pwc-archive/papers-with-abstracts huggingface dataset:
Deep kernel processes
Authors: Laurence Aitchison, Adam X. Yang, Sebastian W. Ober
Date: 2020-10-04 00:00:00
Tasks: Gaussian Processes, Variational Inference

We define deep kernel processes in which positive definite Gram matrices are progressively transformed by nonlinear kernel functions and by sampling from (inverse) Wishart distributions. Remarkably, we find that deep Gaussian processes (DGPs), Bayesian neural networks (BNNs), infinite BNNs, and infinite BNNs with bottlenecks can all be written as deep kernel processes. For DGPs the equivalence arises because the Gram matrix formed by the inner product of features is Wishart distributed, and as we show, standard isotropic kernels can be written entirely in terms of this Gram matrix -- we do not need knowledge of the underlying features. We define a tractable deep kernel process, the deep inverse Wishart process, and give a doubly-stochastic inducing-point variational inference scheme that operates on the Gram matrices, not on the features, as in DGPs. We show that the deep inverse Wishart process gives superior performance to DGPs and infinite BNNs on standard fully-connected baselines.
Collaborative Tracking and Capture of Aerial Object using UAVs
Authors: Lima Agnel Tony, Shuvrangshu Jana, Varun V P, Vidyadhara B V, Mohitvishnu S Gadde, Abhishek Kashyap, Rahul Ravichandran, Debasish Ghose
Date: 2020-10-04 00:00:00

This work details the problem of aerial target capture using multiple UAVs. This problem is motivated from the challenge 1 of Mohammed Bin Zayed International Robotic Challenge 2020. The UAVs utilise visual feedback to autonomously detect target, approach it and capture without disturbing the vehicle which carries the target. Multi-UAV collaboration improves the efficiency of the system and increases the chance of capturing the ball robustly in short span of time. In this paper, the proposed architecture is validated through simulation in ROS-Gazebo environment and is further implemented on hardware.
Mining and modeling complex leadership-followership dynamics of movement data
Authors: Chainarong Amornbunchornvej, Tanya Y. Berger-Wolf
Date: 2020-10-04 00:00:00
Tasks: Decision Making, Leadership Inference, Time Series Analysis

Leadership and followership are essential parts of collective decision and organization in social animals, including humans. In nature, relationships of leaders and followers are dynamic and vary with context or temporal factors. Understanding dynamics of leadership and followership, such as how leaders and followers change, emerge, or converge, allows scientists to gain more insight into group decision-making and collective behavior in general. However, given only data of individual activities, it is challenging to infer the dynamics of leaders and followers. In this paper, we focus on mining and modeling frequent patterns of leading and following. We formalize new computational problems and propose a framework that can be used to address several questions regarding group movement. We use the leadership inference framework, mFLICA, to infer the time series of leaders and their factions from movement datasets and then propose an approach to mine and model frequent patterns of both leadership and followership dynamics. We evaluate our framework performance by using several simulated datasets, as well as the real-world dataset of baboon movement to demonstrate the applications of our framework. These are novel computational problems and, to the best of our knowledge, there are no existing comparable methods to address them. Thus, we modify and extend an existing leadership inference framework to provide a non-trivial baseline for comparison. Our framework performs better than this baseline in all datasets. Our framework opens the opportunities for scientists to generate testable scientific hypotheses about the dynamics of leadership in movement data.
DNS Covert Channel Detection via Behavioral Analysis: a Machine Learning Approach
Authors: Salvatore Saeli, Federica Bisio, Pierangelo Lombardo, Danilo Massa
Date: 2020-10-04 00:00:00
Tasks: BIG-bench Machine Learning

Detecting covert channels among legitimate traffic represents a severe challenge due to the high heterogeneity of networks. Therefore, we propose an effective covert channel detection method, based on the analysis of DNS network data passively extracted from a network monitoring system. The framework is based on a machine learning module and on the extraction of specific anomaly indicators able to describe the problem at hand. The contribution of this paper is two-fold: (i) the machine learning models encompass network profiles tailored to the network users, and not to the single query events, hence allowing for the creation of behavioral profiles and spotting possible deviations from the normal baseline; (ii) models are created in an unsupervised mode, thus allowing for the identification of zero-days attacks and avoiding the requirement of signatures or heuristics for new variants. The proposed solution has been evaluated over a 15-day-long experimental session with the injection of traffic that covers the most relevant exfiltration and tunneling attacks: all the malicious variants were detected, while producing a low false-positive rate during the same period.
Facial gesture interfaces for expression and communication
Authors: Michael J. Lyons
Date: 2020-10-04 00:00:00

Considerable effort has been devoted to the automatic extraction of information about action of the face from image sequences. Within the context of human-computer interaction (HCI) we may distinguish systems that allow expression from those which aim at recognition. Most of the work in facial action processing has been directed at automatically recognizing affect from facial actions. By contrast, facial gesture interfaces, which respond to deliberate facial actions, have received comparatively little attention. This paper reviews several projects on vision-based interfaces that rely on facial action for intentional HCI. Applications to several domains are introduced, including text entry, artistic and musical expression and assistive technology for motor-impaired users.
The FaceChannelS: Strike of the Sequences for the AffWild 2 Challenge
Authors: Pablo Barros, Alessandra Sciutti
Date: 2020-10-04 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning

Predicting affective information from human faces became a popular task for most of the machine learning community in the past years. The development of immense and dense deep neural networks was backed by the availability of numerous labeled datasets. These models, most of the time, present state-of-the-art results in such benchmarks, but are very difficult to adapt to other scenarios. In this paper, we present one more chapter of benchmarking different versions of the FaceChannel neural network: we demonstrate how our little model can predict affective information from the facial expression on the novel AffWild2 dataset.
Reverse Operation based Data Augmentation for Solving Math Word Problems
Authors: Qianying Liu, Wenyu Guan, Sujian Li, Fei Cheng, Daisuke Kawahara, Sadao Kurohashi
Date: 2020-10-04 00:00:00
Tasks: Data Augmentation, Math, Mathematical Reasoning, Math Word Problem Solving

Automatically solving math word problems is a critical task in the field of natural language processing. Recent models have reached their performance bottleneck and require more high-quality data for training. We propose a novel data augmentation method that reverses the mathematical logic of math word problems to produce new high-quality math problems and introduce new knowledge points that can benefit learning the mathematical reasoning logic. We apply the augmented data on two SOTA math word problem solving models and compare our results with a strong data augmentation baseline. Experimental results show the effectiveness of our approach. We release our code and data at https://github.com/yiyunya/RODA.
Leveraging Multilingual News Websites for Building a Kurdish Parallel Corpus
Authors: Sina Ahmadi, Hossein Hassani, Daban Q. Jaff
Date: 2020-10-04 00:00:00
Tasks: Articles, Machine Translation, Translation, Transliteration

Machine translation has been a major motivation of development in natural language processing. Despite the burgeoning achievements in creating more efficient machine translation systems thanks to deep learning methods, parallel corpora have remained indispensable for progress in the field. In an attempt to create parallel corpora for the Kurdish language, in this paper, we describe our approach in retrieving potentially-alignable news articles from multi-language websites and manually align them across dialects and languages based on lexical similarity and transliteration of scripts. We present a corpus containing 12,327 translation pairs in the two major dialects of Kurdish, Sorani and Kurmanji. We also provide 1,797 and 650 translation pairs in English-Kurmanji and English-Sorani. The corpus is publicly available under the CC BY-NC-SA 4.0 license.
Intermittent Demand Forecasting with Renewal Processes
Authors: Ali Caner Turkmen, Tim Januschowski, Yuyang Wang, Ali Taylan Cemgil
Date: 2020-10-04 00:00:00
Tasks: Clustering, Demand Forecasting

Intermittency is a common and challenging problem in demand forecasting. We introduce a new, unified framework for building intermittent demand forecasting models, which incorporates and allows to generalize existing methods in several directions. Our framework is based on extensions of well-established model-based methods to discrete-time renewal processes, which can parsimoniously account for patterns such as aging, clustering and quasi-periodicity in demand arrivals. The connection to discrete-time renewal processes allows not only for a principled extension of Croston-type models, but also for an natural inclusion of neural network based models---by replacing exponential smoothing with a recurrent neural network. We also demonstrate that modeling continuous-time demand arrivals, i.e., with a temporal point process, is possible via a trivial extension of our framework. This leads to more flexible modeling in scenarios where data of individual purchase orders are directly available with granular timestamps. Complementing this theoretical advancement, we demonstrate the efficacy of our framework for forecasting practice via an extensive empirical study on standard intermittent demand data sets, in which we report predictive accuracy in a variety of scenarios that compares favorably to the state of the art.
Static and Animated 3D Scene Generation from Free-form Text Descriptions
Authors: Faria Huq, Nafees Ahmed, Anindya Iqbal
Date: 2020-10-04 00:00:00
Tasks: Decoder, Form, Language Modelling, Scene Generation, Sentence

Generating coherent and useful image/video scenes from a free-form textual description is technically a very difficult problem to handle. Textual description of the same scene can vary greatly from person to person, or sometimes even for the same person from time to time. As the choice of words and syntax vary while preparing a textual description, it is challenging for the system to reliably produce a consistently desirable output from different forms of language input. The prior works of scene generation have been mostly confined to rigorous sentence structures of text input which restrict the freedom of users to write description. In our work, we study a new pipeline that aims to generate static as well as animated 3D scenes from different types of free-form textual scene description without any major restriction. In particular, to keep our study practical and tractable, we focus on a small subspace of all possible 3D scenes, containing various combinations of cube, cylinder and sphere. We design a two-stage pipeline. In the first stage, we encode the free-form text using an encoder-decoder neural architecture. In the second stage, we generate a 3D scene based on the generated encoding. Our neural architecture exploits state-of-the-art language model as encoder to leverage rich contextual encoding and a new multi-head decoder to predict multiple features of an object in the scene simultaneously. For our experiments, we generate a large synthetic data-set which contains 13,00,000 and 14,00,000 samples of unique static and animated scene descriptions, respectively. We achieve 98.427% accuracy on test data set in detecting the 3D objects features successfully. Our work shows a proof of concept of one approach towards solving the problem, and we believe with enough training data, the same pipeline can be expanded to handle even broader set of 3D scene generation problems.
Feature Whitening via Gradient Transformation for Improved Convergence
Authors: Shmulik Markovich-Golan, Barak Battash, Amit Bleiweiss
Date: 2020-10-04 00:00:00
Tasks: image-classification, Image Classification

Feature whitening is a known technique for speeding up training of DNN. Under certain assumptions, whitening the activations reduces the Fisher information matrix to a simple identity matrix, in which case stochastic gradient descent is equivalent to the faster natural gradient descent. Due to the additional complexity resulting from transforming the layer inputs and their corresponding gradients in the forward and backward propagation, and from repeatedly computing the Eigenvalue decomposition (EVD), this method is not commonly used to date. In this work, we address the complexity drawbacks of feature whitening. Our contribution is twofold. First, we derive an equivalent method, which replaces the sample transformations by a transformation to the weight gradients, applied to every batch of B samples. The complexity is reduced by a factor of S=(2B), where S denotes the feature dimension of the layer output. As the batch size increases with distributed training, the benefit of using the proposed method becomes more compelling. Second, motivated by the theoretical relation between the condition number of the sample covariance matrix and the convergence speed, we derive an alternative sub-optimal algorithm which recursively reduces the condition number of the latter matrix. Compared to EVD, complexity is reduced by a factor of the input feature dimension M. We exemplify the proposed algorithms with ResNet-based networks for image classification demonstrated on the CIFAR and Imagenet datasets. Parallelizing the proposed algorithms is straightforward and we implement a distributed version thereof. Improved convergence, in terms of speed and attained accuracy, can be observed in our experiments.
A Survey of Unsupervised Dependency Parsing
Authors: Wenjuan Han, Yong Jiang, Hwee Tou Ng, Kewei Tu
Date: 2020-10-04 00:00:00
Proceeding: COLING 2020 8
Tasks: Dependency Parsing, Survey, Unsupervised Dependency Parsing

Syntactic dependency parsing is an important task in natural language processing. Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no annotation of their correct parse trees. Despite its difficulty, unsupervised parsing is an interesting research direction because of its capability of utilizing almost unlimited unannotated text data. It also serves as the basis for other research in low-resource parsing. In this paper, we survey existing approaches to unsupervised dependency parsing, identify two major classes of approaches, and discuss recent trends. We hope that our survey can provide insights for researchers and facilitate future research on this topic.
Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation
Authors: Kang Li, Lequan Yu, Shujun Wang, Pheng-Ann Heng
Date: 2020-10-04 00:00:00
Tasks: Cardiac Segmentation, Image Segmentation, Knowledge Distillation, Medical Image Segmentation, Segmentation, Semantic Segmentation

The success of deep convolutional neural networks is partially attributed to the massive amount of annotated training data. However, in practice, medical data annotations are usually expensive and time-consuming to be obtained. Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e.g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity. To alleviate the learning difficulties caused by modality-specific appearance discrepancy, we first present an Image Alignment Module (IAM) to narrow the appearance gap between assistant and target modality data.We then propose a novel Mutual Knowledge Distillation (MKD) scheme to thoroughly exploit the modality-shared knowledge to facilitate the target-modality segmentation. To be specific, we formulate our framework as an integration of two individual segmentors. Each segmentor not only explicitly extracts one modality knowledge from corresponding annotations, but also implicitly explores another modality knowledge from its counterpart in mutual-guided manner. The ensemble of two segmentors would further integrate the knowledge from both modalities and generate reliable segmentation results on target modality. Experimental results on the public multi-class cardiac segmentation data, i.e., MMWHS 2017, show that our method achieves large improvements on CT segmentation by utilizing additional MRI data and outperforms other state-of-the-art multi-modality learning methods.
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting
Authors: Sayna Ebrahimi, Suzanne Petryk, Akash Gokul, William Gan, Joseph E. Gonzalez, Marcus Rohrbach, Trevor Darrell
Date: 2020-10-04 00:00:00
Proceeding: ICLR 2021 1
Tasks: Continual Learning

The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced when the model is encouraged to remember the \textit{evidence} for previously made decisions. As a first step towards exploring this hypothesis, we propose a simple novel training paradigm, called Remembering for the Right Reasons (RRR), that additionally stores visual model explanations for each example in the buffer and ensures the model has "the right reasons" for its predictions by encouraging its explanations to remain consistent with those used to make decisions at training time. Without this constraint, there is a drift in explanations and increase in forgetting as conventional continual learning algorithms learn new tasks. We demonstrate how RRR can be easily added to any memory or regularization-based approach and results in reduced forgetting, and more importantly, improved model explanations. We have evaluated our approach in the standard and few-shot settings and observed a consistent improvement across various CL approaches using different architectures and techniques to generate model explanations and demonstrated our approach showing a promising connection between explainability and continual learning. Our code is available at \url{https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons}.
NLP Service APIs and Models for Efficient Registration of New Clients
Authors: Sahil Shah, Vihari Piratla, Soumen Chakrabarti, Sunita Sarawagi
Date: 2020-10-04 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: GPU, Language Modeling, Language Modelling, NER

State-of-the-art NLP inference uses enormous neural architectures and models trained for GPU-months, well beyond the reach of most consumers of NLP. This has led to one-size-fits-all public API-based NLP service models by major AI companies, serving large numbers of clients. Neither (hardware deficient) clients nor (heavily subscribed) servers can afford traditional fine tuning. Many clients own little or no labeled data. We initiate a study of adaptation of centralized NLP services to clients, and present one practical and lightweight approach. Each client uses an unsupervised, corpus-based sketch to register to the service. The server uses an auxiliary network to map the sketch to an abstract vector representation, which then informs the main labeling network. When a new client registers with its sketch, it gets immediate accuracy benefits. We demonstrate the success of the proposed architecture using sentiment labeling, NER, and predictive language modeling
Multi-Level Evolution Strategies for High-Resolution Black-Box Control
Authors: Ofer M. Shir, Xi Xing, Herschel Rabitz
Date: 2020-10-04 00:00:00
Tasks: global-optimization, Vocal Bursts Intensity Prediction

This paper introduces a multi-level (m-lev) mechanism into Evolution Strategies (ESs) in order to address a class of global optimization problems that could benefit from fine discretization of their decision variables. Such problems arise in engineering and scientific applications, which possess a multi-resolution control nature, and thus may be formulated either by means of low-resolution variants (providing coarser approximations with presumably lower accuracy for the general problem) or by high-resolution controls. A particular scientific application concerns practical Quantum Control (QC) problems, whose targeted optimal controls may be discretized to increasingly higher resolution, which in turn carries the potential to obtain better control yields. However, state-of-the-art derivative-free optimization heuristics for high-resolution formulations nominally call for an impractically large number of objective function calls. Therefore, an effective algorithmic treatment for such problems is needed. We introduce a framework with an automated scheme to facilitate guided-search over increasingly finer levels of control resolution for the optimization problem, whose on-the-fly learned parameters require careful adaptation. We instantiate the proposed m-lev self-adaptive ES framework by two specific strategies, namely the classical elitist single-child (1+1)-ES and the non-elitist multi-child derandomized $(\mu_W,\lambda)$-sep-CMA-ES. We first show that the approach is suitable by simulation-based optimization of QC systems which were heretofore viewed as too complex to address. We also present a laboratory proof-of-concept for the proposed approach on a basic experimental QC system objective.
RODE: Learning Roles to Decompose Multi-Agent Tasks
Authors: Tonghan Wang, Tarun Gupta, Anuj Mahajan, Bei Peng, Shimon Whiteson, Chongjie Zhang
Date: 2020-10-04 00:00:00
Proceeding: ICLR 2021 1
Tasks: Clustering, Starcraft, Starcraft II

Role-based learning holds the promise of achieving scalable multi-agent learning by decomposing complex tasks using roles. However, it is largely unclear how to efficiently discover such a set of roles. To solve this problem, we propose to first decompose joint action spaces into restricted role action spaces by clustering actions according to their effects on the environment and other agents. Learning a role selector based on action effects makes role discovery much easier because it forms a bi-level learning hierarchy -- the role selector searches in a smaller role space and at a lower temporal resolution, while role policies learn in significantly reduced primitive action-observation spaces. We further integrate information about action effects into the role policies to boost learning efficiency and policy generalization. By virtue of these advances, our method (1) outperforms the current state-of-the-art MARL algorithms on 10 of the 14 scenarios that comprise the challenging StarCraft II micromanagement benchmark and (2) achieves rapid transfer to new environments with three times the number of agents. Demonstrative videos are available at https://sites.google.com/view/rode-marl .