pwc-archive/papers-with-abstracts huggingface dataset:
Minimum Bayes Risk Training of RNN-Transducer for End-to-End Speech Recognition
Authors: Chao Weng, Chengzhu Yu, Jia Cui, Chunlei Zhang, Dong Yu
Date: 2019-11-28 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In this work, we propose minimum Bayes risk (MBR) training of RNN-Transducer (RNN-T) for end-to-end speech recognition. Specifically, initialized with a RNN-T trained model, MBR training is conducted via minimizing the expected edit distance between the reference label sequence and on-the-fly generated N-best hypothesis. We also introduce a heuristic to incorporate an external neural network language model (NNLM) in RNN-T beam search decoding and explore MBR training with the external NNLM. Experimental results demonstrate an MBR trained model outperforms a RNN-T trained model substantially and further improvements can be achieved if trained with an external NNLM. Our best MBR trained system achieves absolute character error rate (CER) reductions of 1.2% and 0.5% on read and spontaneous Mandarin speech respectively over a strong convolution and transformer based RNN-T baseline trained on ~21,000 hours of speech.
Dual-Attention Graph Convolutional Network
Authors: Xueya Zhang, Tong Zhang, Wenting Zhao, Zhen Cui, Jian Yang
Date: 2019-11-28 00:00:00
Tasks: Diversity, text-classification, Text Classification

Graph convolutional networks (GCNs) have shown the powerful ability in text structure representation and effectively facilitate the task of text classification. However, challenges still exist in adapting GCN on learning discriminative features from texts due to the main issue of graph variants incurred by the textual complexity and diversity. In this paper, we propose a dual-attention GCN to model the structural information of various texts as well as tackle the graph-invariant problem through embedding two types of attention mechanisms, i.e. the connection-attention and hop-attention, into the classic GCN. To encode various connection patterns between neighbour words, connection-attention adaptively imposes different weights specified to neighbourhoods of each word, which captures the short-term dependencies. On the other hand, the hop-attention applies scaled coefficients to different scopes during the graph diffusion process to make the model learn more about the distribution of context, which captures long-term semantics in an adaptive way. Extensive experiments are conducted on five widely used datasets to evaluate our dual-attention GCN, and the achieved state-of-the-art performance verifies the effectiveness of dual-attention mechanisms.
Designing the Next Generation of Intelligent Personal Robotic Assistants for the Physically Impaired
Authors: Basit Ayantunde, Jane Odum, Fadlullah Olawumi, Joshua Olalekan
Date: 2019-11-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Natural Language Understanding, object-detection, Object Detection, speech-recognition, Speech Recognition, Speech Synthesis

The physically impaired commonly have difficulties performing simple routine tasks without relying on other individuals who are not always readily available and thus make them strive for independence. While their impaired abilities can in many cases be augmented (to certain degrees) with the use of assistive technologies, there has been little attention to their applications in embodied AI with assistive technologies. This paper presents the modular framework, architecture, and design of the mid-fidelity prototype of MARVIN: an artificial-intelligence-powered robotic assistant designed to help the physically impaired in performing simple day-to-day tasks. The prototype features a trivial locomotion unit and also utilizes various state-of-the-art neural network architectures for specific modular components of the system. These components perform specialized functions, such as automatic speech recognition, object detection, natural language understanding, speech synthesis, etc. We also discuss the constraints, challenges encountered, potential future applications and improvements towards succeeding prototypes.
Product Knowledge Graph Embedding for E-commerce
Authors: Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Date: 2019-11-28 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Marketing, Multi-Task Learning, Representation Learning

In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critical for general e-commerce applications including marketing, advertisement, search ranking and recommendation. We first provide a comprehensive comparison between PKG and ordinary knowledge graph (KG) and then illustrate why KG embedding methods are not suitable for PKG learning. We construct a self-attention-enhanced distributed representation learning model for learning PKG embeddings from raw customer activity data in an end-to-end fashion. We design an effective multi-task learning schema to fully leverage the multi-modal e-commerce data. The Poincare embedding is also employed to handle complex entity structures. We use a real-world dataset from grocery.walmart.com to evaluate the performances on knowledge completion, search ranking and recommendation. The proposed approach compares favourably to baselines in knowledge completion and downstream tasks.
Metre as a stylometric feature in Latin hexameter poetry
Authors: Benjamin Nagy
Date: 2019-11-28 00:00:00
Tasks: Classification, General Classification, One-Class Classification

This paper demonstrates that metre is a privileged indicator of authorial style in classical Latin hexameter poetry. Using only metrical features, pairwise classification experiments are performed between 5 first-century authors (10 comparisons) using four different machine-learning models. The results showed a two-label classification accuracy of at least 95% with samples as small as ten lines and no greater than eighty lines (up to around 500 words). These sample sizes are an order of magnitude smaller than those typically recommended for BOW ('bag of words') or n-gram approaches, and the reported accuracy is outstanding. Additionally, this paper explores the potential for novelty (forgery) detection, or 'one-class classification'. An analysis of the disputed Aldine Additamentum (Sil. Ital. Puni. 8:144-225) concludes (p=0.0013) that the metrical style differs significantly from that of the rest of the poem.
Learning Multi-level Weight-centric Features for Few-shot Learning
Authors: Mingjiang Liang, Shaoli Huang, Shirui Pan, Mingming Gong, Wei Liu
Date: 2019-11-28 00:00:00
Tasks: Few-Shot Learning

Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning. Contemporary approaches based on weight-generation scheme delivers a straightforward and flexible solution to the problem. However, they did not fully consider both the representation power for unseen categories and weight generation capacity in feature learning, making it a significant performance bottleneck. This paper proposes a multi-level weight-centric feature learning to give full play to feature extractor's dual roles in few-shot learning. Our proposed method consists of two essential techniques: a weight-centric training strategy to improve the features' prototype-ability and a multi-level feature incorporating a mid- and relation-level information. The former increases the feasibility of constructing a discriminative decision boundary based on a few samples. Simultaneously, the latter helps improve the transferability for characterizing novel classes and preserve classification capability for base classes. We extensively evaluate our approach to low-shot classification benchmarks. Experiments demonstrate our proposed method significantly outperforms its counterparts in both standard and generalized settings and using different network backbones.
Qini-based Uplift Regression
Authors: Mouloud Belbahri, Alejandro Murua, Olivier Gandouet, Vahid Partovi Nia
Date: 2019-11-28 00:00:00
Tasks: Marketing, regression

Uplift models provide a solution to the problem of isolating the marketing effect of a campaign. For customer churn reduction, uplift models are used to identify the customers who are likely to respond positively to a retention activity only if targeted, and to avoid wasting resources on customers that are very likely to switch to another company. We introduce a Qini-based uplift regression model to analyze a large insurance company's retention marketing campaign. Our approach is based on logistic regression models. We show that a Qini-optimized uplift model acts as a regularizing factor for uplift, much as a penalized likelihood model does for regression. This results in interpretable parsimonious models with few relevant xplanatory variables. Our results show that performing Qini-based parameters estimation significantly improves the uplift models performance.
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs
Authors: Dang Nguyen, Sunil Gupta, Santu Rana, Alistair Shilton, Svetha Venkatesh
Date: 2019-11-28 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning, Thompson Sampling

Many real-world functions are defined over both categorical and category-specific continuous variables and thus cannot be optimized by traditional Bayesian optimization (BO) methods. To optimize such functions, we propose a new method that formulates the problem as a multi-armed bandit problem, wherein each category corresponds to an arm with its reward distribution centered around the optimum of the objective function in continuous variables. Our goal is to identify the best arm and the maximizer of the corresponding continuous function simultaneously. Our algorithm uses a Thompson sampling scheme that helps connecting both multi-arm bandit and BO in a unified framework. We extend our method to batch BO to allow parallel optimization when multiple resources are available. We theoretically analyze our method for convergence and prove sub-linear regret bounds. We perform a variety of experiments: optimization of several benchmark functions, hyper-parameter tuning of a neural network, and automatic selection of the best machine learning model along with its optimal hyper-parameters (a.k.a automated machine learning). Comparisons with other methods demonstrate the effectiveness of our proposed method.
Towards Reliable Evaluation of Road Network Reconstructions
Authors: Leonardo Citraro, Mateusz Koziński, Pascal Fua
Date: 2019-11-28 00:00:00

Existing performance measures rank delineation algorithms inconsistently, which makes it difficult to decide which one is best in any given situation. We show that these inconsistencies stem from design flaws that make the metrics insensitive to whole classes of errors. To provide more reliable evaluation, we design three new metrics that are far more consistent even though they use very different approaches to comparing ground-truth and reconstructed road networks. We use both synthetic and real data to demonstrate this and advocate the use of these corrected metrics as a tool to gauge future progress.
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering
Authors: Ali Javed, Scott D. Hamshaw, Donna M. Rizzo, Byung Suk Lee
Date: 2019-11-28 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis, Time Series Clustering

Hydrological storm events are a primary driver for transporting water quality constituents such as turbidity, suspended sediments and nutrients. Analyzing the concentration (C) of these water quality constituents in response to increased streamflow discharge (Q), particularly when monitored at high temporal resolution during a hydrological event, helps to characterize the dynamics and flux of such constituents. A conventional approach to storm event analysis is to reduce the C-Q time series to two-dimensional (2-D) hysteresis loops and analyze these 2-D patterns. While effective and informative to some extent, this hysteresis loop approach has limitations because projecting the C-Q time series onto a 2-D plane obscures detail (e.g., temporal variation) associated with the C-Q relationships. In this paper, we address this issue using a multivariate time series clustering approach. Clustering is applied to sequences of river discharge and suspended sediment data (acquired through turbidity-based monitoring) from six watersheds located in the Lake Champlain Basin in the northeastern United States. While clusters of the hydrological storm events using the multivariate time series approach were found to be correlated to 2-D hysteresis loop classifications and watershed locations, the clusters differed from the 2-D hysteresis classifications. Additionally, using available meteorological data associated with storm events, we examine the characteristics of computational clusters of storm events in the study watersheds and identify the features driving the clustering approach.
3D Shape Completion with Multi-view Consistent Inference
Authors: Tao Hu, Zhizhong Han, Matthias Zwicker
Date: 2019-11-28 00:00:00

3D shape completion is important to enable machines to perceive the complete geometry of objects from partial observations. To address this problem, view-based methods have been presented. These methods represent shapes as multiple depth images, which can be back-projected to yield corresponding 3D point clouds, and they perform shape completion by learning to complete each depth image using neural networks. While view-based methods lead to state-of-the-art results, they currently do not enforce geometric consistency among the completed views during the inference stage. To resolve this issue, we propose a multi-view consistent inference technique for 3D shape completion, which we express as an energy minimization problem including a data term and a regularization term. We formulate the regularization term as a consistency loss that encourages geometric consistency among multiple views, while the data term guarantees that the optimized views do not drift away too much from a learned shape descriptor. Experimental results demonstrate that our method completes shapes more accurately than previous techniques.
Information-Geometric Set Embeddings (IGSE): From Sets to Probability Distributions
Authors: Ke Sun, Frank Nielsen
Date: 2019-11-27 00:00:00

This letter introduces an abstract learning problem called the "set embedding": The objective is to map sets into probability distributions so as to lose less information. We relate set union and intersection operations with corresponding interpolations of probability distributions. We also demonstrate a preliminary solution with experimental results on toy set embedding examples.
Android Botnet Detection using Convolutional Neural Networks
Authors: Sina Hojjatinia, Sajad Hamzenejadi, Hadis Mohseni
Date: 2019-11-27 00:00:00

Today, Android devices are able to provide various services. They support applications for different purposes such as entertainment, business, health, education, and banking services. Because of the functionality and popularity of Android devices as well as the open-source policy of Android OS, they have become a suitable target for attackers. Android Botnet is one of the most dangerous malwares because an attacker called Botmaster can control that remotely to perform destructive attacks. A number of researchers have used different well-known Machine Learning (ML) methods to recognize Android Botnets from benign applications. However, these conventional methods are not able to detect new sophisticated Android Botnets. In this paper, we propose a novel method based on Android permissions and Convolutional Neural Networks (CNNs) to classify Botnets and benign Android applications. Being the first developed method that uses CNNs for this aim, we also proposed a novel method to represent each application as an image which is constructed based on the co-occurrence of used permissions in that application. The proposed CNN is a binary classifier that is trained using these images. Evaluating the proposed method on 5450 Android applications consist of Botnet and benign samples, the obtained results show the accuracy of 97.2% and recall of 96% which is a promising result just using Android permissions.
Empirical Upper Bound in Object Detection and More
Authors: Ali Borji, Seyed Mehdi Iranmanesh
Date: 2019-11-27 00:00:00
Tasks: Object, object-detection, Object Detection

Object detection remains as one of the most notorious open problems in computer vision. Despite large strides in accuracy in recent years, modern object detectors have started to saturate on popular benchmarks raising the question of how far we can reach with deep learning tools and tricks. Here, by employing 2 state-of-the-art object detection benchmarks, and analyzing more than 15 models over 4 large scale datasets, we I) carefully determine the upperbound in AP, which is 91.6% on VOC (test2007), 78.2% on COCO (val2017), and 58.9% on OpenImages V4 (validation), regardless of the IOU. These numbers are much better than the mAP of the best model1 (47.9% on VOC, and 46.9% on COCO; IOUs=.5:.95), II) characterize the sources of errors in object detectors, in a novel and intuitive way, and find that classification error (confusion with other classes and misses) explains the largest fraction of errors and weighs more than localization and duplicate errors, and III) analyze the invariance properties of models when surrounding context of an object is removed, when an object is placed in an incongruent background, and when images are blurred or flipped vertically. We find that models generate boxes on empty regions and that context is more important for detecting small objects than larger ones. Our work taps into the tight relationship between recognition and detection and offers insights for building better models.
Soft Anchor-Point Object Detection
Authors: Chenchen Zhu, Fangyi Chen, Zhiqiang Shen, Marios Savvides
Date: 2019-11-27 00:00:00
Proceeding: ECCV 2020 8
Tasks: Dense Object Detection, feature selection, Object, object-detection, Object Detection

Recently, anchor-free detection methods have been through great progress. The major two families, anchor-point detection and key-point detection, are at opposite edges of the speed-accuracy trade-off, with anchor-point detectors having the speed advantage. In this work, we boost the performance of the anchor-point detector over the key-point counterparts while maintaining the speed advantage. To achieve this, we formulate the detection problem from the anchor point's perspective and identify ineffective training as the main problem. Our key insight is that anchor points should be optimized jointly as a group both within and across feature pyramid levels. We propose a simple yet effective training strategy with soft-weighted anchor points and soft-selected pyramid levels to address the false attention issue within each pyramid level and the feature selection issue across all the pyramid levels, respectively. To evaluate the effectiveness, we train a single-stage anchor-free detector called Soft Anchor-Point Detector (SAPD). Experiments show that our concise SAPD pushes the envelope of speed/accuracy trade-off to a new level, outperforming recent state-of-the-art anchor-free and anchor-based detectors. Without bells and whistles, our best model can achieve a single-model single-scale AP of 47.4% on COCO.
QubitHD: A Stochastic Acceleration Method for HD Computing-Based Machine Learning
Authors: Samuel Bosch, Alexander Sanchez de la Cerda, Mohsen Imani, Tajana Simunic Rosing, Giovanni De Micheli
Date: 2019-11-27 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Clustering, General Classification

Machine Learning algorithms based on Brain-inspired Hyperdimensional(HD) computing imitate cognition by exploiting statistical properties of high-dimensional vector spaces. It is a promising solution for achieving high energy efficiency in different machine learning tasks, such as classification, semi-supervised learning, and clustering. A weakness of existing HD computing-based ML algorithms is the fact that they have to be binarized to achieve very high energy efficiency. At the same time, binarized models reach lower classification accuracies. To solve the problem of the trade-off between energy efficiency and classification accuracy, we propose the QubitHD algorithm. It stochastically binarizes HD-based algorithms, while maintaining comparable classification accuracies to their non-binarized counterparts. The FPGA implementation of QubitHD provides a 65% improvement in terms of energy efficiency, and a 95% improvement in terms of training time, as compared to state-of-the-art HD-based ML algorithms. It also outperforms state-of-the-art low-cost classifiers (such as Binarized Neural Networks) in terms of speed and energy efficiency by an order of magnitude during training and inference.
Calibrationless Parallel MRI using Model based Deep Learning (C-MODL)
Authors: Aniket Pramanik, Hemant Aggarwal, Mathews Jacob
Date: 2019-11-27 00:00:00
Tasks: Deep Learning, MRI Reconstruction

We introduce a fast model based deep learning approach for calibrationless parallel MRI reconstruction. The proposed scheme is a non-linear generalization of structured low rank (SLR) methods that self learn linear annihilation filters from the same subject. It pre-learns non-linear annihilation relations in the Fourier domain from exemplar data. The pre-learning strategy significantly reduces the computational complexity, making the proposed scheme three orders of magnitude faster than SLR schemes. The proposed framework also allows the use of a complementary spatial domain prior; the hybrid regularization scheme offers improved performance over calibrated image domain MoDL approach. The calibrationless strategy minimizes potential mismatches between calibration data and the main scan, while eliminating the need for a fully sampled calibration region.
Improving Model Robustness Using Causal Knowledge
Authors: Trent Kyono, Mihaela van der Schaar
Date: 2019-11-27 00:00:00
Tasks: model, Model Selection

For decades, researchers in fields, such as the natural and social sciences, have been verifying causal relationships and investigating hypotheses that are now well-established or understood as truth. These causal mechanisms are properties of the natural world, and thus are invariant conditions regardless of the collection domain or environment. We show in this paper how prior knowledge in the form of a causal graph can be utilized to guide model selection, i.e., to identify from a set of trained networks the models that are the most robust and invariant to unseen domains. Our method incorporates prior knowledge (which can be incomplete) as a Structural Causal Model (SCM) and calculates a score based on the likelihood of the SCM given the target predictions of a candidate model and the provided input variables. We show on both publicly available and synthetic datasets that our method is able to identify more robust models in terms of generalizability to unseen out-of-distribution test examples and domains where covariates have shifted.