pwc-archive/papers-with-abstracts huggingface dataset:
Online Active Learning of Reject Option Classifiers
Authors: Kulin Shah, Naresh Manwani
Date: 2019-06-14 00:00:00
Tasks: Active Learning, Binary Classification, General Classification

Active learning is an important technique to reduce the number of labeled examples in supervised learning. Active learning for binary classification has been well addressed in machine learning. However, active learning of the reject option classifier remains unaddressed. In this paper, we propose novel algorithms for active learning of reject option classifiers. We develop an active learning algorithm using double ramp loss function. We provide mistake bounds for this algorithm. We also propose a new loss function called double sigmoid loss function for reject option and corresponding active learning algorithm. We offer a convergence guarantee for this algorithm. We provide extensive experimental results to show the effectiveness of the proposed algorithms. The proposed algorithms efficiently reduce the number of label examples required.
A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
Authors: Robin Brügger, Christian F. Baumgartner, Ender Konukoglu
Date: 2019-06-14 00:00:00
Tasks: image-classification, Image Classification, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

One of the key drawbacks of 3D convolutional neural networks for segmentation is their memory footprint, which necessitates compromises in the network architecture in order to fit into a given memory budget. Motivated by the RevNet for image classification, we propose a partially reversible U-Net architecture that reduces memory consumption substantially. The reversible architecture allows us to exactly recover each layer's outputs from the subsequent layer's ones, eliminating the need to store activations for backpropagation. This alleviates the biggest memory bottleneck and enables very deep (theoretically infinitely deep) 3D architectures. On the BraTS challenge dataset, we demonstrate substantial memory savings. We further show that the freed memory can be used for processing the whole field-of-view (FOV) instead of patches. Increasing network depth led to higher segmentation accuracy while growing the memory footprint only by a very small fraction, thanks to the partially reversible architecture.
Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders
Authors: Taichi Sumi, Brian Kenji Iwana, Hideaki Hayashi, Seiichi Uchida
Date: 2019-06-14 00:00:00

This research attempts to construct a network that can convert online and offline handwritten characters to each other. The proposed network consists of two Variational Auto-Encoders (VAEs) with a shared latent space. The VAEs are trained to generate online and offline handwritten Latin characters simultaneously. In this way, we create a cross-modal VAE (Cross-VAE). During training, the proposed Cross-VAE is trained to minimize the reconstruction loss of the two modalities, the distribution loss of the two VAEs, and a novel third loss called the space sharing loss. This third, space sharing loss is used to encourage the modalities to share the same latent space by calculating the distance between the latent variables. Through the proposed method mutual conversion of online and offline handwritten characters is possible. In this paper, we demonstrate the performance of the Cross-VAE through qualitative and quantitative analysis.
Anomaly Detection with HMM Gauge Likelihood Analysis
Authors: Boris Lorbeer, Tanja Deutsch, Peter Ruppel, Axel Küpper
Date: 2019-06-14 00:00:00
Tasks: Anomaly Detection, Clustering, Outlier Detection, Time Series, Time Series Analysis

This paper describes a new method, HMM gauge likelihood analysis, or GLA, of detecting anomalies in discrete time series using Hidden Markov Models and clustering. At the center of the method lies the comparison of subsequences. To achieve this, they first get assigned to their Hidden Markov Models using the Baum-Welch algorithm. Next, those models are described by an approximating representation of the probability distributions they define. Finally, this representation is then analyzed with the help of some clustering technique or other outlier detection tool and anomalies are detected. Clearly, HMMs could be substituted by some other appropriate model, e.g. some other dynamic Bayesian network. Our learning algorithm is unsupervised, so it does not require the labeling of large amounts of data. The usability of this method is demonstrated by applying it to synthetic and real-world syslog data.
DocRED: A Large-Scale Document-Level Relation Extraction Dataset
Authors: Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zheng-Hao Liu, Zhiyuan Liu, Lixin Huang, Jie zhou, Maosong Sun
Date: 2019-06-14 00:00:00
Proceeding: ACL 2019 7
Tasks: Document-level Relation Extraction, Relation, Relation Extraction, Sentence

Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: (1) DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text; (2) DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document; (3) along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios. In order to verify the challenges of document-level RE, we implement recent state-of-the-art methods for RE and conduct a thorough evaluation of these methods on DocRED. Empirical results show that DocRED is challenging for existing RE methods, which indicates that document-level RE remains an open problem and requires further efforts. Based on the detailed analysis on the experiments, we discuss multiple promising directions for future research.
GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis
Authors: Changhee Han, Leonardo Rundo, Kohei Murao, Zoltán Ádám Milacski, Kazuki Umemoto, Evis Sala, Hideki Nakayama, Shin'ichi Satoh
Date: 2019-06-14 00:00:00
Tasks: Anomaly Detection, Generative Adversarial Network, Unsupervised Anomaly Detection

Unsupervised learning can discover various unseen diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with disease stages. Therefore, we propose a two-step method using Generative Adversarial Network-based multiple adjacent brain MRI slice reconstruction to detect AD at various stages: (Reconstruction) Wasserstein loss with Gradient Penalty + L1 loss---trained on 3 healthy slices to reconstruct the next 3 ones---reconstructs unseen healthy/AD cases; (Diagnosis) Average/Maximum loss (e.g., L2 loss) per scan discriminates them, comparing the reconstructed/ground truth images. The results show that we can reliably detect AD at a very early stage with Area Under the Curve (AUC) 0.780 while also detecting AD at a late stage much more accurately with AUC 0.917; since our method is fully unsupervised, it should also discover and alert any anomalies including rare disease.
A Survey on Deep Learning Architectures for Image-based Depth Reconstruction
Authors: Hamid Laga
Date: 2019-06-14 00:00:00
Tasks: Deep Learning, Survey

Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. In this article, we provide a comprehensive survey of the recent developments in this field. We will focus on the works which use deep learning techniques to estimate depth from one or multiple images. Deep learning, coupled with the availability of large training datasets, have revolutionized the way the depth reconstruction problem is being approached by the research community. In this article, we survey more than 100 key contributions that appeared in the past five years, summarize the most commonly used pipelines, and discuss their benefits and limitations. In retrospect of what has been achieved so far, we also conjecture what the future may hold for learning-based depth reconstruction research.
Towards Compact and Robust Deep Neural Networks
Authors: Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana
Date: 2019-06-14 00:00:00
Tasks: Adversarial Robustness, Network Pruning

Deep neural networks have achieved impressive performance in many applications but their large number of parameters lead to significant computational and storage overheads. Several recent works attempt to mitigate these overheads by designing compact networks using pruning of connections. However, we observe that most of the existing strategies to design compact networks fail to preserve network robustness against adversarial examples. In this work, we rigorously study the extension of network pruning strategies to preserve both benign accuracy and robustness of a network. Starting with a formal definition of the pruning procedure, including pre-training, weights pruning, and fine-tuning, we propose a new pruning method that can create compact networks while preserving both benign accuracy and robustness. Our method is based on two main insights: (1) we ensure that the training objectives of the pre-training and fine-tuning steps match the training objective of the desired robust model (e.g., adversarial robustness/verifiable robustness), and (2) we keep the pruning strategy agnostic to pre-training and fine-tuning objectives. We evaluate our method on four different networks on the CIFAR-10 dataset and measure benign accuracy, empirical robust accuracy, and verifiable robust accuracy. We demonstrate that our pruning method can preserve on average 93\% benign accuracy, 92.5\% empirical robust accuracy, and 85.0\% verifiable robust accuracy while compressing the tested network by 10$\times$.
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization
Authors: Alexander Mey, Tom Viering, Marco Loog
Date: 2019-06-14 00:00:00
Tasks: General Classification

Manifold regularization is a commonly used technique in semi-supervised learning. It enforces the classification rule to be smooth with respect to the data-manifold. Here, we derive sample complexity bounds based on pseudo-dimension for models that add a convex data dependent regularization term to a supervised learning process, as is in particular done in Manifold regularization. We then compare the bound for those semi-supervised methods to purely supervised methods, and discuss a setting in which the semi-supervised method can only have a constant improvement, ignoring logarithmic terms. By viewing Manifold regularization as a kernel method we then derive Rademacher bounds which allow for a distribution dependent analysis. Finally we illustrate that these bounds may be useful for choosing an appropriate manifold regularization parameter in situations with very sparsely labeled data.
$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
Authors: Maxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin, Denis Derkach, Fedor Ratnikov, Olga Mineeva
Date: 2019-06-14 00:00:00
Tasks: Anomaly Detection, General Classification

Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples. The method is evaluated using several data sets and compared to a set of conventional one-class and two-class approaches.
Binary Classification using Pairs of Minimum Spanning Trees or N-ary Trees
Authors: Riccardo La Grassa, Ignazio Gallo, Alessandro Calefati, Dimitri Ognibene
Date: 2019-06-14 00:00:00
Tasks: Binary Classification, Classification, General Classification

One-class classifiers are trained with target class only samples. Intuitively, their conservative modelling of the class description may benefit classical classification tasks where classes are difficult to separate due to overlapping and data imbalance. In this work, three methods are proposed which leverage on the combination of one-class classifiers based on non-parametric models, N-ary Trees and Minimum Spanning Trees class descriptors (MST-CD), to tackle binary classification problems. The methods deal with the inconsistencies arising from combining multiple classifiers and with spurious connections that MST-CD creates in multi-modal class distributions. As shown by our tests on several datasets, the proposed approach is feasible and comparable with state-of-the-art algorithms.
Copy and Paste: A Simple But Effective Initialization Method for Black-Box Adversarial Attacks
Authors: Thomas Brunner, Frederik Diehl, Alois Knoll
Date: 2019-06-14 00:00:00
Tasks: valid

Many optimization methods for generating black-box adversarial examples have been proposed, but the aspect of initializing said optimizers has not been considered in much detail. We show that the choice of starting points is indeed crucial, and that the performance of state-of-the-art attacks depends on it. First, we discuss desirable properties of starting points for attacking image classifiers, and how they can be chosen to increase query efficiency. Notably, we find that simply copying small patches from other images is a valid strategy. We then present an evaluation on ImageNet that clearly demonstrates the effectiveness of this method: Our initialization scheme reduces the number of queries required for a state-of-the-art Boundary Attack by 81%, significantly outperforming previous results reported for targeted black-box adversarial examples.
Identify treatment effect patterns for personalised decisions
Authors: Jiuyong Li, Lin Liu, Shisheng Zhang, Saisai Ma, Thuc Duy Le, Jixue Liu
Date: 2019-06-14 00:00:00
Tasks: Decision Making

In personalised decision making, evidence is required to determine whether an action (treatment) is suitable for an individual. Such evidence can be obtained by modelling treatment effect heterogeneity in subgroups. The existing interpretable modelling methods take a top-down approach to search for subgroups with heterogeneous treatment effects and they may miss the most specific and relevant context for an individual. In this paper, we design a \emph{Treatment effect pattern (TEP)} to represent treatment effect heterogeneity in data. To achieve an interpretable presentation of TEPs, we use a local causal structure around the outcome to explicitly show how those important variables are used in modelling. We also derive a formula for unbiasedly estimating the \emph{Conditional Average Causal Effect (CATE)} using the local structure in our problem setting. In the discovery process, we aim at minimising heterogeneity within each subgroup represented by a pattern. We propose a bottom-up search algorithm to discover the most specific patterns fitting individual circumstances the best for personalised decision making. Experiments show that the proposed method models treatment effect heterogeneity better than three other existing tree based methods in synthetic and real world data sets.
Direct Image to Point Cloud Descriptors Matching for 6-DOF Camera Localization in Dense 3D Point Cloud
Authors: Uzair Nadeem, Mohammad A. A. K. Jalwana, Mohammed Bennamoun, Roberto Togneri, Ferdous Sohel
Date: 2019-06-14 00:00:00
Tasks: Camera Localization, Pose Estimation

We propose a novel concept to directly match feature descriptors extracted from RGB images, with feature descriptors extracted from 3D point clouds. We use this concept to localize the position and orientation (pose) of the camera of a query image in dense point clouds. We generate a dataset of matching 2D and 3D descriptors, and use it to train a proposed Descriptor-Matcher algorithm. To localize a query image in a point cloud, we extract 2D keypoints and descriptors from the query image. Then the Descriptor-Matcher is used to find the corresponding pairs 2D and 3D keypoints by matching the 2D descriptors with the pre-extracted 3D descriptors of the point cloud. This information is used in a robust pose estimation algorithm to localize the query image in the 3D point cloud. Experiments demonstrate that directly matching 2D and 3D descriptors is not only a viable idea but also achieves competitive accuracy compared to other state-of-the-art approaches for camera pose localization.
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
Authors: Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow
Date: 2019-06-14 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Heuristic Search, Reinforcement Learning

Direct optimization is an appealing framework that replaces integration with optimization of a random objective for approximating gradients in models with discrete random variables. A$^\star$ sampling is a framework for optimizing such random objectives over large spaces. We show how to combine these techniques to yield a reinforcement learning algorithm that approximates a policy gradient by finding trajectories that optimize a random objective. We call the resulting algorithms "direct policy gradient" (DirPG) algorithms. A main benefit of DirPG algorithms is that they allow the insertion of domain knowledge in the form of upper bounds on return-to-go at training time, like is used in heuristic search, while still directly computing a policy gradient. We further analyze their properties, showing there are cases where DirPG has an exponentially larger probability of sampling informative gradients compared to REINFORCE. We also show that there is a built-in variance reduction technique and that a parameter that was previously viewed as a numerical approximation can be interpreted as controlling risk sensitivity. Empirically, we evaluate the effect of key degrees of freedom and show that the algorithm performs well in illustrative domains compared to baselines.
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation
Authors: Lorenzo Bertoni, Sven Kreiss, Alexandre Alahi
Date: 2019-06-14 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Depth Estimation, 3D Object Detection, Self-Driving Cars

We tackle the fundamentally ill-posed problem of 3D human localization from monocular RGB images. Driven by the limitation of neural networks outputting point estimates, we address the ambiguity in the task by predicting confidence intervals through a loss function based on the Laplace distribution. Our architecture is a light-weight feed-forward neural network that predicts 3D locations and corresponding confidence intervals given 2D human poses. The design is particularly well suited for small training data, cross-dataset generalization, and real-time applications. Our experiments show that we (i) outperform state-of-the-art results on KITTI and nuScenes datasets, (ii) even outperform a stereo-based method for far-away pedestrians, and (iii) estimate meaningful confidence intervals. We further share insights on our model of uncertainty in cases of limited observations and out-of-distribution samples.
Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification
Authors: Christoph Haarburger, Michael Baumgartner, Daniel Truhn, Mirjam Broeckmann, Hannah Schneider, Simone Schrading, Christiane Kuhl, Dorit Merhof
Date: 2019-06-14 00:00:00
Tasks: General Classification, Object, object-detection, Object Detection

Classification of malignancy for breast cancer and other cancer types is usually tackled as an object detection problem: Individual lesions are first localized and then classified with respect to malignancy. However, the drawback of this approach is that abstract features incorporating several lesions and areas that are not labelled as a lesion but contain global medically relevant information are thus disregarded: especially for dynamic contrast-enhanced breast MRI, criteria such as background parenchymal enhancement and location within the breast are important for diagnosis and cannot be captured by object detection approaches properly. In this work, we propose a 3D CNN and a multi scale curriculum learning strategy to classify malignancy globally based on an MRI of the whole breast. Thus, the global context of the whole breast rather than individual lesions is taken into account. Our proposed approach does not rely on lesion segmentations, which renders the annotation of training data much more effective than in current object detection approaches. Achieving an AUROC of 0.89, we compare the performance of our approach to Mask R-CNN and Retina U-Net as well as a radiologist. Our performance is on par with approaches that, in contrast to our method, rely on pixelwise segmentations of lesions.
Learning Mixtures of Graphs from Epidemic Cascades
Authors: Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis
Date: 2019-06-14 00:00:00
Proceeding: ICML 2020 1

We consider the problem of learning the weighted edges of a balanced mixture of two undirected graphs from epidemic cascades. While mixture models are popular modeling tools, algorithmic development with rigorous guarantees has lagged. Graph mixtures are apparently no exception: until now, very little is known about whether this problem is solvable. To the best of our knowledge, we establish the first necessary and sufficient conditions for this problem to be solvable in polynomial time on edge-separated graphs. When the conditions are met, i.e., when the graphs are connected with at least three edges, we give an efficient algorithm for learning the weights of both graphs with optimal sample complexity (up to log factors). We give complimentary results and provide sample-optimal (up to log factors) algorithms for mixtures of directed graphs of out-degree at least three, for mixture of undirected graphs of unbalanced and/or unknown priors.
Microsoft AI Challenge India 2018: Learning to Rank Passages for Web Question Answering with Deep Attention Networks
Authors: Chaitanya Sai Alaparthi
Date: 2019-06-14 00:00:00
Tasks: Deep Attention, Learning-To-Rank, Question Answering

This paper describes our system for The Microsoft AI Challenge India 2018: Ranking Passages for Web Question Answering. The system uses the biLSTM network with co-attention mechanism between query and passage representations. Additionally, we use self attention on embeddings to increase the lexical coverage by allowing the system to take union over different embeddings. We also incorporate hand-crafted features to improve the system performance. Our system achieved a Mean Reciprocal Rank (MRR) of 0.67 on eval-1 dataset.