pwc-archive/papers-with-abstracts huggingface dataset:
Turing Test Revisited: A Framework for an Alternative
Authors: Aladdin Ayesh
Date: 2019-06-26 00:00:00
Tasks: Philosophy, valid

This paper aims to question the suitability of the Turing Test, for testing machine intelligence, in the light of advances made in the last 60 years in science, medicine, and philosophy of mind. While the main concept of the test may seem sound and valid, a detailed analysis of what is required to pass the test highlights a significant flow. Once the analysis of the test is presented, a systematic approach is followed in analysing what is needed to devise a test or tests for intelligent machines. The paper presents a plausible generic framework based on categories of factors implied by subjective perception of intelligence. An evaluative discussion concludes the paper highlighting some of the unaddressed issues within this generic framework.
Further advantages of data augmentation on convolutional neural networks
Authors: Alex Hernández-García, Peter König
Date: 2019-06-26 00:00:00
Tasks: Data Augmentation

Data augmentation is a popular technique largely used to enhance the training of convolutional neural networks. Although many of its benefits are well known by deep learning researchers and practitioners, its implicit regularization effects, as compared to popular explicit regularization techniques, such as weight decay and dropout, remain largely unstudied. As a matter of fact, convolutional neural networks for image object classification are typically trained with both data augmentation and explicit regularization, assuming the benefits of all techniques are complementary. In this paper, we systematically analyze these techniques through ablation studies of different network architectures trained with different amounts of training data. Our results unveil a largely ignored advantage of data augmentation: networks trained with just data augmentation more easily adapt to different architectures and amount of training data, as opposed to weight decay and dropout, which require specific fine-tuning of their hyperparameters.
Principal Component Analysis for Multivariate Extremes
Authors: Holger Drees, Anne Sabourin
Date: 2019-06-26 00:00:00

The first order behavior of multivariate heavy-tailed random vectors above large radial thresholds is ruled by a limit measure in a regular variation framework. For a high dimensional vector, a reasonable assumption is that the support of this measure is concentrated on a lower dimensional subspace, meaning that certain linear combinations of the components are much likelier to be large than others. Identifying this subspace and thus reducing the dimension will facilitate a refined statistical analysis. In this work we apply Principal Component Analysis (PCA) to a re-scaled version of radially thresholded observations. Within the statistical learning framework of empirical risk minimization, our main focus is to analyze the squared reconstruction error for the exceedances over large radial thresholds. We prove that the empirical risk converges to the true risk, uniformly over all projection subspaces. As a consequence, the best projection subspace is shown to converge in probability to the optimal one, in terms of the Hausdorff distance between their intersections with the unit sphere. In addition, if the exceedances are re-scaled to the unit ball, we obtain finite sample uniform guarantees to the reconstruction error pertaining to the estimated projection sub-space. Numerical experiments illustrate the relevance of the proposed framework for practical purposes.
Cognitive Systems Approach to Smart Cities
Authors: Aladdin Ayesh
Date: 2019-06-26 00:00:00

In our connected world, services are expected to be delivered at speed through multiple means with seamless communication. To put it in day to day conversational terms, 'there is an app for it' attitude prevails. Several technologies are needed to meet this growing demand and indeed these technologies are being developed. The first noteworthy is Internet of Things (IoT), which is in itself coupled technologies to deliver seamless communication with 'anywhere, anytime' as an underlying objective. The 'anywhere, anytime' service delivery paradigm requires a new type of smart systems in developing these services with better capabilities to interact with the human user, such as personalisation, affect state recognition, etc. Here enter cognitive systems, where AI meets cognitive sciences (e.g. cognitive psychology, linguistics, social cognition, etc.). In this paper we will examine the requirements imposed by smart cities development, e.g. intelligent logistics, sensor networks and domestic appliances connectivity, data streams and media delivery, to mention but few. Then we will explore how cognitive systems can meet the challenges these requirements present to the development of new systems. Throughout our discussion here, examples from our recent and current projects will be given supplemented by examples from the literature.
Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations
Authors: Reuben R Shamir, Yuval Duchin, Jin-Young Kim, Guillermo Sapiro, Noam Harel
Date: 2019-06-26 00:00:00
Tasks: Segmentation

Objective: Overlapping measures are often utilized to quantify the similarity between two binary regions. However, modern segmentation algorithms output a probability or confidence map with continuous values in the zero-to-one interval. Moreover, these binary overlapping measures are biased to structure size. Addressing these challenges is the objective of this work. Methods: We extend the definition of the classical Dice coefficient (DC) overlap to facilitate the direct comparison of a ground truth binary image with a probabilistic map. We call the extended method continuous Dice coefficient (cDC) and show that 1) cDC is less or equal to 1 and cDC = 1 if-and-only-if the structures overlap is complete, and, 2) cDC is monotonically decreasing with the amount of overlap. We compare the classical DC and the cDC in a simulation of partial volume effects that incorporates segmentations of common targets for deep-brainstimulation. Lastly, we investigate the cDC for an automatic segmentation of the subthalamic-nucleus. Results: Partial volume effect simulation on thalamus (large structure) resulted with DC and cDC averages (SD) of 0.98 (0.006) and 0.99 (0.001), respectively. For subthalamic-nucleus (small structure) DC and cDC were 0.86 (0.025) and 0.97 (0.006), respectively. The DC and cDC for automatic STN segmentation were 0.66 and 0.80, respectively. Conclusion: The cDC is well defined for probabilistic segmentation, less biased to structure size and more robust to partial volume effects in comparison to DC. Significance: The proposed method facilitates a better evaluation of segmentation algorithms. As a better measurement tool, it opens the door for the development of better segmentation methods.
Sharing Attention Weights for Fast Transformer
Authors: Tong Xiao, Yinqiao Li, Jingbo Zhu, Zhengtao Yu, Tongran Liu
Date: 2019-06-26 00:00:00
Tasks: Machine Translation, Translation

Recently, the Transformer machine translation system has shown strong results by stacking attention layers on both the source and target-language sides. But the inference of this model is slow due to the heavy use of dot-product attention in auto-regressive decoding. In this paper we speed up Transformer via a fast and lightweight attention model. More specifically, we share attention weights in adjacent layers and enable the efficient re-use of hidden states in a vertical manner. Moreover, the sharing policy can be jointly learned with the MT model. We test our approach on ten WMT and NIST OpenMT tasks. Experimental results show that it yields an average of 1.3X speed-up (with almost no decrease in BLEU) on top of a state-of-the-art implementation that has already adopted a cache for fast inference. Also, our approach obtains a 1.8X speed-up when it works with the \textsc{Aan} model. This is even 16 times faster than the baseline with no use of the attention cache.
Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic
Authors: Maxime Bouton, Alireza Nakhaei, Kikuo Fujimura, Mykel J. Kochenderfer
Date: 2019-06-26 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Decision making in dense traffic can be challenging for autonomous vehicles. An autonomous system only relying on predefined road priorities and considering other drivers as moving objects will cause the vehicle to freeze and fail the maneuver. Human drivers leverage the cooperation of other drivers to avoid such deadlock situations and convince others to change their behavior. Decision making algorithms must reason about the interaction with other drivers and anticipate a broad range of driver behaviors. In this work, we present a reinforcement learning approach to learn how to interact with drivers with different cooperation levels. We enhanced the performance of traditional reinforcement learning algorithms by maintaining a belief over the level of cooperation of other drivers. We show that our agent successfully learns how to navigate a dense merging scenario with less deadlocks than with online planning methods.
Evaluation of head segmentation quality for treatment planning of tumor treating fields in brain tumors
Authors: Reuben R Shamir, Zeev Bomzon
Date: 2019-06-26 00:00:00
Tasks: Segmentation

Tumor treating fields (TTFields) is an FDA approved therapy for the treatment of Gliobastoma Multiform (GBM) and currently being investigated for additional tumor types. TTFields are delivered to the tumor through the placement of transducer arrays (TAs) placed on the patient scalp. The positions of the TAs are associated with treatment outcomes via simulations of the electric fields. Therefore, we are currently developing a method for recommending optimal placement of TAs. A key step to achieve this goal is to correctly segment the head into tissues of similar electrical properties. Visual inspection of segmentation quality is invaluable but time-consuming. Automatic quality assessment can assist in automatic refinement of the segmentation parameters, suggest flaw points to the user and indicate if the segmented method is of sufficient accuracy for TTFields simulation. As a first step in this direction, we identified a set of features that are relevant to atlas-based segmentation and show that these are significantly correlated (p < 0.05) with a similarity measure between validated and automatically computed segmentations. Furthermore, we incorporated these features in a decision tree regressor to predict the similarity of the validated and computed segmentations of 20 TTFields patients using a leave-one-out approach. The predicted similarity measures were highly correlated with the actual ones (average abs. difference 3% (SD = 3%); r = 0.92, p < 0.001). We conclude that quality estimation of segmentations is feasible by incorporating machine learning and segmentation-relevant features.
Color Texture Classification Based on Proposed Impulse-Noise Resistant Color Local Binary Patterns and Significant Points Selection Algorithm
Authors: Shervan Fekri-Ershad, Farshad Tajeripour
Date: 2019-06-26 00:00:00
Tasks: General Classification, Sensitivity, Texture Classification

The main aim of this paper is to propose a color texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise sensitivity and low computational complexity are specified aims for our proposed approach. One of the efficient texture analysis operations is local binary patterns. The proposed approach includes two steps. First, a noise resistant version of color local binary patterns is proposed to decrease sensitivity to noise of LBP. This step is evaluated based on combination of color sensor information using AND operation. In second step, a significant points selection algorithm is proposed to select significant LBP. This phase decreases final computational complexity along with increasing accuracy rate. The Proposed approach is evaluated using Vistex, Outex, and KTH TIPS2a data sets. Our approach has been compared with some state of the art methods. It is experimentally demonstrated that the proposed approach achieves highest accuracy. In two other experiments, result show low noise sensitivity and low computational complexity of the proposed approach in comparison with previous versions of LBP. Rotation invariant, multi resolution, general usability are other advantages of our proposed approach. In the present paper, a new version of LBP is proposed originally, which is called Hybrid color local binary patterns. It can be used in many image processing applications to extract color and texture features jointly. Also, a significant point selection algorithm is proposed for the first time to select key points of images.
Generalized Median Graph via Iterative Alternate Minimizations
Authors: Nicolas Boria, S'ebastien Bougleux, Benoit Gaüzère, Luc Brun
Date: 2019-06-26 00:00:00
Tasks: Clustering

Computing a graph prototype may constitute a core element for clustering or classification tasks. However, its computation is an NP-Hard problem, even for simple classes of graphs. In this paper, we propose an efficient approach based on block coordinate descent to compute a generalized median graph from a set of graphs. This approach relies on a clear definition of the optimization process and handles labeling on both edges and nodes. This iterative process optimizes the edit operations to perform on a graph alternatively on nodes and edges. Several experiments on different datasets show the efficiency of our approach.
Learning Soft-Attention Models for Tempo-invariant Audio-Sheet Music Retrieval
Authors: Stefan Balke, Matthias Dorfer, Luis Carvalho, Andreas Arzt, Gerhard Widmer
Date: 2019-06-26 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval

Connecting large libraries of digitized audio recordings to their corresponding sheet music images has long been a motivation for researchers to develop new cross-modal retrieval systems. In recent years, retrieval systems based on embedding space learning with deep neural networks got a step closer to fulfilling this vision. However, global and local tempo deviations in the music recordings still require careful tuning of the amount of temporal context given to the system. In this paper, we address this problem by introducing an additional soft-attention mechanism on the audio input. Quantitative and qualitative results on synthesized piano data indicate that this attention increases the robustness of the retrieval system by focusing on different parts of the input representation based on the tempo of the audio. Encouraged by these results, we argue for the potential of attention models as a very general tool for many MIR tasks.
Verifying Robustness of Gradient Boosted Models
Authors: Gil Einziger, Maayan Goldstein, Yaniv Sa'ar, Itai Segall
Date: 2019-06-26 00:00:00
Tasks: BIG-bench Machine Learning

Gradient boosted models are a fundamental machine learning technique. Robustness to small perturbations of the input is an important quality measure for machine learning models, but the literature lacks a method to prove the robustness of gradient boosted models. This work introduces VeriGB, a tool for quantifying the robustness of gradient boosted models. VeriGB encodes the model and the robustness property as an SMT formula, which enables state of the art verification tools to prove the model's robustness. We extensively evaluate VeriGB on publicly available datasets and demonstrate a capability for verifying large models. Finally, we show that some model configurations tend to be inherently more robust than others.
Learning Orthogonal Projections in Linear Bandits
Authors: Qiyu Kang, Wee Peng Tay
Date: 2019-06-26 00:00:00

In a linear stochastic bandit model, each arm is a vector in an Euclidean space and the observed return at each time step is an unknown linear function of the chosen arm at that time step. In this paper, we investigate the problem of learning the best arm in a linear stochastic bandit model, where each arm's expected reward is an unknown linear function of the projection of the arm onto a subspace. We call this the projection reward. Unlike the classical linear bandit problem in which the observed return corresponds to the reward, the projection reward at each time step is unobservable. Such a model is useful in recommendation applications where the observed return includes corruption by each individual's biases, which we wish to exclude in the learned model. In the case where there are finitely many arms, we develop a strategy to achieve $O(|\bbD|\log n)$ regret, where $n$ is the number of time steps and $|\bbD|$ is the number of arms. In the case where each arm is chosen from an infinite compact set, our strategy achieves $O(n^{2/3}(\log{n})^{1/2})$ regret. Experiments verify the efficiency of our strategy.
Defending Adversarial Attacks by Correcting logits
Authors: Yifeng Li, Lingxi Xie, Ya zhang, Rui Zhang, Yanfeng Wang, Qi Tian
Date: 2019-06-26 00:00:00

Generating and eliminating adversarial examples has been an intriguing topic in the field of deep learning. While previous research verified that adversarial attacks are often fragile and can be defended via image-level processing, it remains unclear how high-level features are perturbed by such attacks. We investigate this issue from a new perspective, which purely relies on logits, the class scores before softmax, to detect and defend adversarial attacks. Our defender is a two-layer network trained on a mixed set of clean and perturbed logits, with the goal being recovering the original prediction. Upon a wide range of adversarial attacks, our simple approach shows promising results with relatively high accuracy in defense, and the defender can transfer across attackers with similar properties. More importantly, our defender can work in the scenarios that image data are unavailable, and enjoys high interpretability especially at the semantic level.
Q-Learning Inspired Self-Tuning for Energy Efficiency in HPC
Authors: Andreas Gocht, Robert Schöne, Mario Bielert
Date: 2019-06-26 00:00:00
Tasks: Q-Learning

System self-tuning is a crucial task to lower the energy consumption of computers. Traditional approaches decrease the processor frequency in idle or synchronisation periods. However, in High-Performance Computing (HPC) this is not sufficient: if the executed code is load balanced, there are neither idle nor synchronisation phases that can be exploited. Therefore, alternative self-tuning approaches are needed, which allow exploiting different compute characteristics of HPC programs. The novel notion of application regions based on function call stacks, introduced in the Horizon 2020 Project READEX, allows us to define such a self-tuning approach. In this paper, we combine these regions with the Q-Learning typical state-action maps, which save information about available states, possible actions to take, and the expected rewards. By exploiting the existing processor power interface, we are able to provide direct feedback to the learning process. This approach allows us to save up to 15% energy, while only adding a minor runtime overhead.
End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving
Authors: Mohammed Abdou, Mahmoud Elkhateeb, Ibrahim Sobh, Ahmad El-Sallab
Date: 2019-06-26 00:00:00
Tasks: Autonomous Driving, Scene Labeling, Semantic Segmentation, Transfer Learning

3D semantic scene labeling is a fundamental task for Autonomous Driving. Recent work shows the capability of Deep Neural Networks in labeling 3D point sets provided by sensors like LiDAR, and Radar. Imbalanced distribution of classes in the dataset is one of the challenges that face 3D semantic scene labeling task. This leads to misclassifying for the non-dominant classes which suffer from two main problems: a) rare appearance in the dataset, and b) few sensor points reflected from one object of these classes. This paper proposes a Weighted Self-Incremental Transfer Learning as a generalized methodology that solves the imbalanced training dataset problems. It re-weights the components of the loss function computed from individual classes based on their frequencies in the training dataset, and applies Self-Incremental Transfer Learning by running the Neural Network model on non-dominant classes first, then dominant classes one-by-one are added. The experimental results introduce a new 3D point cloud semantic segmentation benchmark for KITTI dataset.
Latent Multi-Criteria Ratings for Recommendations
Authors: Pan Li, Alexander Tuzhilin
Date: 2019-06-26 00:00:00
Tasks: Recommendation Systems

Multi-criteria recommender systems have been increasingly valuable for helping consumers identify the most relevant items based on different dimensions of user experiences. However, previously proposed multi-criteria models did not take into account latent embeddings generated from user reviews, which capture latent semantic relations between users and items. To address these concerns, we utilize variational autoencoders to map user reviews into latent embeddings, which are subsequently compressed into low-dimensional discrete vectors. The resulting compressed vectors constitute latent multi-criteria ratings that we use for the recommendation purposes via standard multi-criteria recommendation methods. We show that the proposed latent multi-criteria rating approach outperforms several baselines significantly and consistently across different datasets and performance evaluation measures.
Solving Statistical Mechanics on Sparse Graphs with Feedback Set Variational Autoregressive Networks
Authors: Feng Pan, Pengfei Zhou, Hai-Jun Zhou, Pan Zhang
Date: 2019-06-26 00:00:00
Tasks: Feedback Vertex Set (FVS)

We propose a method for solving statistical mechanics problems defined on sparse graphs. It extracts a small Feedback Vertex Set (FVS) from the sparse graph, converting the sparse system to a much smaller system with many-body and dense interactions with an effective energy on every configuration of the FVS, then learns a variational distribution parameterized using neural networks to approximate the original Boltzmann distribution. The method is able to estimate free energy, compute observables, and generate unbiased samples via direct sampling without auto-correlation. Extensive experiments show that our approach is more accurate than existing approaches for sparse spin glasses. On random graphs and real-world networks, our approach significantly outperforms the standard methods for sparse systems such as the belief-propagation algorithm; on structured sparse systems such as two-dimensional lattices our approach is significantly faster and more accurate than recently proposed variational autoregressive networks using convolution neural networks.