pwc-archive/papers-with-abstracts huggingface dataset:
Analyzing the Role of Model Uncertainty for Electronic Health Records
Authors: Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine Heller, Andrew M. Dai
Date: 2019-06-10 00:00:00

In medicine, both ethical and monetary costs of incorrect predictions can be significant, and the complexity of the problems often necessitates increasingly complex models. Recent work has shown that changing just the random seed is enough for otherwise well-tuned deep neural networks to vary in their individual predicted probabilities. In light of this, we investigate the role of model uncertainty methods in the medical domain. Using RNN ensembles and various Bayesian RNNs, we show that population-level metrics, such as AUC-PR, AUC-ROC, log-likelihood, and calibration error, do not capture model uncertainty. Meanwhile, the presence of significant variability in patient-specific predictions and optimal decisions motivates the need for capturing model uncertainty. Understanding the uncertainty for individual patients is an area with clear clinical impact, such as determining when a model decision is likely to be brittle. We further show that RNNs with only Bayesian embeddings can be a more efficient way to capture model uncertainty compared to ensembles, and we analyze how model uncertainty is impacted across individual input features and patient subgroups.
Synthesizing 3D Shapes from Silhouette Image Collections using Multi-projection Generative Adversarial Networks
Authors: Xiao Li, Yue Dong, Pieter Peers, Xin Tong
Date: 2019-06-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Generative Adversarial Network, Weakly-supervised Learning

We present a new weakly supervised learning-based method for generating novel category-specific 3D shapes from unoccluded image collections. Our method is weakly supervised and only requires silhouette annotations from unoccluded, category-specific objects. Our method does not require access to the object's 3D shape, multiple observations per object from different views, intra-image pixel-correspondences, or any view annotations. Key to our method is a novel multi-projection generative adversarial network (MP-GAN) that trains a 3D shape generator to be consistent with multiple 2D projections of the 3D shapes, and without direct access to these 3D shapes. This is achieved through multiple discriminators that encode the distribution of 2D projections of the 3D shapes seen from a different views. Additionally, to determine the view information for each silhouette image, we also train a view prediction network on visualizations of 3D shapes synthesized by the generator. We iteratively alternate between training the generator and training the view prediction network. We validate our multi-projection GAN on both synthetic and real image datasets. Furthermore, we also show that multi-projection GANs can aid in learning other high-dimensional distributions from lower dimensional training datasets, such as material-class specific spatially varying reflectance properties from images.
SAR: Learning Cross-Language API Mappings with Little Knowledge
Authors: Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
Date: 2019-06-10 00:00:00
Tasks: Domain Adaptation, Translation

To save manual effort, developers often translate programs from one programming language to another, instead of implementing it from scratch. Translating application program interfaces (APIs) used in one language to functionally equivalent ones available in another language is an important aspect of program translation. Existing approaches facilitate the translation by automatically identifying the API mappings across programming languages. However, all these approaches still require large amount of manual effort in preparing parallel program corpora, ranging from pairs of APIs, to manually identified code in different languages that are considered as functionally equivalent. To minimize the manual effort in identifying parallel program corpora and API mappings, this paper aims at an automated approach to map APIs across languages with much less knowledge a priori needed than other existing approaches. The approach is based on an realization of the notion of domain adaption combined with code embedding, which can better align two vector spaces: taking as input large sets of programs, our approach first generates numeric vector representations of the programs, especially the APIs used in each language, and it adapts generative adversarial networks (GAN) to align the vectors from the spaces of two languages. For a better alignment, we initialize the GAN with parameters derived from optional API mapping seeds that can be identified accurately with a simple automatic signature-based matching heuristic. Then the cross-language API mappings can be identified via nearest-neighbors queries in the aligned vector spaces.
Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization
Authors: Navid Azizan, Sahin Lale, Babak Hassibi
Date: 2019-06-10 00:00:00

Most modern learning problems are highly overparameterized, meaning that there are many more parameters than the number of training data points, and as a result, the training loss may have infinitely many global minima (parameter vectors that perfectly interpolate the training data). Therefore, it is important to understand which interpolating solutions we converge to, how they depend on the initialization point and the learning algorithm, and whether they lead to different generalization performances. In this paper, we study these questions for the family of stochastic mirror descent (SMD) algorithms, of which the popular stochastic gradient descent (SGD) is a special case. Our contributions are both theoretical and experimental. On the theory side, we show that in the overparameterized nonlinear setting, if the initialization is close enough to the manifold of global minima (something that comes for free in the highly overparameterized case), SMD with sufficiently small step size converges to a global minimum that is approximately the closest one in Bregman divergence. On the experimental side, our extensive experiments on standard datasets and models, using various initializations, various mirror descents, and various Bregman divergences, consistently confirms that this phenomenon happens in deep learning. Our experiments further indicate that there is a clear difference in the generalization performance of the solutions obtained by different SMD algorithms. Experimenting on a standard image dataset and network architecture with SMD with different kinds of implicit regularization, $\ell_1$ to encourage sparsity, $\ell_2$ yielding SGD, and $\ell_{10}$ to discourage large components in the parameter vector, consistently and definitively shows that $\ell_{10}$-SMD has better generalization performance than SGD, which in turn has better generalization performance than $\ell_1$-SMD.
Network Implosion: Effective Model Compression for ResNets via Static Layer Pruning and Retraining
Authors: Yasutoshi Ida, Yasuhiro Fujiwara
Date: 2019-06-10 00:00:00
Tasks: Model Compression

Residual Networks with convolutional layers are widely used in the field of machine learning. Since they effectively extract features from input data by stacking multiple layers, they can achieve high accuracy in many applications. However, the stacking of many layers raises their computation costs. To address this problem, we propose Network Implosion, it erases multiple layers from Residual Networks without degrading accuracy. Our key idea is to introduce a priority term that identifies the importance of a layer; we can select unimportant layers according to the priority and erase them after the training. In addition, we retrain the networks to avoid critical drops in accuracy after layer erasure. A theoretical assessment reveals that our erasure and retraining scheme can erase layers without accuracy drop, and achieve higher accuracy than is possible with training from scratch. Our experiments show that Network Implosion can, for classification on Cifar-10/100 and ImageNet, reduce the number of layers by 24.00 to 42.86 percent without any drop in accuracy.
A Survey on Neural Machine Reading Comprehension
Authors: Boyu Qiu, Xu Chen, Jungang Xu, Yingfei Sun
Date: 2019-06-10 00:00:00
Tasks: Machine Reading Comprehension, Reading Comprehension, Survey

Enabling a machine to read and comprehend the natural language documents so that it can answer some questions remains an elusive challenge. In recent years, the popularity of deep learning and the establishment of large-scale datasets have both promoted the prosperity of Machine Reading Comprehension. This paper aims to present how to utilize the Neural Network to build a Reader and introduce some classic models, analyze what improvements they make. Further, we also point out the defects of existing models and future research directions
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
Authors: Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
Date: 2019-06-10 00:00:00
Tasks: BIG-bench Machine Learning, Translation

Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network models, layers composing the neural models are simultaneously trained using backpropagation. We argue that the isolated training scheme of ML pipelines is sub-optimal, since it cannot jointly optimize multiple components. To this end, we propose a framework that translates a pre-trained ML pipeline into a neural network and fine-tunes the ML models within the pipeline jointly using backpropagation. Our experiments show that fine-tuning of the translated pipelines is a promising technique able to increase the final accuracy.
Time-Series Anomaly Detection Service at Microsoft
Authors: Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang
Date: 2019-06-10 00:00:00
Tasks: Anomaly Detection, Saliency Detection, Time Series, Time Series Analysis, Time Series Anomaly Detection

Large companies need to monitor various metrics (for example, Page Views and Revenue) of their applications and services in real time. At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time. In this paper, we introduce the pipeline and algorithm of our anomaly detection service, which is designed to be accurate, efficient and general. The pipeline consists of three major modules, including data ingestion, experimentation platform and online compute. To tackle the problem of time-series anomaly detection, we propose a novel algorithm based on Spectral Residual (SR) and Convolutional Neural Network (CNN). Our work is the first attempt to borrow the SR model from visual saliency detection domain to time-series anomaly detection. Moreover, we innovatively combine SR and CNN together to improve the performance of SR model. Our approach achieves superior experimental results compared with state-of-the-art baselines on both public datasets and Microsoft production data.
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification
Authors: Minghao Hu, Yuxing Peng, Zhen Huang, Dongsheng Li, Yiwei Lv
Date: 2019-06-10 00:00:00
Proceeding: ACL 2019 7
Tasks: Aspect-Based Sentiment Analysis (ABSA), Aspect Term Extraction and Sentiment Classification, General Classification, Sentence, Sentiment Analysis

Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence. Prior work typically formulates this task as a sequence tagging problem. However, such formulation suffers from problems such as huge search space and sentiment inconsistency. To address these problems, we propose a span-based extract-then-classify framework, where multiple opinion targets are directly extracted from the sentence under the supervision of target span boundaries, and corresponding polarities are then classified using their span representations. We further investigate three approaches under this framework, namely the pipeline, joint, and collapsed models. Experiments on three benchmark datasets show that our approach consistently outperforms the sequence tagging baseline. Moreover, we find that the pipeline model achieves the best performance compared with the other two models.
Learning to Segment Skin Lesions from Noisy Annotations
Authors: Zahra Mirikharaji, Yiqi Yan, Ghassan Hamarneh
Date: 2019-06-10 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Meta-Learning, Segmentation, Semantic Segmentation

Deep convolutional neural networks have driven substantial advancements in the automatic understanding of images. Requiring a large collection of images and their associated annotations is one of the main bottlenecks limiting the adoption of deep networks. In the task of medical image segmentation, requiring pixel-level semantic annotations performed by human experts exacerbate this difficulty. This paper proposes a new framework to train a fully convolutional segmentation network from a large set of cheap unreliable annotations and a small set of expert-level clean annotations. We propose a spatially adaptive reweighting approach to treat clean and noisy pixel-level annotations commensurately in the loss function. We deploy a meta-learning approach to assign higher importance to pixels whose loss gradient direction is closer to those of clean data. Our experiments on training the network using segmentation ground truth corrupted with different levels of annotation noise show how spatial reweighting improves the robustness of deep networks to noisy annotations.
Learned Conjugate Gradient Descent Network for Massive MIMO Detection
Authors: Yi Wei, Ming-Min Zhao, Mingyi Hong, Min-Jian Zhao, Ming Lei
Date: 2019-06-10 00:00:00

In this work, we consider the use of model-driven deep learning techniques for massive multiple-input multiple-output (MIMO) detection. Compared with conventional MIMO systems, massive MIMO promises improved spectral efficiency, coverage and range. Unfortunately, these benefits are coming at the cost of significantly increased computational complexity. To reduce the complexity of signal detection and guarantee the performance, we present a learned conjugate gradient descent network (LcgNet), which is constructed by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes, we explicitly learn their universal values. Also, we can enhance the proposed network by augmenting the dimensions of these step-sizes. Furthermore, in order to reduce the memory costs, a novel quantized LcgNet is proposed, where a low-resolution nonuniform quantizer is integrated into the LcgNet to smartly quantize the aforementioned step-sizes. The quantizer is based on a specially designed soft staircase function with learnable parameters to adjust its shape. Meanwhile, due to fact that the number of learnable parameters is limited, the proposed networks are easy and fast to train. Numerical results demonstrate that the proposed network can achieve promising performance with much lower complexity.
Sampling Humans for Optimizing Preferences in Coloring Artwork
Authors: Michael McCourt, Ian Dewancker
Date: 2019-06-10 00:00:00
Tasks: Bayesian Optimization

Many circumstances of practical importance have performance or success metrics which exist implicitly---in the eye of the beholder, so to speak. Tuning aspects of such problems requires working without defined metrics and only considering pairwise comparisons or rankings. In this paper, we review an existing Bayesian optimization strategy for determining most-preferred outcomes, and identify an adaptation to allow it to handle ties. We then discuss some of the issues we have encountered when humans use this optimization strategy to optimize coloring a piece of abstract artwork. We hope that, by participating in this workshop, we can learn how other researchers encounter difficulties unique to working with humans in the loop.
A Closed-Form Learned Pooling for Deep Classification Networks
Authors: Vighnesh Birodkar, Hossein Mobahi, Dilip Krishnan, Samy Bengio
Date: 2019-06-10 00:00:00
Tasks: Classification, Form, Foveation, General Classification, image-classification, Image Classification

In modern computer vision tasks, convolutional neural networks (CNNs) are indispensable for image classification tasks due to their efficiency and effectiveness. Part of their superiority compared to other architectures, comes from the fact that a single, local filter is shared across the entire image. However, there are scenarios where we may need to treat spatial locations in non-uniform manner. We see this in nature when considering how humans have evolved foveation to process different areas in their field of vision with varying levels of detail. In this paper we propose a way to enable CNNs to learn different pooling weights for each pixel location. We do so by introducing an extended definition of a pooling operator. This operator can learn a strict super-set of what can be learned by average pooling or convolutions. It has the benefit of being shared across feature maps and can be encouraged to be local or diffuse depending on the data. We show that for fixed network weights, our pooling operator can be computed in closed-form by spectral decomposition of matrices associated with class separability. Through experiments, we show that this operator benefits generalization for ResNets and CNNs on the CIFAR-10, CIFAR-100 and SVHN datasets and improves robustness to geometric corruptions and perturbations on the CIFAR-10-C and CIFAR-10-P test sets.
Multiway clustering via tensor block models
Authors: Miaoyan Wang, Yuchen Zeng
Date: 2019-06-10 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Clustering

We consider the problem of identifying multiway block structure from a large noisy tensor. Such problems arise frequently in applications such as genomics, recommendation system, topic modeling, and sensor network localization. We propose a tensor block model, develop a unified least-square estimation, and obtain the theoretical accuracy guarantees for multiway clustering. The statistical convergence of the estimator is established, and we show that the associated clustering procedure achieves partition consistency. A sparse regularization is further developed for identifying important blocks with elevated means. The proposal handles a broad range of data types, including binary, continuous, and hybrid observations. Through simulation and application to two real datasets, we demonstrate the outperformance of our approach over previous methods.
Improving Neural Language Modeling via Adversarial Training
Authors: Dilin Wang, Chengyue Gong, Qiang Liu
Date: 2019-06-10 00:00:00
Tasks: Diversity, Language Modeling, Language Modelling, Machine Translation, Translation

Recently, substantial progress has been made in language modeling by using deep neural networks. However, in practice, large scale neural language models have been shown to be prone to overfitting. In this paper, we present a simple yet highly effective adversarial training mechanism for regularizing neural language models. The idea is to introduce adversarial noise to the output embedding layer while training the models. We show that the optimal adversarial noise yields a simple closed-form solution, thus allowing us to develop a simple and time efficient algorithm. Theoretically, we show that our adversarial mechanism effectively encourages the diversity of the embedding vectors, helping to increase the robustness of models. Empirically, we show that our method improves on the single model state-of-the-art results for language modeling on Penn Treebank (PTB) and Wikitext-2, achieving test perplexity scores of 46.01 and 38.07, respectively. When applied to machine translation, our method improves over various transformer-based translation baselines in BLEU scores on the WMT14 English-German and IWSLT14 German-English tasks.
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal
Authors: Alekh Agarwal, Sham Kakade, Lin F. Yang
Date: 2019-06-10 00:00:00
Tasks: model, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This work considers the sample and computational complexity of obtaining an $\epsilon$-optimal policy in a discounted Markov Decision Process (MDP), given only access to a generative model. In this work, we study the effectiveness of the most natural plug-in approach to model-based planning: we build the maximum likelihood estimate of the transition model in the MDP from observations and then find an optimal policy in this empirical MDP. We ask arguably the most basic and unresolved question in model based planning: is the naive "plug-in" approach, non-asymptotically, minimax optimal in the quality of the policy it finds, given a fixed sample size? Here, the non-asymptotic regime refers to when the sample size is sublinear in the model size. With access to a generative model, we resolve this question in the strongest possible sense: our main result shows that \emph{any} high accuracy solution in the plug-in model constructed with $N$ samples, provides an $\epsilon$-optimal policy in the true underlying MDP (where $\epsilon$ is the minimax accuracy with $N$ samples at every state, action pair). In comparison, all prior (non-asymptotically) minimax optimal results use model free approaches, such as the Variance Reduced Q-value iteration algorithm (Sidford et al 2018), while the best known model-based results (e.g. Azar et al 2013) require larger sample sizes in their dependence on the planning horizon or the state space. Notably, we show that the model-based approach allows the use of \emph{any} efficient planning algorithm in the empirical MDP, which simplifies algorithm design as this approach does not tie the algorithm to the sampling procedure. The core of our analysis is avnovel "absorbing MDP" construction to address the statistical dependency issues that arise in the analysis of model-based planning approaches, a construction which may be helpful more generally.
Fast Spatially-Varying Indoor Lighting Estimation
Authors: Mathieu Garon, Kalyan Sunkavalli, Sunil Hadap, Nathan Carr, Jean-François Lalonde
Date: 2019-06-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Lighting Estimation, Position

We propose a real-time method to estimate spatiallyvarying indoor lighting from a single RGB image. Given an image and a 2D location in that image, our CNN estimates a 5th order spherical harmonic representation of the lighting at the given location in less than 20ms on a laptop mobile graphics card. While existing approaches estimate a single, global lighting representation or require depth as input, our method reasons about local lighting without requiring any geometry information. We demonstrate, through quantitative experiments including a user study, that our results achieve lower lighting estimation errors and are preferred by users over the state-of-the-art. Our approach can be used directly for augmented reality applications, where a virtual object is relit realistically at any position in the scene in real-time.