do have lower imputation error (on average). Moreover, for real-valued data, the resulting confidence intervals are well-calibrated.
Stability of Internal States in Recurrent Neural Networks Trained on Regular Languages
Authors: Christian Oliva, Luis F. Lago-Fernández
Date: 2020-06-18 00:00:00

We provide an empirical study of the stability of recurrent neural networks trained to recognize regular languages. When a small amount of noise is introduced into the activation function, the neurons in the recurrent layer tend to saturate in order to compensate the variability. In this saturated regime, analysis of the network activation shows a set of clusters that resemble discrete states in a finite state machine. We show that transitions between these states in response to input symbols are deterministic and stable. The networks display a stable behavior for arbitrarily long strings, and when random perturbations are applied to any of the states, they are able to recover and their evolution converges to the original clusters. This observation reinforces the interpretation of the networks as finite automata, with neurons or groups of neurons coding specific and meaningful input patterns.
Cooperative Multi-Agent Reinforcement Learning with Partial Observations
Authors: Yan Zhang, Michael M. Zavlanos
Date: 2020-06-18 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose a distributed zeroth-order policy optimization method for Multi-Agent Reinforcement Learning (MARL). Existing MARL algorithms often assume that every agent can observe the states and actions of all the other agents in the network. This can be impractical in large-scale problems, where sharing the state and action information with multi-hop neighbors may incur significant communication overhead. The advantage of the proposed zeroth-order policy optimization method is that it allows the agents to compute the local policy gradients needed to update their local policy functions using local estimates of the global accumulated rewards that depend on partial state and action information only and can be obtained using consensus. Specifically, to calculate the local policy gradients, we develop a new distributed zeroth-order policy gradient estimator that relies on one-point residual-feedback which, compared to existing zeroth-order estimators that also rely on one-point feedback, significantly reduces the variance of the policy gradient estimates improving, in this way, the learning performance. We show that the proposed distributed zeroth-order policy optimization method with constant stepsize converges to the neighborhood of a policy that is a stationary point of the global objective function. The size of this neighborhood depends on the agents' learning rates, the exploration parameters, and the number of consensus steps used to calculate the local estimates of the global accumulated rewards. Moreover, we provide numerical experiments that demonstrate that our new zeroth-order policy gradient estimator is more sample-efficient compared to other existing one-point estimators.
A New One-Point Residual-Feedback Oracle For Black-Box Learning and Control
Authors: Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos
Date: 2020-06-18 00:00:00

Zeroth-order optimization (ZO) algorithms have been recently used to solve black-box or simulation-based learning and control problems, where the gradient of the objective function cannot be easily computed but can be approximated using the objective function values. Many existing ZO algorithms adopt two-point feedback schemes due to their fast convergence rate compared to one-point feedback schemes. However, two-point schemes require two evaluations of the objective function at each iteration, which can be impractical in applications where the data are not all available a priori, e.g., in online optimization. In this paper, we propose a novel one-point feedback scheme that queries the function value once at each iteration and estimates the gradient using the residual between two consecutive points. When optimizing a deterministic Lipschitz function, we show that the query complexity of ZO with the proposed one-point residual feedback matches that of ZO with the existing two-point schemes. Moreover, the query complexity of the proposed algorithm can be improved when the objective function has Lipschitz gradient. Then, for stochastic bandit optimization problems where only noisy objective function values are given, we show that ZO with one-point residual feedback achieves the same convergence rate as that of two-point scheme with uncontrollable data samples. We demonstrate the effectiveness of the proposed one-point residual feedback via extensive numerical experiments.
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
Authors: Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe
Date: 2020-06-18 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Portfolio Optimization

Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values. Recent work has shown that including the optimization problem as a layer in the model training pipeline results in predictions of the unobserved parameters that lead to higher decision quality. Unfortunately, this process comes at a large computational cost because the optimization problem must be solved and differentiated through in each training iteration; furthermore, it may also sometimes fail to improve solution quality due to non-smoothness issues that arise when training through a complex optimization layer. To address these shortcomings, we learn a low-dimensional surrogate model of a large optimization problem by representing the feasible space in terms of meta-variables, each of which is a linear combination of the original variables. By training a low-dimensional surrogate model end-to-end, and jointly with the predictive model, we achieve: i) a large reduction in training and inference time; and ii) improved performance by focusing attention on the more important variables in the optimization and learning in a smoother space. Empirically, we demonstrate these improvements on a non-convex adversary modeling task, a submodular recommendation task and a convex portfolio optimization task.
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
Authors: Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun
Date: 2020-06-18 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: reinforcement-learning, Reinforcement Learning (RL), Representation Learning

In order to deal with the curse of dimensionality in reinforcement learning (RL), it is common practice to make parametric assumptions where values or policies are functions of some low dimensional feature space. This work focuses on the representation learning question: how can we learn such features? Under the assumption that the underlying (unknown) dynamics correspond to a low rank transition matrix, we show how the representation learning question is related to a particular non-linear matrix decomposition problem. Structurally, we make precise connections between these low rank MDPs and latent variable models, showing how they significantly generalize prior formulations for representation learning in RL. Algorithmically, we develop FLAMBE, which engages in exploration and representation learning for provably efficient RL in low rank transition models.
Learning to infer in recurrent biological networks
Authors: Ari S. Benjamin, Konrad P. Kording
Date: 2020-06-18 00:00:00
Tasks: Bayesian Inference, Variational Inference

A popular theory of perceptual processing holds that the brain learns both a generative model of the world and a paired recognition model using variational Bayesian inference. Most hypotheses of how the brain might learn these models assume that neurons in a population are conditionally independent given their common inputs. This simplification is likely not compatible with the type of local recurrence observed in the brain. Seeking an alternative that is compatible with complex inter-dependencies yet consistent with known biology, we argue here that the cortex may learn with an adversarial algorithm. Many observable symptoms of this approach would resemble known neural phenomena, including wake/sleep cycles and oscillations that vary in magnitude with surprise, and we describe how further predictions could be tested. We illustrate the idea on recurrent neural networks trained to model image and video datasets. This framework for learning brings variational inference closer to neuroscience and yields multiple testable hypotheses.