pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Symmetric Reward Noising for Reinforcement Learning
Authors: Refael Vivanti, Talya D. Sohlberg-Baris, Shlomo Cohen, Orna Cohen
Date: 2019-05-24 00:00:00
Tasks: Autonomous Driving, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent reinforcement learning algorithms, though achieving impressive results in various fields, suffer from brittle training effects such as regression in results and high sensitivity to initialization and parameters. We claim that some of the brittleness stems from variance differences, i.e. when different environment areas - states and/or actions - have different rewards variance. This causes two problems: First, the "Boring Areas Trap" in algorithms such as Q-learning, where moving between areas depends on the current area variance, and getting out of a boring area is hard due to its low variance. Second, the "Manipulative Consultant" problem, when value-estimation functions used in DQN and Actor-Critic algorithms influence the agent to prefer boring areas, regardless of the mean rewards return, as they maximize estimation precision rather than rewards. This sheds a new light on how exploration contribute to training, as it helps with both challenges. Cognitive experiments in humans showed that noised reward signals may paradoxically improve performance. We explain this using the two mentioned problems, claiming that both humans and algorithms may share similar challenges. Inspired by this result, we propose the Adaptive Symmetric Reward Noising (ASRN), by which we mean adding Gaussian noise to rewards according to their states' estimated variance, thus avoiding the two problems while not affecting the environment's mean rewards behavior. We conduct our experiments in a Multi Armed Bandit problem with variance differences. We demonstrate that a Q-learning algorithm shows the brittleness effect in this problem, and that the ASRN scheme can dramatically improve the results. We show that ASRN helps a DQN algorithm training process reach better results in an end to end autonomous driving task using the AirSim driving simulator.
The Effectiveness of Uniform Sampling for Center-Based Clustering with Outliers
Authors: Hu Ding, Jiawei Huang, Haikuo Yu
Date: 2019-05-24 00:00:00
Tasks: Clustering

Clustering has many important applications in computer science, but real-world datasets often contain outliers. Moreover, the presence of outliers can make the clustering problems to be much more challenging. To reduce the complexities, various sampling methods have been proposed in past years. Namely, we take a small sample (uniformly or non-uniformly) from input and run an existing approximation algorithm on the sample. Comparing with existing non-uniform sampling methods, the uniform sampling approach has several significant benefits. For example, it only needs to read the data in one-pass and is very easy to implement in practice. Thus, the effectiveness of uniform sampling for clustering with outliers is a natural and fundamental problem deserving to study in both theory and practice. In this paper, we propose a new and unified framework for analyzing the effectiveness of uniform sampling for three representative center-based clustering with outliers problems, $k$-center/median/means clustering with outliers. We introduce a "significance" criterion and prove that the performance of uniform sampling depends on the significance degree of the given instance. In particular, we show that the sample size can be independent of the ratio $n/z$ and the dimensionality. More importantly, to the best of our knowledge, our method is the first uniform sampling approach that allows to discard exactly $z$ outliers for these three center-based clustering with outliers problems. The results proposed in this paper also can be viewed as an extension of the previous sub-linear time algorithms for the ordinary clustering problems (without outliers). The experiments suggest that the uniform sampling method can achieve comparable clustering results with other existing methods, but greatly reduce the running times.
FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks
Authors: Alberto Marchisio, Beatrice Bussolino, Alessio Colucci, Muhammad Abdullah Hanif, Maurizio Martina, Guido Masera, Muhammad Shafique
Date: 2019-05-24 00:00:00
Tasks: Image Classification, Object Detection

Recently, Capsule Networks (CapsNets) have shown improved performance compared to the traditional Convolutional Neural Networks (CNNs), by encoding and preserving spatial relationships between the detected features in a better way. This is achieved through the so-called Capsules (i.e., groups of neurons) that encode both the instantiation probability and the spatial information. However, one of the major hurdles in the wide adoption of CapsNets is their gigantic training time, which is primarily due to the relatively higher complexity of their new constituting elements that are different from CNNs. In this paper, we implement different optimizations in the training loop of the CapsNets, and investigate how these optimizations affect their training speed and the accuracy. Towards this, we propose a novel framework FasTrCaps that integrates multiple lightweight optimizations and a novel learning rate policy called WarmAdaBatch (that jointly performs warm restarts and adaptive batch size), and steers them in an appropriate way to provide high training-loop speedup at minimal accuracy loss. We also propose weight sharing for capsule layers. The goal is to reduce the hardware requirements of CapsNets by removing unused/redundant connections and capsules, while keeping high accuracy through tests of different learning rate policies and batch sizes. We demonstrate that one of the solutions generated by the FasTrCaps framework can achieve 58.6% reduction in the training time, while preserving the accuracy (even 0.12% accuracy improvement for the MNIST dataset), compared to the CapsNet by Google Brain. The Pareto-optimal solutions generated by FasTrCaps can be leveraged to realize trade-offs between training time and achieved accuracy. We have open-sourced our framework on https://github.com/Alexei95/FasTrCaps.
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization
Authors: Se Jung Kwon, Dongsoo Lee, Byeongwook Kim, Parichay Kapoor, Baeseong Park, Gu-Yeon Wei
Date: 2019-05-24 00:00:00
Proceeding: CVPR 2020 6
Tasks: Model Compression, Quantization

Model compression techniques, such as pruning and quantization, are becoming increasingly important to reduce the memory footprints and the amount of computations. Despite model size reduction, achieving performance enhancement on devices is, however, still challenging mainly due to the irregular representations of sparse matrix formats. This paper proposes a new weight representation scheme for Sparse Quantized Neural Networks, specifically achieved by fine-grained and unstructured pruning method. The representation is encrypted in a structured regular format, which can be efficiently decoded through XOR-gate network during inference in a parallel manner. We demonstrate various deep learning models that can be compressed and represented by our proposed format with fixed and high compression ratio. For example, for fully-connected layers of AlexNet on ImageNet dataset, we can represent the sparse weights by only 0.28 bits/weight for 1-bit quantization and 91% pruning rate with a fixed decoding rate and full memory bandwidth usage. Decoding through XOR-gate network can be performed without any model accuracy degradation with additional patch data associated with small overhead.
Sliced Gromov-Wasserstein
Authors: Titouan Vayer, Rémi Flamary, Romain Tavenard, Laetitia Chapel, Nicolas Courty
Date: 2019-05-24 00:00:00
Proceeding: NeurIPS 2019 12

Recently used in various machine learning contexts, the Gromov-Wasserstein distance (GW) allows for comparing distributions whose supports do not necessarily lie in the same metric space. However, this Optimal Transport (OT) distance requires solving a complex non convex quadratic program which is most of the time very costly both in time and memory. Contrary to GW, the Wasserstein distance (W) enjoys several properties (e.g. duality) that permit large scale optimization. Among those, the solution of W on the real line, that only requires sorting discrete samples in 1D, allows defining the Sliced Wasserstein (SW) distance. This paper proposes a new divergence based on GW akin to SW. We first derive a closed form for GW when dealing with 1D distributions, based on a new result for the related quadratic assignment problem. We then define a novel OT discrepancy that can deal with large scale distributions via a slicing approach and we show how it relates to the GW distance while being $O(n\log(n))$ to compute. We illustrate the behavior of this so called Sliced Gromov-Wasserstein (SGW) discrepancy in experiments where we demonstrate its ability to tackle similar problems as GW while being several order of magnitudes faster to compute.
Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion
Authors: Andreas Pfeuffer, Klaus Dietmayer
Date: 2019-05-24 00:00:00
Tasks: Semantic Segmentation, Sensor Fusion

A robust and reliable semantic segmentation in adverse weather conditions is very important for autonomous cars, but most state-of-the-art approaches only achieve high accuracy rates in optimal weather conditions. The reason is that they are only optimized for good weather conditions and given noise models. However, most of them fail, if data with unknown disturbances occur, and their performance decrease enormously. One possibility to still obtain reliable results is to observe the environment with different sensor types, such as camera and lidar, and to fuse the sensor data by means of neural networks, since different sensors behave differently in diverse weather conditions. Hence, the sensors can complement each other by means of an appropriate sensor data fusion. Nevertheless, the fusion-based approaches are still susceptible to disturbances and fail to classify disturbed image areas correctly. This problem can be solved by means of a special training method, the so called Robust Learning Method (RLM), a method by which the neural network learns to handle unknown noise. In this work, two different sensor fusion architectures for semantic segmentation are compared and evaluated on several datasets. Furthermore, it is shown that the RLM increases the robustness in adverse weather conditions enormously, and achieve good results although no disturbance model has been learned by the neural network.
Semi-Parametric Efficient Policy Learning with Continuous Actions
Authors: Mert Demirer, Vasilis Syrgkanis, Greg Lewis, Victor Chernozhukov
Date: 2019-05-24 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Off-policy evaluation

We consider off-policy evaluation and optimization with continuous action spaces. We focus on observational data where the data collection policy is unknown and needs to be estimated. We take a semi-parametric approach where the value function takes a known parametric form in the treatment, but we are agnostic on how it depends on the observed contexts. We propose a doubly robust off-policy estimate for this setting and show that off-policy optimization based on this estimate is robust to estimation errors of the policy function or the regression model. Our results also apply if the model does not satisfy our semi-parametric form, but rather we measure regret in terms of the best projection of the true value function to this functional space. Our work extends prior approaches of policy optimization from observational data that only considered discrete actions. We provide an experimental evaluation of our method in a synthetic data example motivated by optimal personalized pricing and costly resource allocation.
Multi-Kernel Correntropy for Robust Learning
Authors: Badong Chen, Yuqing Xie, Xin Wang, Zejian yuan, Pengju Ren, Jing Qin
Date: 2019-05-24 00:00:00

As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian kernel. In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely a linear combination of several zero-mean Gaussian kernels with different widths. In both correntropy and mixture correntropy, the center of the kernel function is, however, always located at zero. In the present work, to further improve the learning performance, we propose the concept of multi-kernel correntropy (MKC), in which each component of the mixture Gaussian kernel can be centered at a different location. The properties of the MKC are investigated and an efficient approach is proposed to determine the free parameters in MKC. Experimental results show that the learning algorithms under the maximum multi-kernel correntropy criterion (MMKCC) can outperform those under the original maximum correntropy criterion (MCC) and the maximum mixture correntropy criterion (MMCC).
Continual Reinforcement Learning in 3D Non-stationary Environments
Authors: Vincenzo Lomonaco, Karan Desai, Eugenio Culurciello, Davide Maltoni
Date: 2019-05-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

High-dimensional always-changing environments constitute a hard challenge for current reinforcement learning techniques. Artificial agents, nowadays, are often trained off-line in very static and controlled conditions in simulation such that training observations can be thought as sampled i.i.d. from the entire observations space. However, in real world settings, the environment is often non-stationary and subject to unpredictable, frequent changes. In this paper we propose and openly release CRLMaze, a new benchmark for learning continually through reinforcement in a complex 3D non-stationary task based on ViZDoom and subject to several environmental changes. Then, we introduce an end-to-end model-free continual reinforcement learning strategy showing competitive results with respect to four different baselines and not requiring any access to additional supervised signals, previously encountered environmental conditions or observations.
Learning Surrogate Losses
Authors: Josif Grabocka, Randolf Scholz, Lars Schmidt-Thieme
Date: 2019-05-24 00:00:00
Tasks: Bilevel Optimization, General Classification

The minimization of loss functions is the heart and soul of Machine Learning. In this paper, we propose an off-the-shelf optimization approach that can minimize virtually any non-differentiable and non-decomposable loss function (e.g. Miss-classification Rate, AUC, F1, Jaccard Index, Mathew Correlation Coefficient, etc.) seamlessly. Our strategy learns smooth relaxation versions of the true losses by approximating them through a surrogate neural network. The proposed loss networks are set-wise models which are invariant to the order of mini-batch instances. Ultimately, the surrogate losses are learned jointly with the prediction model via bilevel optimization. Empirical results on multiple datasets with diverse real-life loss functions compared with state-of-the-art baselines demonstrate the efficiency of learning surrogate losses.
Guided Stereo Matching
Authors: Matteo Poggi, Davide Pallotti, Fabio Tosi, Stefano Mattoccia
Date: 2019-05-24 00:00:00
Proceeding: CVPR 2019 6
Tasks: Stereo Matching, Stereo Matching Hand

Stereo is a prominent technique to infer dense depth maps from images, and deep learning further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when enough data is available for training. However, deep networks suffer from significant drops in accuracy when dealing with new environments. Therefore, in this paper, we introduce Guided Stereo Matching, a novel paradigm leveraging a small amount of sparse, yet reliable depth measurements retrieved from an external source enabling to ameliorate this weakness. The additional sparse cues required by our method can be obtained with any strategy (e.g., a LiDAR) and used to enhance features linked to corresponding disparity hypotheses. Our formulation is general and fully differentiable, thus enabling to exploit the additional sparse inputs in pre-trained deep stereo networks as well as for training a new instance from scratch. Extensive experiments on three standard datasets and two state-of-the-art deep architectures show that even with a small set of sparse input cues, i) the proposed paradigm enables significant improvements to pre-trained networks. Moreover, ii) training from scratch notably increases accuracy and robustness to domain shifts. Finally, iii) it is suited and effective even with traditional stereo algorithms such as SGM.
HDI-Forest: Highest Density Interval Regression Forest
Authors: Lin Zhu, Jiaxing Lu, Yihong Chen
Date: 2019-05-24 00:00:00
Tasks: Prediction Intervals, regression

By seeking the narrowest prediction intervals (PIs) that satisfy the specified coverage probability requirements, the recently proposed quality-based PI learning principle can extract high-quality PIs that better summarize the predictive certainty in regression tasks, and has been widely applied to solve many practical problems. Currently, the state-of-the-art quality-based PI estimation methods are based on deep neural networks or linear models. In this paper, we propose Highest Density Interval Regression Forest (HDI-Forest), a novel quality-based PI estimation method that is instead based on Random Forest. HDI-Forest does not require additional model training, and directly reuses the trees learned in a standard Random Forest model. By utilizing the special properties of Random Forest, HDI-Forest could efficiently and more directly optimize the PI quality metrics. Extensive experiments on benchmark datasets show that HDI-Forest significantly outperforms previous approaches, reducing the average PI width by over 20% while achieving the same or better coverage probability
Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing
Authors: Yang Lu, Xiaohui Liang, Frederick W. B. Li
Date: 2019-05-24 00:00:00
Tasks: Multi-class Classification, Scene Parsing

Recently, fully convolutional neural networks (FCNs) have shown significant performance in image parsing, including scene parsing and object parsing. Different from generic object parsing tasks, hand parsing is more challenging due to small size, complex structure, heavy self-occlusion and ambiguous texture problems. In this paper, we propose a novel parsing framework, Multi-Scale Dual-Branch Fully Convolutional Network (MSDB-FCN), for hand parsing tasks. Our network employs a Dual-Branch architecture to extract features of hand area, paying attention on the hand itself. These features are used to generate multi-scale features with pyramid pooling strategy. In order to better encode multi-scale features, we design a Deconvolution and Bilinear Interpolation Block (DB-Block) for upsampling and merging the features of different scales. To address data imbalance, which is a common problem in many computer vision tasks as well as hand parsing tasks, we propose a generalization of Focal Loss, namely Multi-Class Balanced Focal Loss, to tackle data imbalance in multi-class classification. Extensive experiments on RHD-PARSING dataset demonstrate that our MSDB-FCN has achieved the state-of-the-art performance for hand parsing.
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
Authors: Boris Muzellec, Marco Cuturi
Date: 2019-05-24 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Domain Adaptation, Word Embeddings

Computing optimal transport (OT) between measures in high dimensions is doomed by the curse of dimensionality. A popular approach to avoid this curse is to project input measures on lower-dimensional subspaces (1D lines in the case of sliced Wasserstein distances), solve the OT problem between these reduced measures, and settle for the Wasserstein distance between these reductions, rather than that between the original measures. This approach is however difficult to extend to the case in which one wants to compute an OT map (a Monge map) between the original measures. Since computations are carried out on lower-dimensional projections, classical map estimation techniques can only produce maps operating in these reduced dimensions. We propose in this work two methods to extrapolate, from an transport map that is optimal on a subspace, one that is nearly optimal in the entire space. We prove that the best optimal transport plan that takes such "subspace detours" is a generalization of the Knothe-Rosenblatt transport. We show that these plans can be explicitly formulated when comparing Gaussian measures (between which the Wasserstein distance is commonly referred to as the Bures or Fr\'echet distance). We provide an algorithm to select optimal subspaces given pairs of Gaussian measures, and study scenarios in which that mediating subspace can be selected using prior information. We consider applications to semantic mediation between elliptic word embeddings and domain adaptation with Gaussian mixture models.
Learning Cross-Domain Representation with Multi-Graph Neural Network
Authors: Yi Ouyang, Bin Guo, Xing Tang, Xiuqiang He, Jian Xiong, Zhiwen Yu
Date: 2019-05-24 00:00:00
Tasks: Graph Embedding, Graph Neural Network, Recommendation Systems, Representation Learning

Learning effective embedding has been proved to be useful in many real-world problems, such as recommender systems, search ranking and online advertisement. However, one of the challenges is data sparsity in learning large-scale item embedding, as users' historical behavior data are usually lacking or insufficient in an individual domain. In fact, user's behaviors from different domains regarding the same items are usually relevant. Therefore, we can learn complete user behaviors to alleviate the sparsity using complementary information from correlated domains. It is intuitive to model users' behaviors using graph, and graph neural networks (GNNs) have recently shown the great power for representation learning, which can be used to learn item embedding. However, it is challenging to transfer the information across domains and learn cross-domain representation using the existing GNNs. To address these challenges, in this paper, we propose a novel model - Deep Multi-Graph Embedding (DMGE) to learn cross-domain representation. Specifically, we first construct a multi-graph based on users' behaviors from different domains, and then propose a multi-graph neural network to learn cross-domain representation in an unsupervised manner. Particularly, we present a multiple-gradient descent optimizer for efficiently training the model. We evaluate our approach on various large-scale real-world datasets, and the experimental results show that DMGE outperforms other state-of-art embedding methods in various tasks.