pwc-archive/papers-with-abstracts huggingface dataset:
SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble
Authors: Jiaming Shen, Zeqiu Wu, Dongming Lei, Jingbo Shang, Xiang Ren, Jiawei Han
Date: 2019-10-17 00:00:00
Tasks: feature selection, Question Answering

Corpus-based set expansion (i.e., finding the "complete" set of entities belonging to the same semantic class, based on a given corpus and a tiny set of seeds) is a critical task in knowledge discovery. It may facilitate numerous downstream applications, such as information extraction, taxonomy induction, question answering, and web search. To discover new entities in an expanded set, previous approaches either make one-time entity ranking based on distributional similarity, or resort to iterative pattern-based bootstrapping. The core challenge for these methods is how to deal with noisy context features derived from free-text corpora, which may lead to entity intrusion and semantic drifting. In this study, we propose a novel framework, SetExpan, which tackles this problem, with two techniques: (1) a context feature selection method that selects clean context features for calculating entity-entity distributional similarity, and (2) a ranking-based unsupervised ensemble method for expanding entity set based on denoised context features. Experiments on three datasets show that SetExpan is robust and outperforms previous state-of-the-art methods in terms of mean average precision.
Online Learning in Planar Pushing with Combined Prediction Model
Authors: Huidong Gao, Yi Ouyang, Masayoshi Tomizuka
Date: 2019-10-17 00:00:00
Tasks: Prediction, Trajectory Planning

Pushing is a useful robotic capability for positioning and reorienting objects. The ability to accurately predict the effect of pushes can enable efficient trajectory planning and complicated object manipulation. Physical prediction models for planar pushing have long been established, but their assumptions and requirements usually don't hold in most practical settings. Data-driven approaches can provide accurate predictions for offline data, but they often have generalizability issues. In this paper, we propose a combined prediction model and an online learning framework for planar push prediction. The combined model consists of a neural network module and analytical components with a low-dimensional parameter. We train the neural network offline using pre-collected pushing data. In online situations, the low-dimensional analytical parameter is learned directly from online pushes to quickly adapt to the new environments. We test our combined model and learning framework on real pushing experiments. Our experimental results show that our model is able to quickly adapt to new environments while achieving similar final prediction performance as that of pure neural network models.
Deep Weakly-Supervised Domain Adaptation for Pain Localization in Videos
Authors: R. Gnana Praveen, Eric Granger, Patrick Cardinal
Date: 2019-10-17 00:00:00
Tasks: Diagnostic, Domain Adaptation, Multiple Instance Learning

Automatic pain assessment has an important potential diagnostic value for populations that are incapable of articulating their pain experiences. As one of the dominating nonverbal channels for eliciting pain expression events, facial expressions has been widely investigated for estimating the pain intensity of individual. However, using state-of-the-art deep learning (DL) models in real-world pain estimation applications poses several challenges related to the subjective variations of facial expressions, operational capture conditions, and lack of representative training videos with labels. Given the cost of annotating intensity levels for every video frame, we propose a weakly-supervised domain adaptation (WSDA) technique that allows for training 3D CNNs for spatio-temporal pain intensity estimation using weakly labeled videos, where labels are provided on a periodic basis. In particular, WSDA integrates multiple instance learning into an adversarial deep domain adaptation framework to train an Inflated 3D-CNN (I3D) model such that it can accurately estimate pain intensities in the target operational domain. The training process relies on weak target loss, along with domain loss and source loss for domain adaptation of the I3D model. Experimental results obtained using labeled source domain RECOLA videos and weakly-labeled target domain UNBC-McMaster videos indicate that the proposed deep WSDA approach can achieve significantly higher level of sequence (bag)-level and frame (instance)-level pain localization accuracy than related state-of-the-art approaches.
Deep Sub-Ensembles for Fast Uncertainty Estimation in Image Classification
Authors: Matias Valdenegro-Toro
Date: 2019-10-17 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Fast estimates of model uncertainty are required for many robust robotics applications. Deep Ensembles provides state of the art uncertainty without requiring Bayesian methods, but still it is computationally expensive. In this paper we propose deep sub-ensembles, an approximation to deep ensembles where the core idea is to ensemble only the layers close to the output, and not the whole model. With ResNet-20 on the CIFAR10 dataset, we obtain 1.5-2.5 speedup over a Deep Ensemble, with a small increase in error and NLL, and similarly up to 5-15 speedup with a VGG-like network on the SVHN dataset. Our results show that this idea enables a trade-off between error and uncertainty quality versus computational performance.
Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces
Authors: Sean R. Sinclair, Siddhartha Banerjee, Christina Lee Yu
Date: 2019-10-17 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present an efficient algorithm for model-free episodic reinforcement learning on large (potentially continuous) state-action spaces. Our algorithm is based on a novel $Q$-learning policy with adaptive data-driven discretization. The central idea is to maintain a finer partition of the state-action space in regions which are frequently visited in historical trajectories, and have higher payoff estimates. We demonstrate how our adaptive partitions take advantage of the shape of the optimal $Q$-function and the joint space, without sacrificing the worst-case performance. In particular, we recover the regret guarantees of prior algorithms for continuous state-action spaces, which additionally require either an optimal discretization as input, and/or access to a simulation oracle. Moreover, experiments demonstrate how our algorithm automatically adapts to the underlying structure of the problem, resulting in much better performance compared both to heuristics and $Q$-learning with uniform discretization.
Multi Label Restricted Boltzmann Machine for Non-Intrusive Load Monitoring
Authors: Sagar Verma, Shikha Singh, Angshul Majumdar
Date: 2019-10-17 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Non-Intrusive Load Monitoring

Increasing population indicates that energy demands need to be managed in the residential sector. Prior studies have reflected that the customers tend to reduce a significant amount of energy consumption if they are provided with appliance-level feedback. This observation has increased the relevance of load monitoring in today's tech-savvy world. Most of the previously proposed solutions claim to perform load monitoring without intrusion, but they are not completely non-intrusive. These methods require historical appliance-level data for training the model for each of the devices. This data is gathered by putting a sensor on each of the appliances present in the home which causes intrusion in the building. Some recent studies have proposed that if we frame Non-Intrusive Load Monitoring (NILM) as a multi-label classification problem, the need for appliance-level data can be avoided. In this paper, we propose Multi-label Restricted Boltzmann Machine(ML-RBM) for NILM and report an experimental evaluation of proposed and state-of-the-art techniques.
Proposing a Model for Predicting Passenger Origin-Destination in Online Taxi-Hailing Systems
Authors: Pouria Golshanrad, Hamid Mahini, Behnam Bahrak
Date: 2019-10-17 00:00:00
Tasks: Clustering, Management

Due to the significance of transportation planning, traffic management, and dispatch optimization, predicting passenger origin-destination has emerged as a crucial requirement for intelligent transportation systems management. In this study, we present a model designed to forecast the origin and destination of travels within a specified time window. To derive meaningful travel flows, we employ K-means clustering in a four-dimensional space with a maximum cluster size constraint for origin and destination zones. Given the large number of clusters, we utilize non-negative matrix factorization to reduce the number of travel clusters. Furthermore, we implement a stacked recurrent neural network model to predict the travel count in each cluster. A comparison of our results with existing models reveals that our proposed model achieves a 5-7\% lower mean absolute percentage error (MAPE) for 1-hour time windows and a 14\% lower MAPE for 30-minute time windows.
Explainable Authorship Verification in Social Media via Attention-based Similarity Learning
Authors: Benedikt Boenninghoff, Steffen Hessler, Dorothea Kolossa, Robert M. Nickel
Date: 2019-10-17 00:00:00
Tasks: Authorship Verification, Decision Making

Authorship verification is the task of analyzing the linguistic patterns of two or more texts to determine whether they were written by the same author or not. The analysis is traditionally performed by experts who consider linguistic features, which include spelling mistakes, grammatical inconsistencies, and stylistics for example. Machine learning algorithms, on the other hand, can be trained to accomplish the same, but have traditionally relied on so-called stylometric features. The disadvantage of such features is that their reliability is greatly diminished for short and topically varied social media texts. In this interdisciplinary work, we propose a substantial extension of a recently published hierarchical Siamese neural network approach, with which it is feasible to learn neural features and to visualize the decision-making process. For this purpose, a new large-scale corpus of short Amazon reviews for text comparison research is compiled and we show that the Siamese network topologies outperform state-of-the-art approaches that were built up on stylometric features. Our linguistic analysis of the internal attention weights of the network shows that the proposed method is indeed able to latch on to some traditional linguistic categories.
Zero-shot Policy Learning with Spatial Temporal RewardDecomposition on Contingency-aware Observation
Authors: Huazhe Xu, Boyuan Chen, Yang Gao, Trevor Darrell
Date: 2019-10-17 00:00:00
Tasks: continuous-control, Continuous Control, Model Predictive Control, Zero-shot Generalization, Zero-Shot Learning

It is a long-standing challenge to enable an intelligent agent to learn in one environment and generalize to an unseen environment without further data collection and finetuning. In this paper, we consider a zero shot generalization problem setup that complies with biological intelligent agents' learning and generalization processes. The agent is first presented with previous experiences in the training environment, along with task description in the form of trajectory-level sparse rewards. Later when it is placed in the new testing environment, it is asked to perform the task without any interaction with the testing environment. We find this setting natural for biological creatures and at the same time, challenging for previous methods. Behavior cloning, state-of-art RL along with other zero-shot learning methods perform poorly on this benchmark. Given a set of experiences in the training environment, our method learns a neural function that decomposes the sparse reward into particular regions in a contingency-aware observation as a per step reward. Based on such decomposed rewards, we further learn a dynamics model and use Model Predictive Control (MPC) to obtain a policy. Since the rewards are decomposed to finer-granularity observations, they are naturally generalizable to new environments that are composed of similar basic elements. We demonstrate our method on a wide range of environments, including a classic video game -- Super Mario Bros, as well as a robotic continuous control task. Please refer to the project page for more visualized results.
RPBA -- Robust Parallel Bundle Adjustment Based on Covariance Information
Authors: Helmut Mayer
Date: 2019-10-17 00:00:00

A core component of all Structure from Motion (SfM) approaches is bundle adjustment. As the latter is a computational bottleneck for larger blocks, parallel bundle adjustment has become an active area of research. Particularly, consensus-based optimization methods have been shown to be suitable for this task. We have extended them using covariance information derived by the adjustment of individual three-dimensional (3D) points, i.e., "triangulation" or "intersection". This does not only lead to a much better convergence behavior, but also avoids fiddling with the penalty parameter of standard consensus-based approaches. The corresponding novel approach can also be seen as a variant of resection / intersection schemes, where we adjust during intersection a number of sub-blocks directly related to the number of threads available on a computer each containing a fraction of the cameras of the block. We show that our novel approach is suitable for robust parallel bundle adjustment and demonstrate its capabilities in comparison to the basic consensus-based approach as well as a state-of-the-art parallel implementation of bundle adjustment. Code for our novel approach is available on GitHub: https://github.com/helmayer/RPBA
Planning for Goal-Oriented Dialogue Systems
Authors: Christian Muise, Tathagata Chakraborti, Shubham Agarwal, Ondrej Bajgar, Arunima Chaudhary, Luis A. Lastras-Montano, Josef Ondrej, Miroslav Vodolan, Charlie Wiecha
Date: 2019-10-17 00:00:00
Tasks: Goal-Oriented Dialogue Systems, slot-filling, Slot Filling

Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft. This is in large part due to the rapidly growing market demand for dialogue agents capable of goal-oriented behaviour. Due to the business process nature of these conversations, end-to-end machine learning systems are generally not a viable option, as the generated dialogue agents must be deployable and verifiable on behalf of the businesses authoring them. In this work, we propose a paradigm shift in the creation of goal-oriented complex dialogue systems that dramatically eliminates the need for a designer to manually specify a dialogue tree, which nearly all current systems have to resort to when the interaction pattern falls outside standard patterns such as slot filling. We propose a declarative representation of the dialogue agent to be processed by state-of-the-art planning technology. Our proposed approach covers all aspects of the process; from model solicitation to the execution of the generated plans/dialogue agents. Along the way, we introduce novel planning encodings for declarative dialogue synthesis, a variety of interfaces for working with the specification as a dialogue architect, and a robust executor for generalized contingent plans. We have created prototype implementations of all components, and in this paper, we further demonstrate the resulting system empirically.
A Dataset of Multi-Illumination Images in the Wild
Authors: Lukas Murmann, Michael Gharbi, Miika Aittala, Fredo Durand
Date: 2019-10-17 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Relighting

Collections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation. But even with modern learning techniques, many inverse problems involving lighting and material understanding remain too severely ill-posed to be solved with single-illumination datasets. To fill this gap, we introduce a new multi-illumination dataset of more than 1000 real scenes, each captured under 25 lighting conditions. We demonstrate the richness of this dataset by training state-of-the-art models for three challenging applications: single-image illumination estimation, image relighting, and mixed-illuminant white balance.
Marpa, A practical general parser: the recognizer
Authors: Jeffrey Kegler
Date: 2019-10-17 00:00:00

The Marpa recognizer is described. Marpa is a practical and fully implemented algorithm for the recognition, parsing and evaluation of context-free grammars. The Marpa recognizer is the first to unite the improvements to Earley's algorithm found in Joop Leo's 1991 paper to those in Aycock and Horspool's 2002 paper. Marpa tracks the full state of the parse, at it proceeds, in a form convenient for the application. This greatly improves error detection and enables event-driven parsing. One such technique is "Ruby Slippers" parsing, in which the input is altered in response to the parser's expectations.
Optimization and Learning with Information Streams: Time-varying Algorithms and Applications
Authors: Emiliano Dall'Anese, Andrea Simonetto, Stephen Becker, Liam Madden
Date: 2019-10-17 00:00:00

There is a growing cross-disciplinary effort in the broad domain of optimization and learning with streams of data, applied to settings where traditional batch optimization techniques cannot produce solutions at time scales that match the inter-arrival times of the data points due to computational and/or communication bottlenecks. Special types of online algorithms can handle this situation, and this article focuses on such time-varying optimization algorithms, with emphasis on Machine Leaning and Signal Processing, as well as data-driven Control. Approaches for the design of time-varying or online first-order optimization methods are discussed, with emphasis on algorithms that can handle errors in the gradient, as may arise when the gradient is estimated. Insights on performance metrics and accompanying claims are provided, along with evidence of cases where algorithms that are provably convergent in batch optimization may perform poorly in an online regime. The role of distributed computation is discussed. Illustrative numerical examples for a number of applications of broad interest are provided to convey key ideas.
Anatomically-Informed Data Augmentation for functional MRI with Applications to Deep Learning
Authors: Kevin P. Nguyen, Cherise Chin Fatt, Alex Treacher, Cooper Mellema, Madhukar H. Trivedi, Albert Montillo
Date: 2019-10-17 00:00:00
Tasks: Data Augmentation, Hyperparameter Optimization

The application of deep learning to build accurate predictive models from functional neuroimaging data is often hindered by limited dataset sizes. Though data augmentation can help mitigate such training obstacles, most data augmentation methods have been developed for natural images as in computer vision tasks such as CIFAR, not for medical images. This work helps to fills in this gap by proposing a method for generating new functional Magnetic Resonance Images (fMRI) with realistic brain morphology. This method is tested on a challenging task of predicting antidepressant treatment response from pre-treatment task-based fMRI and demonstrates a 26% improvement in performance in predicting response using augmented images. This improvement compares favorably to state-of-the-art augmentation methods for natural images. Through an ablative test, augmentation is also shown to substantively improve performance when applied before hyperparameter optimization. These results suggest the optimal order of operations and support the role of data augmentation method for improving predictive performance in tasks using fMRI.
Obfuscation via Information Density Estimation
Authors: Hsiang Hsu, Shahab Asoodeh, Flavio du Pin Calmon
Date: 2019-10-17 00:00:00
Tasks: Density Estimation

Identifying features that leak information about sensitive attributes is a key challenge in the design of information obfuscation mechanisms. In this paper, we propose a framework to identify information-leaking features via information density estimation. Here, features whose information densities exceed a pre-defined threshold are deemed information-leaking features. Once these features are identified, we sequentially pass them through a targeted obfuscation mechanism with a provable leakage guarantee in terms of $\mathsf{E}_\gamma$-divergence. The core of this mechanism relies on a data-driven estimate of the trimmed information density for which we propose a novel estimator, named the trimmed information density estimator (TIDE). We then use TIDE to implement our mechanism on three real-world datasets. Our approach can be used as a data-driven pipeline for designing obfuscation mechanisms targeting specific features.
Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation
Authors: Anindya Sarkar, Nikhil Kumar Gupta, Raghu Iyengar
Date: 2019-10-17 00:00:00
Tasks: Adversarial Defense, Image Generation, Representation Learning

Recent studies on the adversarial vulnerability of neural networks have shown that models trained with the objective of minimizing an upper bound on the worst-case loss over all possible adversarial perturbations improve robustness against adversarial attacks. Beside exploiting adversarial training framework, we show that by enforcing a Deep Neural Network (DNN) to be linear in transformed input and feature space improves robustness significantly. We also demonstrate that by augmenting the objective function with Local Lipschitz regularizer boost robustness of the model further. Our method outperforms most sophisticated adversarial training methods and achieves state of the art adversarial accuracy on MNIST, CIFAR10 and SVHN dataset. In this paper, we also propose a novel adversarial image generation method by leveraging Inverse Representation Learning and Linearity aspect of an adversarially trained deep neural network classifier.
Multi-level conformal clustering: A distribution-free technique for clustering and anomaly detection
Authors: Ilia Nouretdinov, James Gammerman, Matteo Fontana, Daljit Rehal
Date: 2019-10-17 00:00:00
Tasks: Anomaly Detection, Clustering, Conformal Prediction

In this work we present a clustering technique called \textit{multi-level conformal clustering (MLCC)}. The technique is hierarchical in nature because it can be performed at multiple significance levels which yields greater insight into the data than performing it at just one level. We describe the theoretical underpinnings of MLCC, compare and contrast it with the hierarchical clustering algorithm, and then apply it to real world datasets to assess its performance. There are several advantages to using MLCC over more classical clustering techniques: Once a significance level has been set, MLCC is able to automatically select the number of clusters. Furthermore, thanks to the conformal prediction framework the resulting clustering model has a clear statistical meaning without any assumptions about the distribution of the data. This statistical robustness also allows us to perform clustering and anomaly detection simultaneously. Moreover, due to the flexibility of the conformal prediction framework, our algorithm can be used on top of many other machine learning algorithms.