pwc-archive/papers-with-abstracts huggingface dataset:
Complex-Object Visual Inspection via Multiple Lighting Configurations
Authors: Maya Aghaei, Matteo Bustreo, Pietro Morerio, Nicolo Carissimi, Alessio Del Bue, Vittorio Murino
Date: 2020-04-20 00:00:00
Tasks: Object

The design of an automatic visual inspection system is usually performed in two stages. While the first stage consists in selecting the most suitable hardware setup for highlighting most effectively the defects on the surface to be inspected, the second stage concerns the development of algorithmic solutions to exploit the potentials offered by the collected data. In this paper, first, we present a novel illumination setup embedding four illumination configurations to resemble diffused, dark-field, and front lighting techniques. Second, we analyze the contributions brought by deploying the proposed setup in training phase only - mimicking the scenario in which an already developed visual inspection system cannot be modified on the customer site - and in evaluation phase. Along with an exhaustive set of experiments, in this paper, we demonstrate the suitability of the proposed setup for effective illumination of complex-objects, defined as manufactured items with variable surface characteristics that cannot be determined a priori. Moreover, we discuss the importance of multiple light configurations availability during training and their natural boosting effect which, without the need to modify the system design in evaluation phase, lead to improvements in the overall system performance.
Learning Ising models from one or multiple samples
Authors: Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala, Anthimos Vardis Kandiros
Date: 2020-04-20 00:00:00

There have been two separate lines of work on estimating Ising models: (1) estimating them from multiple independent samples under minimal assumptions about the model's interaction matrix; and (2) estimating them from one sample in restrictive settings. We propose a unified framework that smoothly interpolates between these two settings, enabling significantly richer estimation guarantees from one, a few, or many samples. Our main theorem provides guarantees for one-sample estimation, quantifying the estimation error in terms of the metric entropy of a family of interaction matrices. As corollaries of our main theorem, we derive bounds when the model's interaction matrix is a (sparse) linear combination of known matrices, or it belongs to a finite set, or to a high-dimensional manifold. In fact, our main result handles multiple independent samples by viewing them as one sample from a larger model, and can be used to derive estimation bounds that are qualitatively similar to those obtained in the afore-described multiple-sample literature. Our technical approach benefits from sparsifying a model's interaction network, conditioning on subsets of variables that make the dependencies in the resulting conditional distribution sufficiently weak. We use this sparsification technique to prove strong concentration and anti-concentration results for the Ising model, which we believe have applications beyond the scope of this paper.
ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers
Authors: Jung-Woo Ha, Kihyun Nam, Jingu Kang, Sang-Woo Lee, Sohee Yang, Hyunhoon Jung, Eunmi Kim, Hyeji Kim, Soojin Kim, Hyun Ah Kim, Kyoungtae Doh, Chan Kyu Lee, Nako Sung, Sunghun Kim
Date: 2020-04-20 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Goal-Oriented Dialog, Open-Domain Dialog, Sentence, speech-recognition, Speech Recognition

Automatic speech recognition (ASR) via call is essential for various applications, including AI for contact center (AICC) services. Despite the advancement of ASR, however, most publicly available call-based speech corpora such as Switchboard are old-fashioned. Also, most existing call corpora are in English and mainly focus on open domain dialog or general scenarios such as audiobooks. Here we introduce a new large-scale Korean call-based speech corpus under a goal-oriented dialog scenario from more than 11,000 people, i.e., ClovaCall corpus. ClovaCall includes approximately 60,000 pairs of a short sentence and its corresponding spoken utterance in a restaurant reservation domain. We validate the effectiveness of our dataset with intensive experiments using two standard ASR models. Furthermore, we release our ClovaCall dataset and baseline source codes to be available via https://github.com/ClovaAI/ClovaCall.
Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning
Authors: Shervin Minaee, Rahele Kafieh, Milan Sonka, Shakib Yazdani, Ghazaleh Jamalipour Soufi
Date: 2020-04-20 00:00:00
Tasks: Sentence, Transfer Learning

The novel corona-virus (also known as Covid-19) has led to a pandemic, impacting more than 200 countries across the globe. With this huge scale, there are a very large number of tweets coming out from every corner of the world, about Covid-19. Analyzing the tweets and detecting the major topics and concerns people are posting about, can help us to better understand the situation, and come up with better planning. In this work, we propose a model based on sentence Transformer to detect the main topics of Tweets in recent months. The proposed model first learns sentence-level representation of tweets, and then group them based on their embedding similarities into some groups, and then detect the most important words in each cluster based on their frequency and average similarity to other words. Through experimental results, we show that our model can detect very informative topics, by processing the tweets on sentence level (which can preserve the overall meaning of the tweets). The proposed model is trained in an unsupervised fashion, and can be applied to any dataset our textual data from social media websites.
End-to-End Whisper to Natural Speech Conversion using Modified Transformer Network
Authors: Abhishek Niranjan, Mukesh Sharma, Sai Bharath Chandra Gutha, M Ali Basha Shaik
Date: 2020-04-20 00:00:00
Tasks: Decoder, Speech Recognition

Machine recognition of an atypical speech like whispered speech, is a challenging task. We introduce whisper-to-natural-speech conversion using sequence-to-sequence approach by proposing enhanced transformer architecture, which uses both parallel and non-parallel data. We investigate different features like Mel frequency cepstral coefficients and smoothed spectral features. The proposed networks are trained end-to-end using supervised approach for feature-to-feature transformation. Further, we also investigate the effectiveness of embedded auxillary decoder used after N encoder sub-layers, trained with the frame-level objective function for identifying source phoneme labels. We show results on opensource wTIMIT and CHAINS datasets by measuring word error rate using end-to-end ASR and also BLEU scores for the generated speech. Alternatively, we also propose a novel method to measure spectral shape of it by measuring formant distributions w.r.t. reference speech, as formant divergence metric. We have found whisper-to-natural converted speech formants probability distribution is similar to the groundtruth distribution. To the authors' best knowledge, this is the first time enhanced transformer has been proposed, both with and without auxiliary decoder for whisper-to-natural-speech conversion and vice versa.
Deep Unfolded Multicast Beamforming
Authors: Satoshi Takabe, Tadashi Wadayama
Date: 2020-04-20 00:00:00

Multicast beamforming is a promising technique for multicast communication. Providing an efficient and powerful beamforming design algorithm is a crucial issue because multicast beamforming problems such as a max-min-fair problem are NP-hard in general. Recently, deep learning-based approaches have been proposed for beamforming design. Although these approaches using deep neural networks exhibit reasonable performance gain compared with conventional optimization-based algorithms, their scalability is an emerging problem for large systems in which beamforming design becomes a more demanding task. In this paper, we propose a novel deep unfolded trainable beamforming design with high scalability and efficiency. The algorithm is designed by expanding the recursive structure of an existing algorithm based on projections onto convex sets and embedding a constant number of trainable parameters to the expanded network, which leads to a scalable and stable training process. Numerical results show that the proposed algorithm can accelerate its convergence speed by using unsupervised learning, which is a challenging training process for deep unfolding.
Robust Partial Matching for Person Search in the Wild
Authors: Yingji Zhong, Xiaoyu Wang, Shiliang Zhang
Date: 2020-04-20 00:00:00
Proceeding: CVPR 2020 6
Tasks: Human Detection, Person Search, valid

Various factors like occlusions, backgrounds, etc., would lead to misaligned detected bounding boxes , e.g., ones covering only portions of human body. This issue is common but overlooked by previous person search works. To alleviate this issue, this paper proposes an Align-to-Part Network (APNet) for person detection and re-Identification (reID). APNet refines detected bounding boxes to cover the estimated holistic body regions, from which discriminative part features can be extracted and aligned. Aligned part features naturally formulate reID as a partial feature matching procedure, where valid part features are selected for similarity computation, while part features on occluded or noisy regions are discarded. This design enhances the robustness of person search to real-world challenges with marginal computation overhead. This paper also contributes a Large-Scale dataset for Person Search in the wild (LSPS), which is by far the largest and the most challenging dataset for person search. Experiments show that APNet brings considerable performance improvement on LSPS. Meanwhile, it achieves competitive performance on existing person search benchmarks like CUHK-SYSU and PRW.
Tracemax: A Novel Single Packet IP Traceback Strategy for Data-Flow Analysis
Authors: Peter Hillmann, Frank Tietze, Gabi Dreo Rodosek
Date: 2020-04-20 00:00:00

The identification of the exact path that packets are routed on in the network is quite a challenge. This paper presents a novel, efficient traceback strategy named Tracemax in context of a defense system against distributed denial of service (DDoS) attacks. A single packet can be directly traced over many more hops than the current existing techniques allow. In combination with a defense system it differentiates between multiple connections. It aims to letting non-malicious connections pass while bad ones get thwarted. The novel concept allows detailed analyses of the traffic and the transmission path through the network. The strategy can effectively reduce the effect of common bandwidth and resource consumption attacks, foster early warning and prevention as well as higher the availability of the network services for the wanted customers.
Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework
Authors: Marta B. M. Ranzini, Irme Groothuis, Kerstin Kläser, M. Jorge Cardoso, Johann Henckel, Sébastien Ourselin, Alister Hart, Marc Modat
Date: 2020-04-20 00:00:00
Tasks: Computed Tomography (CT), Deep Learning

Metal artefact reduction (MAR) techniques aim at removing metal-induced noise from clinical images. In Computed Tomography (CT), supervised deep learning approaches have been shown effective but limited in generalisability, as they mostly rely on synthetic data. In Magnetic Resonance Imaging (MRI) instead, no method has yet been introduced to correct the susceptibility artefact, still present even in MAR-specific acquisitions. In this work, we hypothesise that a multimodal approach to MAR would improve both CT and MRI. Given their different artefact appearance, their complementary information can compensate for the corrupted signal in either modality. We thus propose an unsupervised deep learning method for multimodal MAR. We introduce the use of Locally Normalised Cross Correlation as a loss term to encourage the fusion of multimodal information. Experiments show that our approach favours a smoother correction in the CT, while promoting signal recovery in the MRI.
How Do Neural Networks Estimate Optical Flow? A Neuropsychology-Inspired Study
Authors: D. B. de Jong, F. Paredes-Vallés, G. C. H. E. de Croon
Date: 2020-04-20 00:00:00
Tasks: Decoder, Optical Flow Estimation

End-to-end trained convolutional neural networks have led to a breakthrough in optical flow estimation. The most recent advances focus on improving the optical flow estimation by improving the architecture and setting a new benchmark on the publicly available MPI-Sintel dataset. Instead, in this article, we investigate how deep neural networks estimate optical flow. A better understanding of how these networks function is important for (i) assessing their generalization capabilities to unseen inputs, and (ii) suggesting changes to improve their performance. For our investigation, we focus on FlowNetS, as it is the prototype of an encoder-decoder neural network for optical flow estimation. Furthermore, we use a filter identification method that has played a major role in uncovering the motion filters present in animal brains in neuropsychological research. The method shows that the filters in the deepest layer of FlowNetS are sensitive to a variety of motion patterns. Not only do we find translation filters, as demonstrated in animal brains, but thanks to the easier measurements in artificial neural networks, we even unveil dilation, rotation, and occlusion filters. Furthermore, we find similarities in the refinement part of the network and the perceptual filling-in process which occurs in the mammal primary visual cortex.
A Novel Multi-Agent System for Complex Scheduling Problems
Authors: Peter Hillmann, Tobias Uhlig, Gabi Dreo Rodosek, Oliver Rose
Date: 2020-04-20 00:00:00
Tasks: Scheduling

Complex scheduling problems require a large amount computation power and innovative solution methods. The objective of this paper is the conception and implementation of a multi-agent system that is applicable in various problem domains. Independent specialized agents handle small tasks, to reach a superordinate target. Effective coordination is therefore required to achieve productive cooperation. Role models and distributed artificial intelligence are employed to tackle the resulting challenges. We simulate a NP-hard scheduling problem to demonstrate the validity of our approach. In addition to the general agent based framework we propose new simulation-based optimization heuristics to given scheduling problems. Two of the described optimization algorithms are implemented using agents. This paper highlights the advantages of the agent-based approach, like the reduction in layout complexity, improved control of complicated systems, and extendability.
Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking
Authors: Peiliang Li, Jieqi Shi, Shaojie Shen
Date: 2020-04-20 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Object Tracking, Benchmarking, Object, Object Tracking

Directly learning multiple 3D objects motion from sequential images is difficult, while the geometric bundle adjustment lacks the ability to localize the invisible object centroid. To benefit from both the powerful object understanding skill from deep neural network meanwhile tackle precise geometry modeling for consistent trajectory estimation, we propose a joint spatial-temporal optimization-based stereo 3D object tracking method. From the network, we detect corresponding 2D bounding boxes on adjacent images and regress an initial 3D bounding box. Dense object cues (local depth and local coordinates) that associating to the object centroid are then predicted using a region-based network. Considering both the instant localization accuracy and motion consistency, our optimization models the relations between the object centroid and observed cues into a joint spatial-temporal error function. All historic cues will be summarized to contribute to the current estimation by a per-frame marginalization strategy without repeated computation. Quantitative evaluation on the KITTI tracking dataset shows our approach outperforms previous image-based 3D tracking methods by significant margins. We also report extensive results on multiple categories and larger datasets (KITTI raw and Argoverse Tracking) for future benchmarking.
From graph cuts to isoperimetric inequalities: Convergence rates of Cheeger cuts on data clouds
Authors: Nicolas Garcia Trillos, Ryan Murray, Matthew Thorpe
Date: 2020-04-20 00:00:00
Tasks: Clustering

In this work we study statistical properties of graph-based clustering algorithms that rely on the optimization of balanced graph cuts, the main example being the optimization of Cheeger cuts. We consider proximity graphs built from data sampled from an underlying distribution supported on a generic smooth compact manifold $M$. In this setting, we obtain high probability convergence rates for both the Cheeger constant and the associated Cheeger cuts towards their continuum counterparts. The key technical tools are careful estimates of interpolation operators which lift empirical Cheeger cuts to the continuum, as well as continuum stability estimates for isoperimetric problems. To our knowledge the quantitative estimates obtained here are the first of their kind.
MPNet: Masked and Permuted Pre-training for Language Understanding
Authors: Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu
Date: 2020-04-20 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Language Modeling, Language Modelling, Masked Language Modeling, Only Connect Walls Dataset Task 1 (Grouping), Position, Sentence

BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models. Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence and thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g., BERT, XLNet, RoBERTa) under the same model setting. The code and the pre-trained models are available at: https://github.com/microsoft/MPNet.
Tractable Approximate Gaussian Inference for Bayesian Neural Networks
Authors: James-A. Goulet, Luong Ha Nguyen, Saeid Amiri
Date: 2020-04-20 00:00:00
Proceeding: Journal of Machine Learning Research 2021 9
Tasks: regression

In this paper, we propose an analytical method for performing tractable approximate Gaussian inference (TAGI) in Bayesian neural networks. The method enables the analytical Gaussian inference of the posterior mean vector and diagonal covariance matrix for weights and biases. The method proposed has a computational complexity of $\mathcal{O}(n)$ with respect to the number of parameters $n$, and the tests performed on regression and classification benchmarks confirm that, for a same network architecture, it matches the performance of existing methods relying on gradient backpropagation.
A Revised Generative Evaluation of Visual Dialogue
Authors: Daniela Massiceti, Viveka Kulharia, Puneet K. Dokania, N. Siddharth, Philip H. S. Torr
Date: 2020-04-20 00:00:00

Evaluating Visual Dialogue, the task of answering a sequence of questions relating to a visual input, remains an open research challenge. The current evaluation scheme of the VisDial dataset computes the ranks of ground-truth answers in predefined candidate sets, which Massiceti et al. (2018) show can be susceptible to the exploitation of dataset biases. This scheme also does little to account for the different ways of expressing the same answer--an aspect of language that has been well studied in NLP. We propose a revised evaluation scheme for the VisDial dataset leveraging metrics from the NLP literature to measure consensus between answers generated by the model and a set of relevant answers. We construct these relevant answer sets using a simple and effective semi-supervised method based on correlation, which allows us to automatically extend and scale sparse relevance annotations from humans to the entire dataset. We release these sets and code for the revised evaluation scheme as DenseVisDial, and intend them to be an improvement to the dataset in the face of its existing constraints and design choices.
Thompson Sampling for Linearly Constrained Bandits
Authors: Vidit Saxena, Joseph E. Gonzalez, Joakim Jaldén
Date: 2020-04-20 00:00:00
Tasks: Multi-Armed Bandits, Thompson Sampling

We address multi-armed bandits (MAB) where the objective is to maximize the cumulative reward under a probabilistic linear constraint. For a few real-world instances of this problem, constrained extensions of the well-known Thompson Sampling (TS) heuristic have recently been proposed. However, finite-time analysis of constrained TS is challenging; as a result, only O(\sqrt{T}) bounds on the cumulative reward loss (i.e., the regret) are available. In this paper, we describe LinConTS, a TS-based algorithm for bandits that place a linear constraint on the probability of earning a reward in every round. We show that for LinConTS, the regret as well as the cumulative constraint violations are upper bounded by O(\log T) for the suboptimal arms. We develop a proof technique that relies on careful analysis of the dual problem and combine it with recent theoretical work on unconstrained TS. Through numerical experiments on two real-world datasets, we demonstrate that LinConTS outperforms an asymptotically optimal upper confidence bound (UCB) scheme in terms of simultaneously minimizing the regret and the violation.
Unsupervised Vehicle Counting via Multiple Camera Domain Adaptation
Authors: Luca Ciampi, Carlos Santiago, Joao Paulo Costeira, Claudio Gennaro, Giuseppe Amato
Date: 2020-04-20 00:00:00
Tasks: Domain Adaptation

Monitoring vehicle flows in cities is crucial to improve the urban environment and quality of life of citizens. Images are the best sensing modality to perceive and assess the flow of vehicles in large areas. Current technologies for vehicle counting in images hinge on large quantities of annotated data, preventing their scalability to city-scale as new cameras are added to the system. This is a recurrent problem when dealing with physical systems and a key research area in Machine Learning and AI. We propose and discuss a new methodology to design image-based vehicle density estimators with few labeled data via multiple camera domain adaptations.