pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Task Learning of System Dialogue Act Selection for Supervised Pretraining of Goal-Oriented Dialogue Policies
Authors: Sarah McLeod, Ivana Kruijff-Korbayova, Bernd Kiefer
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9
Tasks: Multi-Task Learning, Natural Language Understanding

This paper describes the use of Multi-Task Neural Networks (NNs) for system dialogue act selection. These models leverage the representations learned by the Natural Language Understanding (NLU) unit to enable robust initialization/bootstrapping of dialogue policies from medium sized initial data sets. We evaluate the models on two goal-oriented dialogue corpora in the travel booking domain. Results show the proposed models improve over models trained without knowledge of NLU tasks.
B. Rex: a dialogue agent for book recommendations
Authors: Mitchell Abrams, Luke Gessler, Matthew Marge
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9

We present B. Rex, a dialogue agent for book recommendations. B. Rex aims to exploit the cognitive ease of natural dialogue and the excitement of a whimsical persona in order to engage users who might not enjoy using more common interfaces for finding new books. B. Rex succeeds in making book recommendations with good quality based on only information revealed by the user in the dialogue.
SpaceRefNet: a neural approach to spatial reference resolution in a real city environment
Authors: Dmytro Kalpakchi, Johan Boye
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9

Adding interactive capabilities to pedestrian wayfinding systems in the form of spoken dialogue will make them more natural to humans. Such an interactive wayfinding system needs to continuously understand and interpret pedestrian{'}s utterances referring to the spatial context. Achieving this requires the system to identify exophoric referring expressions in the utterances, and link these expressions to the geographic entities in the vicinity. This exophoric spatial reference resolution problem is difficult, as there are often several dozens of candidate referents. We present a neural network-based approach for identifying pedestrian{'}s references (using a network called RefNet) and resolving them to appropriate geographic objects (using a network called SpaceRefNet). Both methods show promising results beating the respective baselines and earlier reported results in the literature.
Which aspects of discourse relations are hard to learn? Primitive decomposition for discourse relation classification
Authors: Charlotte Roze, Chlo{\'e} Braud, Philippe Muller
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9
Tasks: General Classification, Relation, Relation Classification, Relation Prediction

Discourse relation classification has proven to be a hard task, with rather low performance on several corpora that notably differ on the relation set they use. We propose to decompose the task into smaller, mostly binary tasks corresponding to various primitive concepts encoded into the discourse relation definitions. More precisely, we translate the discourse relations into a set of values for attributes based on distinctions used in the mappings between discourse frameworks proposed by Sanders et al. (2018). This arguably allows for a more robust representation of discourse relations, and enables us to address usually ignored aspects of discourse relation prediction, namely multiple labels and underspecified annotations. We show experimentally which of the conceptual primitives are harder to learn from the Penn Discourse Treebank English corpus, and propose a correspondence to predict the original labels, with preliminary empirical comparisons with a direct model.
Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks
Authors: Siddharth Varia, Christopher Hidey, Tuhin Chakrabarty
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9
Tasks: Binary Classification, Classification, General Classification, Prediction, Relation, Relation Classification, Relation Prediction

Word pairs across argument spans have been shown to be effective for predicting the discourse relation between them. We propose an approach to distill knowledge from word pairs for discourse relation classification with convolutional neural networks by incorporating joint learning of implicit and explicit relations. Our novel approach of representing the input as word pairs achieves state-of-the-art results on four-way classification of both implicit and explicit relations as well as one of the binary classification tasks. For explicit relation prediction, we achieve around 20{\%} error reduction on the four-way task. At the same time, compared to a two-layered Bi-LSTM-CRF model, our model is able to achieve these results with half the number of learnable parameters and approximately half the amount of training time.
Interpretable Biomanufacturing Process Risk and Sensitivity Analyses for Quality-by-Design and Stability Control
Authors: Wei Xie, Bo wang, Cheng Li, Dongming Xie, Jared Auclair
Date: 2019-09-10 00:00:00
Tasks: Sensitivity

While biomanufacturing plays a significant role in supporting the economy and ensuring public health, it faces critical challenges, including complexity, high variability, lengthy lead time, and very limited process data, especially for personalized new cell and gene biotherapeutics. Driven by these challenges, we propose an interpretable semantic bioprocess probabilistic knowledge graph and develop a game theory based risk and sensitivity analyses for production process to facilitate quality-by-design and stability control. Specifically, by exploring the causal relationships and interactions of critical process parameters and quality attributes (CPPs/CQAs), we create a Bayesian network based probabilistic knowledge graph characterizing the complex causal interdependencies of all factors. Then, we introduce a Shapley value based sensitivity analysis, which can correctly quantify the variation contribution from each input factor on the outputs (i.e., productivity, product quality). Since the bioprocess model coefficients are learned from limited process observations, we derive the Bayesian posterior distribution to quantify model uncertainty and further develop the Shapley value based sensitivity analysis to evaluate the impact of estimation uncertainty from each set of model coefficients. Therefore, the proposed bioprocess risk and sensitivity analyses can identify the bottlenecks, guide the reliable process specifications and the most "informative" data collection, and improve production stability.
Respiratory Rate Estimation from Face Videos
Authors: Mingliang Chen, Qiang Zhu, Harrison Zhang, Min Wu, Quanzeng Wang
Date: 2019-09-08 00:00:00
Tasks: Heart Rate Variability, Motion Compensation, Respiratory Rate Estimation

Vital signs, such as heart rate (HR), heart rate variability (HRV), respiratory rate (RR), are important indicators for a person's health. Vital signs are traditionally measured with contact sensors, and may be inconvenient and cause discomfort during continuous monitoring. Commercial cameras are promising contact-free sensors, and remote photoplethysmography (rPPG) have been studied to remotely monitor heart rate from face videos. For remote RR measurement, most prior art was based on small periodical motions of chest regions caused by breathing cycles, which are vulnerable to subjects' voluntary movements. This paper explores remote RR measurement based on rPPG obtained from face videos. The paper employs motion compensation, two-phase temporal filtering, and signal pruning to capture signals with high quality. The experimental results demonstrate that the proposed framework can obtain accurate RR results and can provide HR, HRV and RR measurement synergistically in one framework.
Ensemble Methods for Causal Effects in Panel Data Settings
Authors: Susan Athey, Mohsen Bayati, Guido Imbens, Zhaonan Qu
Date: 2019-03-24 00:00:00
Tasks: counterfactual, Matrix Completion, regression

This paper studies a panel data setting where the goal is to estimate causal effects of an intervention by predicting the counterfactual values of outcomes for treated units, had they not received the treatment. Several approaches have been proposed for this problem, including regression methods, synthetic control methods and matrix completion methods. This paper considers an ensemble approach, and shows that it performs better than any of the individual methods in several economic datasets. Matrix completion methods are often given the most weight by the ensemble, but this clearly depends on the setting. We argue that ensemble methods present a fruitful direction for further research in the causal panel data setting.
End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds
Authors: Yin Zhou, Pei Sun, Yu Zhang, Dragomir Anguelov, Jiyang Gao, Tom Ouyang, James Guo, Jiquan Ngiam, Vijay Vasudevan
Date: 2019-10-15 00:00:00
Tasks: 3D Object Detection, object-detection, Object Detection

Recent work on 3D object detection advocates point cloud voxelization in birds-eye view, where objects preserve their physical dimensions and are naturally separable. When represented in this view, however, point clouds are sparse and have highly variable point density, which may cause detectors difficulties in detecting distant or small objects (pedestrians, traffic signs, etc.). On the other hand, perspective view provides dense observations, which could allow more favorable feature encoding for such cases. In this paper, we aim to synergize the birds-eye view and the perspective view and propose a novel end-to-end multi-view fusion (MVF) algorithm, which can effectively learn to utilize the complementary information from both. Specifically, we introduce dynamic voxelization, which has four merits compared to existing voxelization methods, i) removing the need of pre-allocating a tensor with fixed size; ii) overcoming the information loss due to stochastic point/voxel dropout; iii) yielding deterministic voxel embeddings and more stable detection outcomes; iv) establishing the bi-directional relationship between points and voxels, which potentially lays a natural foundation for cross-view feature fusion. By employing dynamic voxelization, the proposed feature fusion architecture enables each point to learn to fuse context information from different views. MVF operates on points and can be naturally extended to other approaches using LiDAR point clouds. We evaluate our MVF model extensively on the newly released Waymo Open Dataset and on the KITTI dataset and demonstrate that it significantly improves detection accuracy over the comparable single-view PointPillars baseline.
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models
Authors: César Roberto de Souza, Adrien Gaidon, Yohann Cabon, Naila Murray, Antonio Manuel López
Date: 2019-10-12 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Representation Learning, Temporal Action Localization, Video Generation

Deep video action recognition models have been highly successful in recent years but require large quantities of manually annotated data, which are expensive and laborious to obtain. In this work, we investigate the generation of synthetic training data for video action recognition, as synthetic data have been successfully used to supervise models for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for "Procedural Human Action Videos". PHAV contains a total of 39,982 videos, with more than 1,000 examples for each of 35 action categories. Our video generation approach is not limited to existing motion capture sequences: 14 of these 35 categories are procedurally defined synthetic actions. In addition, each video is represented with 6 different data modalities, including RGB, optical flow and pixel-level semantic labels. These modalities are generated almost simultaneously using the Multiple Render Targets feature of modern GPUs. In order to leverage PHAV, we introduce a deep multi-task (i.e. that considers action classes from multiple datasets) representation learning architecture that is able to simultaneously learn from synthetic and real video datasets, even when their action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance. Our approach also significantly outperforms video representations produced by fine-tuning state-of-the-art unsupervised generative models of videos.
Bayesian Temporal Factorization for Multidimensional Time Series Prediction
Authors: Xinyu Chen, Lijun Sun
Date: 2019-10-14 00:00:00
Tasks: Imputation, Missing Values, Prediction, Time Series, Time Series Analysis, Time Series Prediction

Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in many real-world applications such as monitoring urban traffic and air quality. Making predictions on these time series has become a critical challenge due to not only the large-scale and high-dimensional nature but also the considerable amount of missing data. In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series -- in particular spatiotemporal data -- in the presence of missing values. By integrating low-rank matrix/tensor factorization and vector autoregressive (VAR) process into a single probabilistic graphical model, this framework can characterize both global and local consistencies in large-scale time series data. The graphical model allows us to effectively perform probabilistic predictions and produce uncertainty estimates without imputing those missing values. We develop efficient Gibbs sampling algorithms for model inference and model updating for real-time prediction and test the proposed BTF framework on several real-world spatiotemporal data sets for both missing data imputation and multi-step rolling prediction tasks. The numerical experiments demonstrate the superiority of the proposed BTF approaches over existing state-of-the-art methods.
SegSort: Segmentation by Discriminative Sorting of Segments
Authors: Jyh-Jing Hwang, Stella X. Yu, Jianbo Shi, Maxwell D. Collins, Tien-Ju Yang, Xiao Zhang, Liang-Chieh Chen
Date: 2019-10-15 00:00:00
Proceeding: ICCV 2019 10
Tasks: Clustering, Metric Learning, Segmentation, Semantic Segmentation, Unsupervised Semantic Segmentation

Almost all existing deep learning approaches for semantic segmentation tackle this task as a pixel-wise classification problem. Yet humans understand a scene not in terms of pixels, but by decomposing it into perceptual groups and structures that are the basic building blocks of recognition. This motivates us to propose an end-to-end pixel-wise metric learning approach that mimics this process. In our approach, the optimal visual representation determines the right segmentation within individual images and associates segments with the same semantic classes across images. The core visual learning problem is therefore to maximize the similarity within segments and minimize the similarity between segments. Given a model trained this way, inference is performed consistently by extracting pixel-wise embeddings and clustering, with the semantic label determined by the majority vote of its nearest neighbors from an annotated set. As a result, we present the SegSort, as a first attempt using deep learning for unsupervised semantic segmentation, achieving $76\%$ performance of its supervised counterpart. When supervision is available, SegSort shows consistent improvements over conventional approaches based on pixel-wise softmax training. Additionally, our approach produces more precise boundaries and consistent region predictions. The proposed SegSort further produces an interpretable result, as each choice of label can be easily understood from the retrieved nearest segments.
Tiny Video Networks
Authors: AJ Piergiovanni, Anelia Angelova, Michael S. Ryoo
Date: 2019-10-15 00:00:00
Tasks: CPU, GPU, Video Understanding

Video understanding is a challenging problem with great impact on the abilities of autonomous agents working in the real-world. Yet, solutions so far have been computationally intensive, with the fastest algorithms running for more than half a second per video snippet on powerful GPUs. We propose a novel idea on video architecture learning - Tiny Video Networks - which automatically designs highly efficient models for video understanding. The tiny video models run with competitive performance for as low as 37 milliseconds per video on a CPU and 10 milliseconds on a standard GPU.
Neural tangent kernels, transportation mappings, and universal approximation
Authors: Ziwei Ji, Matus Telgarsky, Ruicheng Xian
Date: 2019-10-15 00:00:00
Proceeding: ICLR 2020 1

This paper establishes rates of universal approximation for the shallow neural tangent kernel (NTK): network weights are only allowed microscopic changes from random initialization, which entails that activations are mostly unchanged, and the network is nearly equivalent to its linearization. Concretely, the paper has two main contributions: a generic scheme to approximate functions with the NTK by sampling from transport mappings between the initial weights and their desired values, and the construction of transport mappings via Fourier transforms. Regarding the first contribution, the proof scheme provides another perspective on how the NTK regime arises from rescaling: redundancy in the weights due to resampling allows individual weights to be scaled down. Regarding the second contribution, the most notable transport mapping asserts that roughly $1 / \delta^{10d}$ nodes are sufficient to approximate continuous functions, where $\delta$ depends on the continuity properties of the target function. By contrast, nearly the same proof yields a bound of $1 / \delta^{2d}$ for shallow ReLU networks; this gap suggests a tantalizing direction for future work, separating shallow ReLU networks and their linearization.
Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora
Authors: Marius Cătălin Iordan, Tyler Giallanza, Cameron T. Ellis, Nicole M. Beckage, Jonathan D. Cohen
Date: 2019-10-15 00:00:00
Tasks: BIG-bench Machine Learning, Empirical Judgments

Applying machine learning algorithms to large-scale, text-based corpora (embeddings) presents a unique opportunity to investigate at scale how human semantic knowledge is organized and how people use it to judge fundamental relationships, such as similarity between concepts. However, efforts to date have shown a substantial discrepancy between algorithm predictions and empirical judgments. Here, we introduce a novel approach of generating embeddings motivated by the psychological theory that semantic context plays a critical role in human judgments. Specifically, we train state-of-the-art machine learning algorithms using contextually-constrained text corpora and show that this greatly improves predictions of similarity judgments and feature ratings. By improving the correspondence between representations derived using embeddings generated by machine learning methods and empirical measurements of human judgments, the approach we describe helps advance the use of large-scale text corpora to understand the structure of human semantic representations.
Electron Neutrino Energy Reconstruction in NOvA Using CNN Particle IDs
Authors: Shiqi Yu
Date: 2019-10-15 00:00:00

NOvA is a long-baseline neutrino oscillation experiment. It is optimized to measure $\nu_e$ appearance and $\nu_{\mu}$ disappearance at the Far Detector in the $\nu_{\mu}$ beam produced by the NuMI facility at Fermilab. NOvA uses a convolutional neural network (CNN) to identify neutrino events in two functionally identical liquid scintillator detectors. A different network, called prong-CNN, has been used to classify reconstructed particles in each event as either lepton or hadron. Within each event, hits are clustered into prongs to reconstruct final-state particles and these prongs form the input to this prong-CNN classifier. Classified particle energies are then used as input to an electron neutrino energy estimator. Improving the resolution and systematic robustness of NOvA's energy estimator will improve the sensitivity of the oscillation parameters measurement. This paper describes the methods to identify particles with prong-CNN and the following approach to estimate $\nu_e$ energy for signal events.
Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI
Authors: Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang, James S. Duncan
Date: 2019-10-15 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis

Recurrent neural networks (RNNs) were designed for dealing with time-series data and have recently been used for creating predictive models from functional magnetic resonance imaging (fMRI) data. However, gathering large fMRI datasets for learning is a difficult task. Furthermore, network interpretability is unclear. To address these issues, we utilize multitask learning and design a novel RNN-based model that learns to discriminate between classes while simultaneously learning to generate the fMRI time-series data. Employing the long short-term memory (LSTM) structure, we develop a discriminative model based on the hidden state and a generative model based on the cell state. The addition of the generative model constrains the network to learn functional communities represented by the LSTM nodes that are both consistent with the data generation as well as useful for the classification task. We apply our approach to the classification of subjects with autism vs. healthy controls using several datasets from the Autism Brain Imaging Data Exchange. Experiments show that our jointly discriminative and generative model improves classification learning while also producing robust and meaningful functional communities for better model understanding.
Data-Driven Deep Learning of Partial Differential Equations in Modal Space
Authors: Kailiang Wu, Dongbin Xiu
Date: 2019-10-15 00:00:00

We present a framework for recovering/approximating unknown time-dependent partial differential equation (PDE) using its solution data. Instead of identifying the terms in the underlying PDE, we seek to approximate the evolution operator of the underlying PDE numerically. The evolution operator of the PDE, defined in infinite-dimensional space, maps the solution from a current time to a future time and completely characterizes the solution evolution of the underlying unknown PDE. Our recovery strategy relies on approximation of the evolution operator in a properly defined modal space, i.e., generalized Fourier space, in order to reduce the problem to finite dimensions. The finite dimensional approximation is then accomplished by training a deep neural network structure, which is based on residual network (ResNet), using the given data. Error analysis is provided to illustrate the predictive accuracy of the proposed method. A set of examples of different types of PDEs, including inviscid Burgers' equation that develops discontinuity in its solution, are presented to demonstrate the effectiveness of the proposed method.