pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Reasoning in Regular Boardgames
Authors: Jakub Kowalski, Radosław Miernik, Maksymilian Mika, Wojciech Pawlik, Jakub Sutowicz, Marek Szykuła, Andrzej Tkaczyk
Date: 2020-06-15 00:00:00

We present the technical side of reasoning in Regular Boardgames (RBG) language -- a universal General Game Playing (GGP) formalism for the class of finite deterministic games with perfect information, encoding rules in the form of regular expressions. RBG serves as a research tool that aims to aid in the development of generalized algorithms for knowledge inference, analysis, generation, learning, and playing games. In all these tasks, both generality and efficiency are important. In the first part, this paper describes optimizations used by the RBG compiler. The impact of these optimizations ranges from 1.7 to even 33-fold efficiency improvement when measuring the number of possible game playouts per second. Then, we perform an in-depth efficiency comparison with three other modern GGP systems (GDL, Ludii, Ai Ai). We also include our own highly optimized game-specific reasoners to provide a point of reference of the maximum speed. Our experiments show that RBG is currently the fastest among the abstract general game playing languages, and its efficiency can be competitive to common interface-based systems that rely on handcrafted game-specific implementations. Finally, we discuss some issues and methodology of computing benchmarks like this.
Robust Locality-Aware Regression for Labeled Data Classification
Authors: Liangchen Hu, Wensheng Zhang
Date: 2020-06-15 00:00:00
Tasks: Classification, feature selection, General Classification, regression, Representation Learning

With the dramatic increase of dimensions in the data representation, extracting latent low-dimensional features becomes of the utmost importance for efficient classification. Aiming at the problems of unclear margin representation and difficulty in revealing the data manifold structure in most of the existing linear discriminant methods, we propose a new discriminant feature extraction framework, namely Robust Locality-Aware Regression (RLAR). In our model, we introduce a retargeted regression to perform the marginal representation learning adaptively instead of using the general average inter-class margin. Besides, we formulate a new strategy for enhancing the local intra-class compactness of the data manifold, which can achieve the joint learning of locality-aware graph structure and desirable projection matrix. To alleviate the disturbance of outliers and prevent overfitting, we measure the regression term and locality-aware term together with the regularization term by the L2,1 norm. Further, forcing the row sparsity on the projection matrix through the L2,1 norm achieves the cooperation of feature selection and feature extraction. Then, we derive an effective iterative algorithm for solving the proposed model. The experimental results over a range of UCI data sets and other benchmark databases demonstrate that the proposed RLAR outperforms some state-of-the-art approaches.
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
Authors: Cher Bass, Mariana da Silva, Carole Sudre, Petru-Daniel Tudosiu, Stephen M. Smith, Emma C. Robinson
Date: 2020-06-15 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Classification, General Classification, Image-to-Image Translation, Lesion Detection, Translation

Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification problems but is particularly crucial within the neuroscience domain, where accurate mechanistic models of behaviours, or disease, require knowledge of all features discriminative of a trait. At the same time, predicting class relevance from brain images is challenging as phenotypes are typically heterogeneous, and changes occur against a background of significant natural variation. Here, we present a novel framework for creating class specific FA maps through image-to-image translation. We propose the use of a VAE-GAN to explicitly disentangle class relevance from background features for improved interpretability properties, which results in meaningful FA maps. We validate our method on 2D and 3D brain image datasets of dementia (ADNI dataset), ageing (UK Biobank), and (simulated) lesion detection. We show that FA maps generated by our method outperform baseline FA methods when validated against ground truth. More significantly, our approach is the first to use latent space sampling to support exploration of phenotype variation. Our code will be available online at https://github.com/CherBass/ICAM.
On the Multi-Property Extraction and Beyond
Authors: Tomasz Dwojak, Michał Pietruszka, Łukasz Borchmann, Filip Graliński, Jakub Chłędowski
Date: 2020-06-15 00:00:00
Tasks: Machine Reading Comprehension, Reading Comprehension

In this paper, we investigate the Dual-source Transformer architecture on the WikiReading information extraction and machine reading comprehension dataset. The proposed model outperforms the current state-of-the-art by a large margin. Next, we introduce WikiReading Recycled - a newly developed public dataset, supporting the task of multiple property extraction. It keeps the spirit of the original WikiReading but does not inherit the identified disadvantages of its predecessor.
Detecting unusual input to neural networks
Authors: Jörg Martin, Clemens Elster
Date: 2020-06-15 00:00:00

Evaluating a neural network on an input that differs markedly from the training data might cause erratic and flawed predictions. We study a method that judges the unusualness of an input by evaluating its informative content compared to the learned parameters. This technique can be used to judge whether a network is suitable for processing a certain input and to raise a red flag that unexpected behavior might lie ahead. We compare our approach to various methods for uncertainty evaluation from the literature for various datasets and scenarios. Specifically, we introduce a simple, effective method that allows to directly compare the output of such metrics for single input points even if these metrics live on different scales.
Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset
Authors: Andrei Andrusenko, Aleksandr Laptev, Ivan Medennikov
Date: 2020-06-15 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech-to-Text

This paper presents an exploration of end-to-end automatic speech recognition systems (ASR) for the largest open-source Russian language data set -- OpenSTT. We evaluate different existing end-to-end approaches such as joint CTC/Attention, RNN-Transducer, and Transformer. All of them are compared with the strong hybrid ASR system based on LF-MMI TDNN-F acoustic model. For the three available validation sets (phone calls, YouTube, and books), our best end-to-end model achieves word error rate (WER) of 34.8%, 19.1%, and 18.1%, respectively. Under the same conditions, the hybridASR system demonstrates 33.5%, 20.9%, and 18.6% WER.
Behind the Mask: A Computational Study of Anonymous' Presence on Twitter
Authors: Keenan Jones, Jason R. C. Nurse, Shujun Li
Date: 2020-06-15 00:00:00

The hacktivist group Anonymous is unusual in its public-facing nature. Unlike other cybercriminal groups, which rely on secrecy and privacy for protection, Anonymous is prevalent on the social media site, Twitter. In this paper we re-examine some key findings reported in previous small-scale qualitative studies of the group using a large-scale computational analysis of Anonymous' presence on Twitter. We specifically refer to reports which reject the group's claims of leaderlessness, and indicate a fracturing of the group after the arrests of prominent members in 2011-2013. In our research, we present the first attempts to use machine learning to identify and analyse the presence of a network of over 20,000 Anonymous accounts spanning from 2008-2019 on the Twitter platform. In turn, this research utilises social network analysis (SNA) and centrality measures to examine the distribution of influence within this large network, identifying the presence of a small number of highly influential accounts. Moreover, we present the first study of tweets from some of the identified key influencer accounts and, through the use of topic modelling, demonstrate a similarity in overarching subjects of discussion between these prominent accounts. These findings provide robust, quantitative evidence to support the claims of smaller-scale, qualitative studies of the Anonymous collective.
Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility
Authors: Sen Cui, Weishen Pan, Chang-Shui Zhang, Fei Wang
Date: 2020-06-15 00:00:00
Tasks: Fairness

Bipartite ranking, which aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data, is widely adopted in various applications where sample prioritization is needed. Recently, there have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups defined by sensitive attributes. While there could be trade-off between fairness and performance, in this paper we propose a model agnostic post-processing framework for balancing them in the bipartite ranking scenario. Specifically, we maximize a weighted sum of the utility and fairness by directly adjusting the relative ordering of samples across groups. By formulating this problem as the identification of an optimal warping path across different protected groups, we propose a non-parametric method to search for such an optimal path through a dynamic programming process. Our method is compatible with various classification models and applicable to a variety of ranking fairness metrics. Comprehensive experiments on a suite of benchmark data sets and two real-world patient electronic health record repositories show that our method can achieve a great balance between the algorithm utility and ranking fairness. Furthermore, we experimentally verify the robustness of our method when faced with the fewer training samples and the difference between training and testing ranking score distributions.
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
Authors: Dingfan Chen, Tribhuvanesh Orekondy, Mario Fritz
Date: 2020-06-15 00:00:00
Proceeding: NeurIPS 2020 12

The wide-spread availability of rich data has fueled the growth of machine learning applications in numerous domains. However, growth in domains with highly-sensitive data (e.g., medical) is largely hindered as the private nature of data prohibits it from being shared. To this end, we propose Gradient-sanitized Wasserstein Generative Adversarial Networks (GS-WGAN), which allows releasing a sanitized form of the sensitive data with rigorous privacy guarantees. In contrast to prior work, our approach is able to distort gradient information more precisely, and thereby enabling training deeper models which generate more informative samples. Moreover, our formulation naturally allows for training GANs in both centralized and federated (i.e., decentralized) data scenarios. Through extensive experiments, we find our approach consistently outperforms state-of-the-art approaches across multiple metrics (e.g., sample quality) and datasets.
AMENet: Attentive Maps Encoder Network for Trajectory Prediction
Authors: Hao Cheng, Wentong Liao, Michael Ying Yang, Bodo Rosenhahn, Monika Sester
Date: 2020-06-15 00:00:00
Tasks: Prediction, Trajectory Prediction

Trajectory prediction is critical for applications of planning safe future movements and remains challenging even for the next few seconds in urban mixed traffic. How an agent moves is affected by the various behaviors of its neighboring agents in different environments. To predict movements, we propose an end-to-end generative model named Attentive Maps Encoder Network (AMENet) that encodes the agent's motion and interaction information for accurate and realistic multi-path trajectory prediction. A conditional variational auto-encoder module is trained to learn the latent space of possible future paths based on attentive dynamic maps for interaction modeling and then is used to predict multiple plausible future trajectories conditioned on the observed past trajectories. The efficacy of AMENet is validated using two public trajectory prediction benchmarks Trajnet and InD.
Robust Federated Recommendation System
Authors: Chen Chen, Jingfeng Zhang, Anthony K. H. Tung, Mohan Kankanhalli, Gang Chen
Date: 2020-06-15 00:00:00
Tasks: Recommendation Systems

Federated recommendation systems can provide good performance without collecting users' private data, making them attractive. However, they are susceptible to low-cost poisoning attacks that can degrade their performance. In this paper, we develop a novel federated recommendation technique that is robust against the poisoning attack where Byzantine clients prevail. We argue that the key to Byzantine detection is monitoring of gradients of the model parameters of clients. We then propose a robust learning strategy where instead of using model parameters, the central server computes and utilizes the gradients to filter out Byzantine clients. Theoretically, we justify our robust learning strategy by our proposed definition of Byzantine resilience. Empirically, we confirm the efficacy of our robust learning strategy employing four datasets in a federated recommendation system.
Markov-Lipschitz Deep Learning
Authors: Stan Z. Li, Zelin Zang, Lirong Wu
Date: 2020-06-15 00:00:00
Tasks: Deep Learning, Dimensionality Reduction, Representation Learning, Topological Data Analysis

We propose a novel framework, called Markov-Lipschitz deep learning (MLDL), to tackle geometric deterioration caused by collapse, twisting, or crossing in vector-based neural network transformations for manifold-based representation learning and manifold data generation. A prior constraint, called locally isometric smoothness (LIS), is imposed across-layers and encoded into a Markov random field (MRF)-Gibbs distribution. This leads to the best possible solutions for local geometry preservation and robustness as measured by locally geometric distortion and locally bi-Lipschitz continuity. Consequently, the layer-wise vector transformations are enhanced into well-behaved, LIS-constrained metric homeomorphisms. Extensive experiments, comparisons, and ablation study demonstrate significant advantages of MLDL for manifold learning and manifold data generation. MLDL is general enough to enhance any vector transformation-based networks. The code is available at https://github.com/westlake-cairi/Markov-Lipschitz-Deep-Learning.
Dermatologist vs Neural Network
Authors: Kaushil Mangaroliya, Mitt Shah
Date: 2020-06-15 00:00:00

Cancer, in general, is very deadly. Timely treatment of any cancer is the key to saving a life. Skin cancer is no exception. There have been thousands of Skin Cancer cases registered per year all over the world. There have been 123,000 deadly melanoma cases detected in a single year. This huge number is proven to be a cause of a high amount of UV rays present in the sunlight due to the degradation of the Ozone layer. If not detected at an early stage, skin cancer can lead to the death of the patient. Unavailability of proper resources such as expert dermatologists, state of the art testing facilities, and quick biopsy results have led researchers to develop a technology that can solve the above problem. Deep Learning is one such method that has offered extraordinary results. The Convolutional Neural Network proposed in this study out performs every pretrained models. We trained our model on the HAM10000 dataset which offers 10015 images belonging to 7 classes of skin disease. The model we proposed gave an accuracy of 89%. This model can predict deadly melanoma skin cancer with a great accuracy. Hopefully, this study can help save people's life where there is the unavailability of proper dermatological resources by bridging the gap using our proposed study.
Adversarial Weighting for Domain Adaptation in Regression
Authors: Antoine de Mathelin, Guillaume Richard, Francois Deheeger, Mathilde Mougeot, Nicolas Vayatis
Date: 2020-06-15 00:00:00
Tasks: Domain Adaptation, regression

We present a novel instance-based approach to handle regression tasks in the context of supervised domain adaptation under an assumption of covariate shift. The approach developed in this paper is based on the assumption that the task on the target domain can be efficiently learned by adequately reweighting the source instances during training phase. We introduce a novel formulation of the optimization objective for domain adaptation which relies on a discrepancy distance characterizing the difference between domains according to a specific task and a class of hypotheses. To solve this problem, we develop an adversarial network algorithm which learns both the source weighting scheme and the task in one feed-forward gradient descent. We provide numerical evidence of the relevance of the method on public data sets for regression domain adaptation through reproducible experiments.
Learn to cycle: Time-consistent feature discovery for action recognition
Authors: Alexandros Stergiou, Ronald Poppe
Date: 2020-06-15 00:00:00
Tasks: Action Classification, Action Recognition, Action Recognition In Videos, Video Classification

Generalizing over temporal variations is a prerequisite for effective action recognition in videos. Despite significant advances in deep neural networks, it remains a challenge to focus on short-term discriminative motions in relation to the overall performance of an action. We address this challenge by allowing some flexibility in discovering relevant spatio-temporal features. We introduce Squeeze and Recursion Temporal Gates (SRTG), an approach that favors inputs with similar activations with potential temporal variations. We implement this idea with a novel CNN block that uses an LSTM to encapsulate feature dynamics, in conjunction with a temporal gate that is responsible for evaluating the consistency of the discovered dynamics and the modeled features. We show consistent improvement when using SRTG blocks, with only a minimal increase in the number of GFLOPs. On Kinetics-700, we perform on par with current state-of-the-art models, and outperform these on HACS, Moments in Time, UCF-101 and HMDB-51.
Learning Heuristic Selection with Dynamic Algorithm Configuration
Authors: David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
Date: 2020-06-15 00:00:00
Tasks: Heuristic Search

A key challenge in satisficing planning is to use multiple heuristics within one heuristic search. An aggregation of multiple heuristic estimates, for example by taking the maximum, has the disadvantage that bad estimates of a single heuristic can negatively affect the whole search. Since the performance of a heuristic varies from instance to instance, approaches such as algorithm selection can be successfully applied. In addition, alternating between multiple heuristics during the search makes it possible to use all heuristics equally and improve performance. However, all these approaches ignore the internal search dynamics of a planning system, which can help to select the most useful heuristics for the current expansion step. We show that dynamic algorithm configuration can be used for dynamic heuristic selection which takes into account the internal search dynamics of a planning system. Furthermore, we prove that this approach generalizes over existing approaches and that it can exponentially improve the performance of the heuristic search. To learn dynamic heuristic selection, we propose an approach based on reinforcement learning and show empirically that domain-wise learned policies, which take the internal search dynamics of a planning system into account, can exceed existing approaches.
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Authors: Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
Date: 2020-06-15 00:00:00
Proceeding: NeurIPS 2020 12

Learning from different data types is a long-standing goal in machine learning research, as multiple information sources co-occur when describing natural phenomena. However, existing generative models that approximate a multimodal ELBO rely on difficult or inefficient training schemes to learn a joint distribution and the dependencies between modalities. In this work, we propose a novel, efficient objective function that utilizes the Jensen-Shannon divergence for multiple distributions. It simultaneously approximates the unimodal and joint multimodal posteriors directly via a dynamic prior. In addition, we theoretically prove that the new multimodal JS-divergence (mmJSD) objective optimizes an ELBO. In extensive experiments, we demonstrate the advantage of the proposed mmJSD model compared to previous work in unsupervised, generative learning tasks.
Non-Stationary Off-Policy Optimization
Authors: Joey Hong, Branislav Kveton, Manzil Zaheer, Yin-Lam Chow, Amr Ahmed
Date: 2020-06-15 00:00:00
Tasks: Multi-Armed Bandits

Off-policy learning is a framework for evaluating and optimizing policies without deploying them, from data collected by another policy. Real-world environments are typically non-stationary and the offline learned policies should adapt to these changes. To address this challenge, we study the novel problem of off-policy optimization in piecewise-stationary contextual bandits. Our proposed solution has two phases. In the offline learning phase, we partition logged data into categorical latent states and learn a near-optimal sub-policy for each state. In the online deployment phase, we adaptively switch between the learned sub-policies based on their performance. This approach is practical and analyzable, and we provide guarantees on both the quality of off-policy optimization and the regret during online deployment. To show the effectiveness of our approach, we compare it to state-of-the-art baselines on both synthetic and real-world datasets. Our approach outperforms methods that act only on observed context.
Monte Carlo estimators of first-and total-orders Sobol' indices
Authors: Ivano Azzini, Thierry Mara, Rossana Rosati
Date: 2020-06-15 00:00:00
Tasks: Sensitivity

This study compares the performances of two sampling-based strategies for the simultaneous estimation of the first-and total-orders variance-based sensitivity indices (a.k.a Sobol' indices). The first strategy was introduced by [8] and is the current approach employed by practitioners. The second one was only recently introduced by the authors of the present article. They both rely on different estimators of first-and total-orders Sobol' indices. The asymp-totic normal variances of the two sets of estimators are established and their accuracies are compared theoretically and numerically. The results show that the new strategy outperforms the current one.Keywords: global sensitivity analysis, variance-based sensitivity indices, first-order Sobol' index, total-order Sobol' index, Monte Carlo estimate, asymptotic normality
Differentiable Neural Architecture Transformation for Reproducible Architecture Improvement
Authors: Do-Guk Kim, Heung-Chang Lee
Date: 2020-06-15 00:00:00
Tasks: Neural Architecture Search

Recently, Neural Architecture Search (NAS) methods are introduced and show impressive performance on many benchmarks. Among those NAS studies, Neural Architecture Transformer (NAT) aims to improve the given neural architecture to have better performance while maintaining computational costs. However, NAT has limitations about a lack of reproducibility. In this paper, we propose differentiable neural architecture transformation that is reproducible and efficient. The proposed method shows stable performance on various architectures. Extensive reproducibility experiments on two datasets, i.e., CIFAR-10 and Tiny Imagenet, present that the proposed method definitely outperforms NAT and be applicable to other models and datasets.