April 3, 2020

3149 words 15 mins read

Paper Group ANR 58

PIC: Permutation Invariant Convolution for Recognizing Long-range Activities. Games Where You Can Play Optimally with Finite Memory. Complete Dictionary Learning via $\ell_p$-norm Maximization. Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program). Distal Explanations for Explainable Reinfor …

PIC: Permutation Invariant Convolution for Recognizing Long-range Activities


Title	PIC: Permutation Invariant Convolution for Recognizing Long-range Activities
Authors	Noureldien Hussein, Efstratios Gavves, Arnold W. M. Smeulders
Abstract	Neural operations as convolutions, self-attention, and vector aggregation are the go-to choices for recognizing short-range actions. However, they have three limitations in modeling long-range activities. This paper presents PIC, Permutation Invariant Convolution, a novel neural layer to model the temporal structure of long-range activities. It has three desirable properties. i. Unlike standard convolution, PIC is invariant to the temporal permutations of features within its receptive field, qualifying it to model the weak temporal structures. ii. Different from vector aggregation, PIC respects local connectivity, enabling it to learn long-range temporal abstractions using cascaded layers. iii. In contrast to self-attention, PIC uses shared weights, making it more capable of detecting the most discriminant visual evidence across long and noisy videos. We study the three properties of PIC and demonstrate its effectiveness in recognizing the long-range activities of Charades, Breakfast, and MultiThumos.
Tasks
Published	2020-03-18
URL	https://arxiv.org/abs/2003.08275v1
PDF	https://arxiv.org/pdf/2003.08275v1.pdf
PWC	https://paperswithcode.com/paper/pic-permutation-invariant-convolution-for
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Games Where You Can Play Optimally with Finite Memory


Title	Games Where You Can Play Optimally with Finite Memory
Authors	Patricia Bouyer, Stéphane Le Roux, Youssouf Oualhadj, Mickael Randour, Pierre Vandenhove
Abstract	For decades, two-player (antagonistic) games on graphs have been a framework of choice for many important problems in theoretical computer science. A notorious one is controller synthesis, which can be rephrased through the game-theoretic metaphor as the quest for a winning strategy of the system in a game against its antagonistic environment. Depending on the specification, optimal strategies might be simple or quite complex, for example having to use (possibly infinite) memory. Hence, research strives to understand which settings allow for simple strategies. In 2005, Gimbert and Zielonka provided a complete characterization of preference relations (a formal framework to model specifications and game objectives) that admit memoryless optimal strategies for both players. In the last fifteen years however, practical applications have driven the community toward games with complex or multiple objectives, where memory — finite or infinite — is almost always required. Despite much effort, the exact frontiers of the class of preference relations that admit finite-memory optimal strategies still elude us. In this work, we establish a complete characterization of preference relations that admit optimal strategies using arena-independent finite memory, generalizing the work of Gimbert and Zielonka to the finite-memory case. We also prove an equivalent to their celebrated corollary of utmost practical interest: if both players have optimal (arena-independent-)finite-memory strategies in all one-player games, then it is also the case in all two-player games. Finally, we pinpoint the boundaries of our results with regard to the literature: our work completely covers the case of arena-independent memory (e.g., multiple parity objectives, lower- and upper-bounded energy objectives), and paves the way to the arena-dependent case (e.g., multiple lower-bounded energy objectives).
Tasks
Published	2020-01-12
URL	https://arxiv.org/abs/2001.03894v1
PDF	https://arxiv.org/pdf/2001.03894v1.pdf
PWC	https://paperswithcode.com/paper/games-where-you-can-play-optimally-with
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Complete Dictionary Learning via $\ell_p$-norm Maximization


Title	Complete Dictionary Learning via $\ell_p$-norm Maximization
Authors	Yifei Shen, Ye Xue, Jun Zhang, Khaled B. Letaief, Vincent Lau
Abstract	Dictionary learning is a classic representation learning method that has been widely applied in signal processing and data analytics. In this paper, we investigate a family of $\ell_p$-norm ($p>2,p \in \mathbb{N}$) maximization approaches for the complete dictionary learning problem from theoretical and algorithmic aspects. Specifically, we prove that the global maximizers of these formulations are very close to the true dictionary with high probability, even when Gaussian noise is present. Based on the generalized power method (GPM), an efficient algorithm is then developed for the $\ell_p$-based formulations. We further show the efficacy of the developed algorithm: for the population GPM algorithm over the sphere constraint, it first quickly enters the neighborhood of a global maximizer, and then converges linearly in this region. Extensive experiments will demonstrate that the $\ell_p$-based approaches enjoy a higher computational efficiency and better robustness than conventional approaches and $p=3$ performs the best.
Tasks	Dictionary Learning, Representation Learning
Published	2020-02-24
URL	https://arxiv.org/abs/2002.10043v2
PDF	https://arxiv.org/pdf/2002.10043v2.pdf
PWC	https://paperswithcode.com/paper/complete-dictionary-learning-via-ell_p-norm
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Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)


Title	Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
Authors	Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d’Alché-Buc, Emily Fox, Hugo Larochelle
Abstract	One of the challenges in machine learning research is to ensure that presented and published results are sound and reliable. Reproducibility, that is obtaining similar results as presented in a paper or talk, using the same code and data (when available), is a necessary step to verify the reliability of research findings. Reproducibility is also an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. Reproducibility also promotes the use of robust experimental workflows, which potentially reduce unintentional errors. In 2019, the Neural Information Processing Systems (NeurIPS) conference, the premier international conference for research in machine learning, introduced a reproducibility program, designed to improve the standards across the community for how we conduct, communicate, and evaluate machine learning research. The program contained three components: a code submission policy, a community-wide reproducibility challenge, and the inclusion of the Machine Learning Reproducibility checklist as part of the paper submission process. In this paper, we describe each of these components, how it was deployed, as well as what we were able to learn from this initiative.
Tasks
Published	2020-03-27
URL	https://arxiv.org/abs/2003.12206v2
PDF	https://arxiv.org/pdf/2003.12206v2.pdf
PWC	https://paperswithcode.com/paper/improving-reproducibility-in-machine-learning
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Distal Explanations for Explainable Reinforcement Learning Agents


Title	Distal Explanations for Explainable Reinforcement Learning Agents
Authors	Prashan Madumal, Tim Miller, Liz Sonenberg, Frank Vetere
Abstract	Causal explanations present an intuitive way to understand the course of events through causal chains, and are widely accepted in cognitive science as the prominent model humans use for explanation. Importantly, causal models can generate opportunity chains, which take the form of `A enables B and B causes C’. We ground the notion of opportunity chains in human-agent experimental data, where we present participants with explanations from different models and ask them to provide their own explanations for agent behaviour. Results indicate that humans do in-fact use the concept of opportunity chains frequently for describing artificial agent behaviour. Recently, action influence models have been proposed to provide causal explanations for model-free reinforcement learning (RL). While these models can generate counterfactuals—things that did not happen but could have under different conditions—they lack the ability to generate explanations of opportunity chains. We introduce a distal explanation model that can analyse counterfactuals and opportunity chains using decision trees and causal models. We employ a recurrent neural network to learn opportunity chains and make use of decision trees to improve the accuracy of task prediction and the generated counterfactuals. We computationally evaluate the model in 6 RL benchmarks using different RL algorithms, and show that our model performs better in task prediction. We report on a study with 90 participants who receive explanations of RL agents behaviour in solving three scenarios: 1) Adversarial; 2) Search and rescue; and 3) Human-Agent collaborative scenarios. We investigate the participants’ understanding of the agent through task prediction and their subjective satisfaction of the explanations and show that our distal explanation model results in improved outcomes over the three scenarios compared with two baseline explanation models. \|
Tasks
Published	2020-01-28
URL	https://arxiv.org/abs/2001.10284v1
PDF	https://arxiv.org/pdf/2001.10284v1.pdf
PWC	https://paperswithcode.com/paper/distal-explanations-for-explainable
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Manifold-Aware CycleGAN for High Resolution Structural-to-DTI Synthesis


Title	Manifold-Aware CycleGAN for High Resolution Structural-to-DTI Synthesis
Authors	Benoit Anctil-Robitaille, Christian Desrosiers, Herve Lombaert
Abstract	Unpaired image-to-image translation has been applied successfully to natural images but has received very little attention for manifold-valued data such as in diffusion tensor imaging (DTI). The non-Euclidean nature of DTI prevents current generative adversarial networks (GANs) from generating plausible images and has mostly limited their application to diffusion MRI scalar maps, such as fractional anisotropy (FA) or mean diffusivity (MD). Even if these scalar maps are clinically useful, they mostly ignore fiber orientations and have, therefore, limited applications for analyzing brain fibers, for instance, impairing fiber tractography. Here, we propose a manifold-aware CycleGAN that learns the generation of high resolution DTI from unpaired T1w images. We formulate the objective as a Wasserstein distance minimization problem of data distributions on a Riemannian manifold of symmetric positive definite 3x3 matrices SPD(3), using adversarial and cycle-consistency losses. To ensure that the generated diffusion tensors lie on the SPD(3) manifold, we exploit the theoretical properties of the exponential and logarithm maps. We demonstrate that, unlike standard GANs, our method is able to generate realistic high resolution DTI that can be used to compute diffusion-based metrics and run fiber tractography algorithms. To evaluate our model’s performance, we compute the cosine similarity between the generated tensors principal orientation and their ground truth orientation and the mean squared error (MSE) of their derived FA values. We demonstrate that our method produces up to 8 times better FA MSE than a standard CycleGAN and 30% better cosine similarity than a manifold-aware Wasserstein GAN while synthesizing sharp high resolution DTI.
Tasks	Image-to-Image Translation
Published	2020-04-01
URL	https://arxiv.org/abs/2004.00173v1
PDF	https://arxiv.org/pdf/2004.00173v1.pdf
PWC	https://paperswithcode.com/paper/manifold-aware-cyclegan-for-high-resolution
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A new approach in model selection for ordinal target variables


Title	A new approach in model selection for ordinal target variables
Authors	Elena Ballante, Pierpaolo Uberti, Silvia Figini
Abstract	This paper introduces a novel approach to assess model performance for predictive models characterized by an ordinal target variable in order to satisfy the lack of suitable tools in this framework. Our methodological proposal is a new index for model assessment which satisfies mathematical properties and can be easily computed. In order to show how our performance indicator works, empirical evidence achieved on a toy examples and simulated data are provided. On the basis of results at hand, we underline that our approach discriminates better for model selection with respect to performance indexes proposed in the literature.
Tasks	Model Selection
Published	2020-03-05
URL	https://arxiv.org/abs/2003.02761v1
PDF	https://arxiv.org/pdf/2003.02761v1.pdf
PWC	https://paperswithcode.com/paper/a-new-approach-in-model-selection-for-ordinal
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Fully-Corrective Gradient Boosting with Squared Hinge: Fast Learning Rates and Early Stopping


Title	Fully-Corrective Gradient Boosting with Squared Hinge: Fast Learning Rates and Early Stopping
Authors	Jinshan Zeng, Min Zhang, Shao-Bo Lin
Abstract	Boosting is a well-known method for improving the accuracy of weak learners in machine learning. However, its theoretical generalization guarantee is missing in literature. In this paper, we propose an efficient boosting method with theoretical generalization guarantees for binary classification. Three key ingredients of the proposed boosting method are: a) the \textit{fully-corrective greedy} (FCG) update in the boosting procedure, b) a differentiable \textit{squared hinge} (also called \textit{truncated quadratic}) function as the loss function, and c) an efficient alternating direction method of multipliers (ADMM) algorithm for the associated FCG optimization. The used squared hinge loss not only inherits the robustness of the well-known hinge loss for classification with outliers, but also brings some benefits for computational implementation and theoretical justification. Under some sparseness assumption, we derive a fast learning rate of the order ${\cal O}((m/\log m)^{-1/4})$ for the proposed boosting method, which can be further improved to ${\cal O}((m/\log m)^{-1/2})$ if certain additional noise assumption is imposed, where $m$ is the size of sample set. Both derived learning rates are the best ones among the existing generalization results of boosting-type methods for classification. Moreover, an efficient early stopping scheme is provided for the proposed method. A series of toy simulations and real data experiments are conducted to verify the developed theories and demonstrate the effectiveness of the proposed method.
Tasks
Published	2020-04-01
URL	https://arxiv.org/abs/2004.00179v1
PDF	https://arxiv.org/pdf/2004.00179v1.pdf
PWC	https://paperswithcode.com/paper/fully-corrective-gradient-boosting-with
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FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation


Title	FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation
Authors	Bei Wang, Jianping An
Abstract	This paper addresses the importance of full-image supervision for monocular depth estimation. We propose a semi-supervised architecture, which combines both unsupervised framework of using image consistency and supervised framework of dense depth completion. The latter provides full-image depth as supervision for the former. Ego-motion from navigation system is also embedded into the unsupervised framework as output supervision of an inner temporal transform network, making monocular depth estimation better. In the evaluation, we show that our proposed model outperforms other approaches on depth estimation.
Tasks	Depth Completion, Depth Estimation, Monocular Depth Estimation
Published	2020-01-19
URL	https://arxiv.org/abs/2001.11092v1
PDF	https://arxiv.org/pdf/2001.11092v1.pdf
PWC	https://paperswithcode.com/paper/fis-nets-full-image-supervised-networks-for
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DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation


Title	DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation
Authors	Xiong Zhang, Hongmin Xu, Hong Mo, Jianchao Tan, Cheng Yang, Wenqi Ren
Abstract	Neural Architecture Search (NAS) has shown great potentials in automatically designing scalable network architectures for dense image predictions. However, existing NAS algorithms usually compromise on restricted search space and search on proxy task to meet the achievable computational demands. To allow as wide as possible network architectures and avoid the gap between target and proxy dataset, we propose a Densely Connected NAS (DCNAS) framework, which directly searches the optimal network structures for the multi-scale representations of visual information, over a large-scale target dataset. Specifically, by connecting cells with each other using learnable weights, we introduce a densely connected search space to cover an abundance of mainstream network designs. Moreover, by combining both path-level and channel-level sampling strategies, we design a fusion module to reduce the memory consumption of ample search space. We demonstrate that the architecture obtained from our DCNAS algorithm achieves state-of-the-art performances on public semantic image segmentation benchmarks, including 83.6% on Cityscapes, and 86.9% on PASCAL VOC 2012 (track w/o additional data). We also retain leading performances when evaluating the architecture on the more challenging ADE20K and Pascal Context dataset.
Tasks	Neural Architecture Search, Semantic Segmentation
Published	2020-03-26
URL	https://arxiv.org/abs/2003.11883v1
PDF	https://arxiv.org/pdf/2003.11883v1.pdf
PWC	https://paperswithcode.com/paper/dcnas-densely-connected-neural-architecture
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Budget-Constrained Bandits over General Cost and Reward Distributions


Title	Budget-Constrained Bandits over General Cost and Reward Distributions
Authors	Semih Cayci, Atilla Eryilmaz, R. Srikant
Abstract	We consider a budget-constrained bandit problem where each arm pull incurs a random cost, and yields a random reward in return. The objective is to maximize the total expected reward under a budget constraint on the total cost. The model is general in the sense that it allows correlated and potentially heavy-tailed cost-reward pairs that can take on negative values as required by many applications. We show that if moments of order $(2+\gamma)$ for some $\gamma > 0$ exist for all cost-reward pairs, $O(\log B)$ regret is achievable for a budget $B>0$. In order to achieve tight regret bounds, we propose algorithms that exploit the correlation between the cost and reward of each arm by extracting the common information via linear minimum mean-square error estimation. We prove a regret lower bound for this problem, and show that the proposed algorithms achieve tight problem-dependent regret bounds, which are optimal up to a universal constant factor in the case of jointly Gaussian cost and reward pairs.
Tasks
Published	2020-02-29
URL	https://arxiv.org/abs/2003.00365v1
PDF	https://arxiv.org/pdf/2003.00365v1.pdf
PWC	https://paperswithcode.com/paper/budget-constrained-bandits-over-general-cost
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TPLVM: Portfolio Construction by Student’s $t$-process Latent Variable Model


Title	TPLVM: Portfolio Construction by Student’s $t$-process Latent Variable Model
Authors	Yusuke Uchiyama, Kei Nakagawa
Abstract	Optimal asset allocation is a key topic in modern finance theory. To realize the optimal asset allocation on investor’s risk aversion, various portfolio construction methods have been proposed. Recently, the applications of machine learning are rapidly growing in the area of finance. In this article, we propose the Student’s $t$-process latent variable model (TPLVM) to describe non-Gaussian fluctuations of financial timeseries by lower dimensional latent variables. Subsequently, we apply the TPLVM to minimum-variance portfolio as an alternative of existing nonlinear factor models. To test the performance of the proposed portfolio, we construct minimum-variance portfolios of global stock market indices based on the TPLVM or Gaussian process latent variable model. By comparing these portfolios, we confirm the proposed portfolio outperforms that of the existing Gaussian process latent variable model.
Tasks
Published	2020-01-29
URL	https://arxiv.org/abs/2002.06243v1
PDF	https://arxiv.org/pdf/2002.06243v1.pdf
PWC	https://paperswithcode.com/paper/tplvm-portfolio-construction-by-students-t
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Evaluating approval-based multiwinner voting in terms of robustness to noise


Title	Evaluating approval-based multiwinner voting in terms of robustness to noise
Authors	Ioannis Caragiannis, Christos Kaklamanis, Nikos Karanikolas, George A. Krimpas
Abstract	Approval-based multiwinner voting rules have recently received much attention in the Computational Social Choice literature. Such rules aggregate approval ballots and determine a winning committee of alternatives. To assess effectiveness, we propose to employ new noise models that are specifically tailored for approval votes and committees. These models take as input a ground truth committee and return random approval votes to be thought of as noisy estimates of the ground truth. A minimum robustness requirement for an approval-based multiwinner voting rule is to return the ground truth when applied to profiles with sufficiently many noisy votes. Our results indicate that approval-based multiwinner voting is always robust to reasonable noise. We further refine this finding by presenting a hierarchy of rules in terms of how robust to noise they are.
Tasks
Published	2020-02-05
URL	https://arxiv.org/abs/2002.01776v1
PDF	https://arxiv.org/pdf/2002.01776v1.pdf
PWC	https://paperswithcode.com/paper/evaluating-approval-based-multiwinner-voting
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Introduction to deep learning


Title	Introduction to deep learning
Authors	Lihi Shiloh-Perl, Raja Giryes
Abstract	Deep Learning (DL) has made a major impact on data science in the last decade. This chapter introduces the basic concepts of this field. It includes both the basic structures used to design deep neural networks and a brief survey of some of its popular use cases.
Tasks
Published	2020-02-29
URL	https://arxiv.org/abs/2003.03253v1
PDF	https://arxiv.org/pdf/2003.03253v1.pdf
PWC	https://paperswithcode.com/paper/introduction-to-deep-learning
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Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach


Title	Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach
Authors	Upol Ehsan, Mark O. Riedl
Abstract	Explanations–a form of post-hoc interpretability–play an instrumental role in making systems accessible as AI continues to proliferate complex and sensitive sociotechnical systems. In this paper, we introduce Human-centered Explainable AI (HCXAI) as an approach that puts the human at the center of technology design. It develops a holistic understanding of “who” the human is by considering the interplay of values, interpersonal dynamics, and the socially situated nature of AI systems. In particular, we advocate for a reflective sociotechnical approach. We illustrate HCXAI through a case study of an explanation system for non-technical end-users that shows how technical advancements and the understanding of human factors co-evolve. Building on the case study, we lay out open research questions pertaining to further refining our understanding of “who” the human is and extending beyond 1-to-1 human-computer interactions. Finally, we propose that a reflective HCXAI paradigm-mediated through the perspective of Critical Technical Practice and supplemented with strategies from HCI, such as value-sensitive design and participatory design–not only helps us understand our intellectual blind spots, but it can also open up new design and research spaces.
Tasks
Published	2020-02-04
URL	https://arxiv.org/abs/2002.01092v2
PDF	https://arxiv.org/pdf/2002.01092v2.pdf
PWC	https://paperswithcode.com/paper/human-centered-explainable-ai-towards-a
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