pwc-archive/papers-with-abstracts huggingface dataset:
Automated Reasoning in Temporal DL-Lite
Authors: Sabiha Tahrat, German Braun, Alessandro Artale, Marco Gario, Ana Ozaki
Date: 2020-08-17 00:00:00
Tasks: Translation

This paper investigates the feasibility of automated reasoning over temporal DL-Lite (TDL-Lite) knowledge bases (KBs). We test the usage of off-the-shelf LTL reasoners to check satisfiability of TDL-Lite KBs. In particular, we test the robustness and the scalability of reasoners when dealing with TDL-Lite TBoxes paired with a temporal ABox. We conduct various experiments to analyse the performance of different reasoners by randomly generating TDL-Lite KBs and then measuring the running time and the size of the translations. Furthermore, in an effort to make the usage of TDL-Lite KBs a reality, we present a fully fledged tool with a graphical interface to design them. Our interface is based on conceptual modelling principles and it is integrated with our translation tool and a temporal reasoner.
On the Suboptimality of Negative Momentum for Minimax Optimization
Authors: Guodong Zhang, Yuanhao Wang
Date: 2020-08-17 00:00:00

Smooth game optimization has recently attracted great interest in machine learning as it generalizes the single-objective optimization paradigm. However, game dynamics is more complex due to the interaction between different players and is therefore fundamentally different from minimization, posing new challenges for algorithm design. Notably, it has been shown that negative momentum is preferred due to its ability to reduce oscillation in game dynamics. Nevertheless, the convergence rate of negative momentum was only established in simple bilinear games. In this paper, we extend the analysis to smooth and strongly-convex strongly-concave minimax games by taking the variational inequality formulation. By connecting momentum method with Chebyshev polynomials, we show that negative momentum accelerates convergence of game dynamics locally, though with a suboptimal rate. To the best of our knowledge, this is the \emph{first work} that provides an explicit convergence rate for negative momentum in this setting.
Adiabatic Quantum Optimization Fails to Solve the Knapsack Problem
Authors: Lauren Pusey-Nazzaro, Prasanna Date
Date: 2020-08-17 00:00:00

In this work, we attempt to solve the integer-weight knapsack problem using the D-Wave 2000Q adiabatic quantum computer. The knapsack problem is a well-known NP-complete problem in computer science, with applications in economics, business, finance, etc. We attempt to solve a number of small knapsack problems whose optimal solutions are known; we find that adiabatic quantum optimization fails to produce solutions corresponding to optimal filling of the knapsack in all problem instances. We compare results obtained on the quantum hardware to the classical simulated annealing algorithm and two solvers employing a hybrid branch-and-bound algorithm. The simulated annealing algorithm also fails to produce the optimal filling of the knapsack, though solutions obtained by simulated and quantum annealing are no more similar to each other than to the correct solution. We discuss potential causes for this observed failure of adiabatic quantum optimization.
Learning to Actively Reduce Memory Requirements for Robot Control Tasks
Authors: Meghan Booker, Anirudha Majumdar
Date: 2020-08-17 00:00:00

Robots equipped with rich sensing modalities (e.g., RGB-D cameras) performing long-horizon tasks motivate the need for policies that are highly memory-efficient. State-of-the-art approaches for controlling robots often use memory representations that are excessively rich for the task or rely on hand-crafted tricks for memory efficiency. Instead, this work provides a general approach for jointly synthesizing memory representations and policies; the resulting policies actively seek to reduce memory requirements. Specifically, we present a reinforcement learning framework that leverages an implementation of the group LASSO regularization to synthesize policies that employ low-dimensional and task-centric memory representations. We demonstrate the efficacy of our approach with simulated examples including navigation in discrete and continuous spaces as well as vision-based indoor navigation set in a photo-realistic simulator. The results on these examples indicate that our method is capable of finding policies that rely only on low-dimensional memory representations, improving generalization, and actively reducing memory requirements.
Zero Shot Domain Generalization
Authors: Udit Maniyar, Joseph K J, Aniket Anand Deshmukh, Urun Dogan, Vineeth N. Balasubramanian
Date: 2020-08-17 00:00:00
Tasks: Domain Generalization

Standard supervised learning setting assumes that training data and test data come from the same distribution (domain). Domain generalization (DG) methods try to learn a model that when trained on data from multiple domains, would generalize to a new unseen domain. We extend DG to an even more challenging setting, where the label space of the unseen domain could also change. We introduce this problem as Zero-Shot Domain Generalization (to the best of our knowledge, the first such effort), where the model generalizes across new domains and also across new classes in those domains. We propose a simple strategy which effectively exploits semantic information of classes, to adapt existing DG methods to meet the demands of Zero-Shot Domain Generalization. We evaluate the proposed methods on CIFAR-10, CIFAR-100, F-MNIST and PACS datasets, establishing a strong baseline to foster interest in this new research direction.
Fast decentralized non-convex finite-sum optimization with recursive variance reduction
Authors: Ran Xin, Usman A. Khan, Soummya Kar
Date: 2020-08-17 00:00:00

This paper considers decentralized minimization of $N:=nm$ smooth non-convex cost functions equally divided over a directed network of $n$ nodes. Specifically, we describe a stochastic first-order gradient method, called GT-SARAH, that employs a SARAH-type variance reduction technique and gradient tracking (GT) to address the stochastic and decentralized nature of the problem. We show that GT-SARAH, with appropriate algorithmic parameters, finds an $\epsilon$-accurate first-order stationary point with $O\big(\max\big\{N^{\frac{1}{2}},n(1-\lambda)^{-2},n^{\frac{2}{3}}m^{\frac{1}{3}}(1-\lambda)^{-1}\big\}L\epsilon^{-2}\big)$ gradient complexity, where ${(1-\lambda)\in(0,1]}$ is the spectral gap of the network weight matrix and $L$ is the smoothness parameter of the cost functions. This gradient complexity outperforms that of the existing decentralized stochastic gradient methods. In particular, in a big-data regime such that ${n = O(N^{\frac{1}{2}}(1-\lambda)^{3})}$, this gradient complexity furthers reduces to ${O(N^{\frac{1}{2}}L\epsilon^{-2})}$, independent of the network topology, and matches that of the centralized near-optimal variance-reduced methods. Moreover, in this regime GT-SARAH achieves a non-asymptotic linear speedup, in that, the total number of gradient computations at each node is reduced by a factor of $1/n$ compared to the centralized near-optimal algorithms that perform all gradient computations at a single node. To the best of our knowledge, GT-SARAH is the first algorithm that achieves this property. In addition, we show that appropriate choices of local minibatch size balance the trade-offs between the gradient and communication complexity of GT-SARAH. Over infinite time horizon, we establish that all nodes in GT-SARAH asymptotically achieve consensus and converge to a first-order stationary point in the almost sure and mean-squared sense.
Hey Human, If your Facial Emotions are Uncertain, You Should Use Bayesian Neural Networks!
Authors: Maryam Matin, Matias Valdenegro-Toro
Date: 2020-08-17 00:00:00
Tasks: Emotion Recognition, Facial Emotion Recognition

Facial emotion recognition is the task to classify human emotions in face images. It is a difficult task due to high aleatoric uncertainty and visual ambiguity. A large part of the literature aims to show progress by increasing accuracy on this task, but this ignores the inherent uncertainty and ambiguity in the task. In this paper we show that Bayesian Neural Networks, as approximated using MC-Dropout, MC-DropConnect, or an Ensemble, are able to model the aleatoric uncertainty in facial emotion recognition, and produce output probabilities that are closer to what a human expects. We also show that calibration metrics show strange behaviors for this task, due to the multiple classes that can be considered correct, which motivates future work. We believe our work will motivate other researchers to move away from Classical and into Bayesian Neural Networks.
Siloed Federated Learning for Multi-Centric Histopathology Datasets
Authors: Mathieu Andreux, Jean Ogier Du Terrail, Constance Beguier, Eric W. Tramel
Date: 2020-08-17 00:00:00
Tasks: Deep Learning, Domain Adaptation, Federated Learning, Transfer Learning

While federated learning is a promising approach for training deep learning models over distributed sensitive datasets, it presents new challenges for machine learning, especially when applied in the medical domain where multi-centric data heterogeneity is common. Building on previous domain adaptation works, this paper proposes a novel federated learning approach for deep learning architectures via the introduction of local-statistic batch normalization (BN) layers, resulting in collaboratively-trained, yet center-specific models. This strategy improves robustness to data heterogeneity while also reducing the potential for information leaks by not sharing the center-specific layer activation statistics. We benchmark the proposed method on the classification of tumorous histopathology image patches extracted from the Camelyon16 and Camelyon17 datasets. We show that our approach compares favorably to previous state-of-the-art methods, especially for transfer learning across datasets.
Improving Emergency Response during Hurricane Season using Computer Vision
Authors: Marc Bosch, Christian Conroy, Benjamin Ortiz, Philip Bogden
Date: 2020-08-17 00:00:00
Tasks: Data Visualization, Decision Making, Disaster Response, Line Detection, Management, Semantic Segmentation

We have developed a framework for crisis response and management that incorporates the latest technologies in computer vision (CV), inland flood prediction, damage assessment and data visualization. The framework uses data collected before, during, and after the crisis to enable rapid and informed decision making during all phases of disaster response. Our computer-vision model analyzes spaceborne and airborne imagery to detect relevant features during and after a natural disaster and creates metadata that is transformed into actionable information through web-accessible mapping tools. In particular, we have designed an ensemble of models to identify features including water, roads, buildings, and vegetation from the imagery. We have investigated techniques to bootstrap and reduce dependency on large data annotation efforts by adding use of open source labels including OpenStreetMaps and adding complementary data sources including Height Above Nearest Drainage (HAND) as a side channel to the network's input to encourage it to learn other features orthogonal to visual characteristics. Modeling efforts include modification of connected U-Nets for (1) semantic segmentation, (2) flood line detection, and (3) for damage assessment. In particular for the case of damage assessment, we added a second encoder to U-Net so that it could learn pre-event and post-event image features simultaneously. Through this method, the network is able to learn the difference between the pre- and post-disaster images, and therefore more effectively classify the level of damage. We have validated our approaches using publicly available data from the National Oceanic and Atmospheric Administration (NOAA)'s Remote Sensing Division, which displays the city and street-level details as mosaic tile images as well as data released as part of the Xview2 challenge.
A deep active inference model of the rubber-hand illusion
Authors: Thomas Rood, Marcel van Gerven, Pablo Lanillos
Date: 2020-08-17 00:00:00

Understanding how perception and action deal with sensorimotor conflicts, such as the rubber-hand illusion (RHI), is essential to understand how the body adapts to uncertain situations. Recent results in humans have shown that the RHI not only produces a change in the perceived arm location, but also causes involuntary forces. Here, we describe a deep active inference agent in a virtual environment, which we subjected to the RHI, that is able to account for these results. We show that our model, which deals with visual high-dimensional inputs, produces similar perceptual and force patterns to those found in humans.
Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks
Authors: Chiara Plizzari, Marco Cannici, Matteo Matteucci
Date: 2020-08-17 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, Activity Recognition, Human Activity Recognition, Skeleton Based Action Recognition

Skeleton-based Human Activity Recognition has achieved great interest in recent years as skeleton data has demonstrated being robust to illumination changes, body scales, dynamic camera views, and complex background. In particular, Spatial-Temporal Graph Convolutional Networks (ST-GCN) demonstrated to be effective in learning both spatial and temporal dependencies on non-Euclidean data such as skeleton graphs. Nevertheless, an effective encoding of the latent information underlying the 3D skeleton is still an open problem, especially when it comes to extracting effective information from joint motion patterns and their correlations. In this work, we propose a novel Spatial-Temporal Transformer network (ST-TR) which models dependencies between joints using the Transformer self-attention operator. In our ST-TR model, a Spatial Self-Attention module (SSA) is used to understand intra-frame interactions between different body parts, and a Temporal Self-Attention module (TSA) to model inter-frame correlations. The two are combined in a two-stream network, whose performance is evaluated on three large-scale datasets, NTU-RGB+D 60, NTU-RGB+D 120, and Kinetics Skeleton 400, consistently improving backbone results. Compared with methods that use the same input data, the proposed ST-TR achieves state-of-the-art performance on all datasets when using joints' coordinates as input, and results on-par with state-of-the-art when adding bones information.
Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit Problems
Authors: Fabio Massimo Zennaro, Audun Jøsang
Date: 2020-08-17 00:00:00
Tasks: Decision Making, Multi-Armed Bandits

The multi-armed bandit problem is a classical decision-making problem where an agent has to learn an optimal action balancing exploration and exploitation. Properly managing this trade-off requires a correct assessment of uncertainty; in multi-armed bandits, as in other machine learning applications, it is important to distinguish between stochasticity that is inherent to the system (aleatoric uncertainty) and stochasticity that derives from the limited knowledge of the agent (epistemic uncertainty). In this paper we consider the formalism of subjective logic, a concise and expressive framework to express Dirichlet-multinomial models as subjective opinions, and we apply it to the problem of multi-armed bandits. We propose new algorithms grounded in subjective logic to tackle the multi-armed bandit problem, we compare them against classical algorithms from the literature, and we analyze the insights they provide in evaluating the dynamics of uncertainty. Our preliminary results suggest that subjective logic quantities enable useful assessment of uncertainty that may be exploited by more refined agents.
SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification
Authors: Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari
Date: 2020-08-17 00:00:00
Proceeding: ECCV 2020 8
Tasks: Classification, Decoder, General Classification, Point Cloud Completion

Point clouds are often the default choice for many applications as they exhibit more flexibility and efficiency than volumetric data. Nevertheless, their unorganized nature -- points are stored in an unordered way -- makes them less suited to be processed by deep learning pipelines. In this paper, we propose a method for 3D object completion and classification based on point clouds. We introduce a new way of organizing the extracted features based on their activations, which we name soft pooling. For the decoder stage, we propose regional convolutions, a novel operator aimed at maximizing the global activation entropy. Furthermore, inspired by the local refining procedure in Point Completion Network (PCN), we also propose a patch-deforming operation to simulate deconvolutional operations for point clouds. This paper proves that our regional activation can be incorporated in many point cloud architectures like AtlasNet and PCN, leading to better performance for geometric completion. We evaluate our approach on different 3D tasks such as object completion and classification, achieving state-of-the-art accuracy.
First U-Net Layers Contain More Domain Specific Information Than The Last Ones
Authors: Boris Shirokikh, Ivan Zakazov, Alexey Chernyavskiy, Irina Fedulova, Mikhail Belyaev
Date: 2020-08-17 00:00:00
Tasks: Diversity, Domain Adaptation

MRI scans appearance significantly depends on scanning protocols and, consequently, the data-collection institution. These variations between clinical sites result in dramatic drops of CNN segmentation quality on unseen domains. Many of the recently proposed MRI domain adaptation methods operate with the last CNN layers to suppress domain shift. At the same time, the core manifestation of MRI variability is a considerable diversity of image intensities. We hypothesize that these differences can be eliminated by modifying the first layers rather than the last ones. To validate this simple idea, we conducted a set of experiments with brain MRI scans from six domains. Our results demonstrate that 1) domain-shift may deteriorate the quality even for a simple brain extraction segmentation task (surface Dice Score drops from 0.85-0.89 even to 0.09); 2) fine-tuning of the first layers significantly outperforms fine-tuning of the last layers in almost all supervised domain adaptation setups. Moreover, fine-tuning of the first layers is a better strategy than fine-tuning of the whole network, if the amount of annotated data from the new domain is strictly limited.
Estimating action plans for smart poultry houses
Authors: Darlan Felipe Klotz, Richardson Ribeiro, Fabrício Enembreck, Gustavo Denardin, Marco Barbosa, Dalcimar Casanova, Marcelo Teixeira
Date: 2020-08-17 00:00:00

In poultry farming, the systematic choice, update, and implementation of periodic (t) action plans define the feed conversion rate (FCR[t]), which is an acceptable measure for successful production. Appropriate action plans provide tailored resources for broilers, allowing them to grow within the so-called thermal comfort zone, without wast or lack of resources. Although the implementation of an action plan is automatic, its configuration depends on the knowledge of the specialist, tending to be inefficient and error-prone, besides to result in different FCR[t] for each poultry house. In this article, we claim that the specialist's perception can be reproduced, to some extent, by computational intelligence. By combining deep learning and genetic algorithm techniques, we show how action plans can adapt their performance over the time, based on previous well succeeded plans. We also implement a distributed network infrastructure that allows to replicate our method over distributed poultry houses, for their smart, interconnected, and adaptive control. A supervision system is provided as interface to users. Experiments conducted over real data show that our method improves 5% on the performance of the most productive specialist, staying very close to the optimal FCR[t].
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization
Authors: Wenlong Mou, Zheng Wen, Xi Chen
Date: 2020-08-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We study the optimal sample complexity in large-scale Reinforcement Learning (RL) problems with policy space generalization, i.e. the agent has a prior knowledge that the optimal policy lies in a known policy space. Existing results show that without a generalization model, the sample complexity of an RL algorithm will inevitably depend on the cardinalities of state space and action space, which are intractably large in many practical problems. To avoid such undesirable dependence on the state and action space sizes, this paper proposes a new notion of eluder dimension for the policy space, which characterizes the intrinsic complexity of policy learning in an arbitrary Markov Decision Process (MDP). Using a simulator oracle, we prove a near-optimal sample complexity upper bound that only depends linearly on the eluder dimension. We further prove a similar regret bound in deterministic systems without the simulator.
Multi-Agent Deep Reinforcement Learning enabled Computation Resource Allocation in a Vehicular Cloud Network
Authors: Shilin Xu, Caili Guo, Rose Qingyang Hu, Yi Qian
Date: 2020-08-14 00:00:00
Tasks: Combinatorial Optimization, Deep Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we investigate the computational resource allocation problem in a distributed Ad-Hoc vehicular network with no centralized infrastructure support. To support the ever increasing computational needs in such a vehicular network, the distributed virtual cloud network (VCN) is formed, based on which a computational resource sharing scheme through offloading among nearby vehicles is proposed. In view of the time-varying computational resource in VCN, the statistical distribution characteristics for computational resource are analyzed in detail. Thereby, a resource-aware combinatorial optimization objective mechanism is proposed. To alleviate the non-stationary environment caused by the typically multi-agent environment in VCN, we adopt a centralized training and decentralized execution framework. In addition, for the objective optimization problem, we model it as a Markov game and propose a DRL based multi-agent deep deterministic reinforcement learning (MADDPG) algorithm to solve it. Interestingly, to overcome the dilemma of lacking a real central control unit in VCN, the allocation is actually completed on the vehicles in a distributed manner. The simulation results are presented to demonstrate our scheme's effectiveness.
HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition
Authors: Leon Weber, Mario Sänger, Jannes Münchmeyer, Maryam Habibi, Ulf Leser, Alan Akbik
Date: 2020-08-17 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Summary: Named Entity Recognition (NER) is an important step in biomedical information extraction pipelines. Tools for NER should be easy to use, cover multiple entity types, highly accurate, and robust towards variations in text genre and style. To this end, we propose HunFlair, an NER tagger covering multiple entity types integrated into the widely used NLP framework Flair. HunFlair outperforms other state-of-the-art standalone NER tools with an average gain of 7.26 pp over the next best tool, can be installed with a single command and is applied with only four lines of code. Availability: HunFlair is freely available through the Flair framework under an MIT license: https://github.com/flairNLP/flair and is compatible with all major operating systems. Contact:{weberple,saengema,alan.akbik}@informatik.hu-berlin.de