pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian nonparametric discontinuity design
Authors: Max Hinne, David Leeftink, Marcel A. J. van Gerven, Luca Ambrogioni
Date: 2019-11-15 00:00:00
Tasks: Causal Inference, Experimental Design, regression, Time Series, Time Series Analysis

Quasi-experimental research designs, such as regression discontinuity and interrupted time series, allow for causal inference in the absence of a randomized controlled trial, at the cost of additional assumptions. In this paper, we provide a framework for discontinuity-based designs using Bayesian model comparison and Gaussian process regression, which we refer to as 'Bayesian nonparametric discontinuity design', or BNDD for short. BNDD addresses the two major shortcomings in most implementations of such designs: overconfidence due to implicit conditioning on the alleged effect, and model misspecification due to reliance on overly simplistic regression models. With the appropriate Gaussian process covariance function, our approach can detect discontinuities of any order, and in spectral features. We demonstrate the usage of BNDD in simulations, and apply the framework to determine the effect of running for political positions on longevity, of the effect of an alleged historical phantom border in the Netherlands on Dutch voting behaviour, and of Kundalini Yoga meditation on heart rate.
In-domain representation learning for remote sensing
Authors: Maxim Neumann, Andre Susano Pinto, Xiaohua Zhai, Neil Houlsby
Date: 2019-11-15 00:00:00
Tasks: Image Classification, Multi-Label Image Classification, Representation Learning, Scene Classification

Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community. To address it and to establish baselines and a common evaluation protocol in this domain, we provide simplified access to 5 diverse remote sensing datasets in a standardized form. Specifically, we investigate in-domain representation learning to develop generic remote sensing representations and explore which characteristics are important for a dataset to be a good source for remote sensing representation learning. The established baselines achieve state-of-the-art performance on these datasets.
A Generalized Markov Chain Model to Capture Dynamic Preferences and Choice Overload
Authors: Kumar Goutam, Vineet Goyal, Agathe Soret
Date: 2019-11-15 00:00:00
Tasks: Assortment Optimization

Assortment optimization is an important problem that arises in many industries such as retailing and online advertising where the goal is to find a subset of products from a universe of substitutable products which maximize seller's expected revenue. One of the key challenges in this problem is to model the customer substitution behavior. Many parametric random utility maximization (RUM) based choice models have been considered in the literature. However, in all these models, probability of purchase increases as we include more products to an assortment. This is not true in general and in many settings more choices hurt sales. This is commonly referred to as the choice overload. In this paper we attempt to address this limitation in RUM through a generalization of the Markov chain based choice model considered in Blanchet et al. (2016). As a special case, we show that our model reduces to a generalization of MNL with no-purchase attractions dependent on the assortment S and strictly increasing with the size of assortment S. While we show that the assortment optimization under this model is NP-hard, we present fully polynomial-time approximation scheme (FPTAS) under reasonable assumptions.
Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma
Authors: Ahmad Chaddad, Saima Rathore, Mingli Zhang, Christian Desrosiers, Tamim Niazi
Date: 2019-11-15 00:00:00
Tasks: General Classification

This paper proposes to use deep radiomic features (DRFs) from a convolutional neural network (CNN) to model fine-grained texture signatures in the radiomic analysis of recurrent glioblastoma (rGBM). We use DRFs to predict survival of rGBM patients with preoperative T1-weighted post-contrast MR images (n=100). DRFs are extracted from regions of interest labelled by a radiation oncologist and used to compare between short-term and long-term survival patient groups. Random forest (RF) classification is employed to predict survival outcome (i.e., short or long survival), as well as to identify highly group-informative descriptors. Classification using DRFs results in an area under the ROC curve (AUC) of 89.15% (p<0.01) in predicting rGBM patient survival, compared to 78.07% (p<0.01) when using standard radiomic features (SRF). These results indicate the potential of DRFs as a prognostic marker for patients with rGBM.
Fair Data Adaptation with Quantile Preservation
Authors: Drago Plečko, Nicolai Meinshausen
Date: 2019-11-15 00:00:00
Tasks: counterfactual, Fairness

Fairness of classification and regression has received much attention recently and various, partially non-compatible, criteria have been proposed. The fairness criteria can be enforced for a given classifier or, alternatively, the data can be adapated to ensure that every classifier trained on the data will adhere to desired fairness criteria. We present a practical data adaption method based on quantile preservation in causal structural equation models. The data adaptation is based on a presumed counterfactual model for the data. While the counterfactual model itself cannot be verified experimentally, we show that certain population notions of fairness are still guaranteed even if the counterfactual model is misspecified. The precise nature of the fulfilled non-causal fairness notion (such as demographic parity, separation or sufficiency) depends on the structure of the underlying causal model and the choice of resolving variables. We describe an implementation of the proposed data adaptation procedure based on Random Forests and demonstrate its practical use on simulated and real-world data.
Generative Models for Effective ML on Private, Decentralized Datasets
Authors: Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Aguera y Arcas
Date: 2019-11-15 00:00:00
Proceeding: ICLR 2020 1
Tasks: Federated Learning

To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact. Manual inspection of raw data - of representative samples, of outliers, of misclassifications - is an essential tool in a) identifying and fixing problems in the data, b) generating new modeling hypotheses, and c) assigning or refining human-provided labels. However, manual data inspection is problematic for privacy sensitive datasets, such as those representing the behavior of real-world individuals. Furthermore, manual data inspection is impossible in the increasingly important setting of federated learning, where raw examples are stored at the edge and the modeler may only access aggregated outputs such as metrics or model parameters. This paper demonstrates that generative models - trained using federated methods and with formal differential privacy guarantees - can be used effectively to debug many commonly occurring data issues even when the data cannot be directly inspected. We explore these methods in applications to text with differentially private federated RNNs and to images using a novel algorithm for differentially private federated GANs.
Bootstrapping NLU Models with Multi-task Learning
Authors: Shubham Kapoor, Caglar Tirkaz
Date: 2019-11-15 00:00:00
Tasks: General Classification, Multi-Task Learning, Natural Language Understanding, Word Embeddings

Bootstrapping natural language understanding (NLU) systems with minimal training data is a fundamental challenge of extending digital assistants like Alexa and Siri to a new language. A common approach that is adapted in digital assistants when responding to a user query is to process the input in a pipeline manner where the first task is to predict the domain, followed by the inference of intent and slots. However, this cascaded approach instigates error propagation and prevents information sharing among these tasks. Further, the use of words as the atomic units of meaning as done in many studies might lead to coverage problems for morphologically rich languages such as German and French when data is limited. We address these issues by introducing a character-level unified neural architecture for joint modeling of the domain, intent, and slot classification. We compose word-embeddings from characters and jointly optimize all classification tasks via multi-task learning. In our results, we show that the proposed architecture is an optimal choice for bootstrapping NLU systems in low-resource settings thus saving time, cost and human effort.
Enforcing Deterministic Constraints on Generative Adversarial Networks for Emulating Physical Systems
Authors: Zeng Yang, Jin-Long Wu, Heng Xiao
Date: 2019-11-15 00:00:00

Generative adversarial networks (GANs) were initially proposed to generate images by learning from a large number of samples. Recently, GANs have been used to emulate complex physical systems such as turbulent flows. However, a critical question must be answered before GANs can be considered trusted emulators for physical systems: do GANs-generated samples conform to the various physical constraints? These include both deterministic constraints (e.g., conservation laws) and statistical constraints (e.g., energy spectrum of turbulent flows). The latter have been studied in a companion paper (Wu et al., Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. Journal of Computational Physics. 406, 109209, 2020). In the present work, we enforce deterministic yet imprecise constraints on GANs by incorporating them into the loss function of the generator. We evaluate the performance of physics-constrained GANs on two representative tasks with geometrical constraints (generating points on circles) and differential constraints (generating divergence-free flow velocity fields), respectively. In both cases, the constrained GANs produced samples that conform to the underlying constraints rather accurately, even though the constraints are only enforced up to a specified interval. More importantly, the imposed constraints significantly accelerate the convergence and improve the robustness in the training, indicating that they serve as a physics-based regularization. These improvements are noteworthy, as the convergence and robustness are two well-known obstacles in the training of GANs.
CenterMask : Real-Time Anchor-Free Instance Segmentation
Authors: Youngwan Lee, Jongyoul Park
Date: 2019-11-15 00:00:00
Proceeding: arXiv 2019 11
Tasks: Instance Segmentation, Object Detection, Panoptic Segmentation, Real-time Instance Segmentation, Real-Time Object Detection, Segmentation, Semantic Segmentation, Semi-Supervised Instance Segmentation

We propose a simple yet efficient anchor-free instance segmentation, called CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch to anchor-free one stage object detector (FCOS) in the same vein with Mask R-CNN. Plugged into the FCOS object detector, the SAG-Mask branch predicts a segmentation mask on each box with the spatial attention map that helps to focus on informative pixels and suppress noise. We also present an improved backbone networks, VoVNetV2, with two effective strategies: (1) residual connection for alleviating the optimization problem of larger VoVNet \cite{lee2019energy} and (2) effective Squeeze-Excitation (eSE) dealing with the channel information loss problem of original SE. With SAG-Mask and VoVNetV2, we deign CenterMask and CenterMask-Lite that are targeted to large and small models, respectively. Using the same ResNet-101-FPN backbone, CenterMask achieves 38.3%, surpassing all previous state-of-the-art methods while at a much faster speed. CenterMask-Lite also outperforms the state-of-the-art by large margins at over 35fps on Titan Xp. We hope that CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance segmentation and backbone network for various vision tasks, respectively. The Code is available at https://github.com/youngwanLEE/CenterMask.
MMGAN: Generative Adversarial Networks for Multi-Modal Distributions
Authors: Teodora Pandeva, Matthias Schubert
Date: 2019-11-15 00:00:00
Tasks: Clustering, Image Generation

Over the past years, Generative Adversarial Networks (GANs) have shown a remarkable generation performance especially in image synthesis. Unfortunately, they are also known for having an unstable training process and might loose parts of the data distribution for heterogeneous input data. In this paper, we propose a novel GAN extension for multi-modal distribution learning (MMGAN). In our approach, we model the latent space as a Gaussian mixture model with a number of clusters referring to the number of disconnected data manifolds in the observation space, and include a clustering network, which relates each data manifold to one Gaussian cluster. Thus, the training gets more stable. Moreover, MMGAN allows for clustering real data according to the learned data manifold in the latent space. By a series of benchmark experiments, we illustrate that MMGAN outperforms competitive state-of-the-art models in terms of clustering performance.
A Policy Editor for Semantic Sensor Networks
Authors: Paolo Pareti, George Konstantinidis, Timothy J. Norman
Date: 2019-11-15 00:00:00

An important use of sensors and actuator networks is to comply with health and safety policies in hazardous environments. In order to deal with increasingly large and dynamic environments, and to quickly react to emergencies, tools are needed to simplify the process of translating high-level policies into executable queries and rules. We present a framework to produce such tools, which uses rules to aggregate low-level sensor data, described using the Semantic Sensor Network Ontology, into more useful and actionable abstractions. Using the schema of the underlying data sources as an input, we automatically generate abstractions which are relevant to the use case at hand. In this demonstration we present a policy editor tool and a simulation on which policies can be tested.
Imputing missing values with unsupervised random trees
Authors: David Cortes
Date: 2019-11-15 00:00:00
Tasks: Imputation, Missing Values, regression

This work proposes a non-iterative strategy for missing value imputations which is guided by similarity between observations, but instead of explicitly determining distances or nearest neighbors, it assigns observations to overlapping buckets through recursive semi-random hyperplane cuts, in which weighted averages are determined as imputations for each variable. The quality of these imputations is oftentimes not as good as that of chained equations, but the proposed technique is much faster, non-iterative, can make imputations on new data without re-calculating anything, and scales easily to large and high-dimensional datasets, providing a significant boost over simple mean/median imputation in regression and classification metrics with imputed values when other methods are not feasible.
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
Authors: Okan Köpüklü, Xiangyu Wei, Gerhard Rigoll
Date: 2019-11-15 00:00:00
Tasks: Actin Detection, Action Detection, Action Localization, Action Recognition In Videos

Spatiotemporal action localization requires the incorporation of two sources of information into the designed architecture: (1) temporal information from the previous frames and (2) spatial information from the key frame. Current state-of-the-art approaches usually extract these information with separate networks and use an extra mechanism for fusion to get detections. In this work, we present YOWO, a unified CNN architecture for real-time spatiotemporal action localization in video streams. YOWO is a single-stage architecture with two branches to extract temporal and spatial information concurrently and predict bounding boxes and action probabilities directly from video clips in one evaluation. Since the whole architecture is unified, it can be optimized end-to-end. The YOWO architecture is fast providing 34 frames-per-second on 16-frames input clips and 62 frames-per-second on 8-frames input clips, which is currently the fastest state-of-the-art architecture on spatiotemporal action localization task. Remarkably, YOWO outperforms the previous state-of-the art results on J-HMDB-21 and UCF101-24 with an impressive improvement of ~3% and ~12%, respectively. Moreover, YOWO is the first and only single-stage architecture that provides competitive results on AVA dataset. We make our code and pretrained models publicly available.
Forgetting to learn logic programs
Authors: Andrew Cropper
Date: 2019-11-15 00:00:00
Tasks: Inductive logic programming, Multi-Task Learning, Program induction

Most program induction approaches require predefined, often hand-engineered, background knowledge (BK). To overcome this limitation, we explore methods to automatically acquire BK through multi-task learning. In this approach, a learner adds learned programs to its BK so that they can be reused to help learn other programs. To improve learning performance, we explore the idea of forgetting, where a learner can additionally remove programs from its BK. We consider forgetting in an inductive logic programming (ILP) setting. We show that forgetting can significantly reduce both the size of the hypothesis space and the sample complexity of an ILP learner. We introduce Forgetgol, a multi-task ILP learner which supports forgetting. We experimentally compare Forgetgol against approaches that either remember or forget everything. Our experimental results show that Forgetgol outperforms the alternative approaches when learning from over 10,000 tasks.
CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation
Authors: Zhiyue Liu, Jiahai Wang, Zhiwei Liang
Date: 2019-11-15 00:00:00
Tasks: Diversity, Text Generation

Generating multiple categories of texts is a challenging task and draws more and more attention. Since generative adversarial nets (GANs) have shown competitive results on general text generation, they are extended for category text generation in some previous works. However, the complicated model structures and learning strategies limit their performance and exacerbate the training instability. This paper proposes a category-aware GAN (CatGAN) which consists of an efficient category-aware model for category text generation and a hierarchical evolutionary learning algorithm for training our model. The category-aware model directly measures the gap between real samples and generated samples on each category, then reducing this gap will guide the model to generate high-quality category samples. The Gumbel-Softmax relaxation further frees our model from complicated learning strategies for updating CatGAN on discrete data. Moreover, only focusing on the sample quality normally leads the mode collapse problem, thus a hierarchical evolutionary learning algorithm is introduced to stabilize the training procedure and obtain the trade-off between quality and diversity while training CatGAN. Experimental results demonstrate that CatGAN outperforms most of the existing state-of-the-art methods.
Catch & Carry: Reusable Neural Controllers for Vision-Guided Whole-Body Tasks
Authors: Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, Nicolas Heess
Date: 2019-11-15 00:00:00

We address the longstanding challenge of producing flexible, realistic humanoid character controllers that can perform diverse whole-body tasks involving object interactions. This challenge is central to a variety of fields, from graphics and animation to robotics and motor neuroscience. Our physics-based environment uses realistic actuation and first-person perception -- including touch sensors and egocentric vision -- with a view to producing active-sensing behaviors (e.g. gaze direction), transferability to real robots, and comparisons to the biology. We develop an integrated neural-network based approach consisting of a motor primitive module, human demonstrations, and an instructed reinforcement learning regime with curricula and task variations. We demonstrate the utility of our approach for several tasks, including goal-conditioned box carrying and ball catching, and we characterize its behavioral robustness. The resulting controllers can be deployed in real-time on a standard PC. See overview video, https://youtu.be/2rQAW-8gQQk .
Single Image Reflection Removal through Cascaded Refinement
Authors: Chao Li, Yixiao Yang, Kun He, Stephen Lin, John E. Hopcroft
Date: 2019-11-15 00:00:00
Proceeding: CVPR 2020 6
Tasks: Community Detection, Reflection Removal

We address the problem of removing undesirable reflections from a single image captured through a glass surface, which is an ill-posed, challenging but practically important problem for photo enhancement. Inspired by iterative structure reduction for hidden community detection in social networks, we propose an Iterative Boost Convolutional LSTM Network (IBCLN) that enables cascaded prediction for reflection removal. IBCLN is a cascaded network that iteratively refines the estimates of transmission and reflection layers in a manner that they can boost the prediction quality to each other, and information across steps of the cascade is transferred using an LSTM. The intuition is that the transmission is the strong, dominant structure while the reflection is the weak, hidden structure. They are complementary to each other in a single image and thus a better estimate and reduction on one side from the original image leads to a more accurate estimate on the other side. To facilitate training over multiple cascade steps, we employ LSTM to address the vanishing gradient problem, and propose residual reconstruction loss as further training guidance. Besides, we create a dataset of real-world images with reflection and ground-truth transmission layers to mitigate the problem of insufficient data. Comprehensive experiments demonstrate that the proposed method can effectively remove reflections in real and synthetic images compared with state-of-the-art reflection removal methods.
GraphX-Convolution for Point Cloud Deformation in 2D-to-3D Conversion
Authors: Anh-Duc Nguyen, Seonghwa Choi, Woojae Kim, Sang-Hoon Lee
Date: 2019-11-15 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Reconstruction, Object

In this paper, we present a novel deep method to reconstruct a point cloud of an object from a single still image. Prior arts in the field struggle to reconstruct an accurate and scalable 3D model due to either the inefficient and expensive 3D representations, the dependency between the output and number of model parameters or the lack of a suitable computing operation. We propose to overcome these by deforming a random point cloud to the object shape through two steps: feature blending and deformation. In the first step, the global and point-specific shape features extracted from a 2D object image are blended with the encoded feature of a randomly generated point cloud, and then this mixture is sent to the deformation step to produce the final representative point set of the object. In the deformation process, we introduce a new layer termed as GraphX that considers the inter-relationship between points like common graph convolutions but operates on unordered sets. Moreover, with a simple trick, the proposed model can generate an arbitrary-sized point cloud, which is the first deep method to do so. Extensive experiments verify that we outperform existing models and halve the state-of-the-art distance score in single image 3D reconstruction.