October 20, 2019

3341 words 16 mins read

Paper Group ANR 53

A Computational Model of Commonsense Moral Decision Making. CoBaR: Confidence-Based Recommender. A General Dichotomy of Evolutionary Algorithms on Monotone Functions. Predicting Group Cohesiveness in Images. A Sparse Coding Multi-Scale Precise-Timing Machine Learning Algorithm for Neuromorphic Event-Based Sensors. ABMOF: A Novel Optical Flow Algori …

A Computational Model of Commonsense Moral Decision Making


Title	A Computational Model of Commonsense Moral Decision Making
Authors	Richard Kim, Max Kleiman-Weiner, Andres Abeliuk, Edmond Awad, Sohan Dsouza, Josh Tenenbaum, Iyad Rahwan
Abstract	We introduce a new computational model of moral decision making, drawing on a recent theory of commonsense moral learning via social dynamics. Our model describes moral dilemmas as a utility function that computes trade-offs in values over abstract moral dimensions, which provide interpretable parameter values when implemented in machine-led ethical decision-making. Moreover, characterizing the social structures of individuals and groups as a hierarchical Bayesian model, we show that a useful description of an individual’s moral values - as well as a group’s shared values - can be inferred from a limited amount of observed data. Finally, we apply and evaluate our approach to data from the Moral Machine, a web application that collects human judgments on moral dilemmas involving autonomous vehicles.
Tasks	Autonomous Vehicles, Decision Making
Published	2018-01-12
URL	http://arxiv.org/abs/1801.04346v1
PDF	http://arxiv.org/pdf/1801.04346v1.pdf
PWC	https://paperswithcode.com/paper/a-computational-model-of-commonsense-moral
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CoBaR: Confidence-Based Recommender


Title	CoBaR: Confidence-Based Recommender
Authors	Fernando S. Aguiar Neto, Arthur F. da Costa, Marcelo G. Manzato
Abstract	Neighborhood-based collaborative filtering algorithms usually adopt a fixed neighborhood size for every user or item, although groups of users or items may have different lengths depending on users’ preferences. In this paper, we propose an extension to a non-personalized recommender based on confidence intervals and hierarchical clustering to generate groups of users with optimal sizes. The evaluation shows that the proposed technique outperformed the traditional recommender algorithms in four publicly available datasets.
Tasks
Published	2018-08-21
URL	http://arxiv.org/abs/1808.07089v1
PDF	http://arxiv.org/pdf/1808.07089v1.pdf
PWC	https://paperswithcode.com/paper/cobar-confidence-based-recommender
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A General Dichotomy of Evolutionary Algorithms on Monotone Functions


Title	A General Dichotomy of Evolutionary Algorithms on Monotone Functions
Authors	Johannes Lengler
Abstract	It is known that the evolutionary algorithm $(1+1)$-EA with mutation rate $c/n$ optimises every monotone function efficiently if $c<1$, and needs exponential time on some monotone functions (HotTopic functions) if $c\geq 2.2$. We study the same question for a large variety of algorithms, particularly for $(1+\lambda)$-EA, $(\mu+1)$-EA, $(\mu+1)$-GA, their fast counterparts like fast $(1+1)$-EA, and for $(1+(\lambda,\lambda))$-GA. We find that all considered mutation-based algorithms show a similar dichotomy for HotTopic functions, or even for all monotone functions. For the $(1+(\lambda,\lambda))$-GA, this dichotomy is in the parameter $c\gamma$, which is the expected number of bit flips in an individual after mutation and crossover, neglecting selection. For the fast algorithms, the dichotomy is in $m_2/m_1$, where $m_1$ and $m_2$ are the first and second falling moment of the number of bit flips. Surprisingly, the range of efficient parameters is not affected by either population size $\mu$ nor by the offspring population size $\lambda$. The picture changes completely if crossover is allowed. The genetic algorithms $(\mu+1)$-GA and fast $(\mu+1)$-GA are efficient for arbitrary mutations strengths if $\mu$ is large enough.
Tasks
Published	2018-03-25
URL	http://arxiv.org/abs/1803.09227v2
PDF	http://arxiv.org/pdf/1803.09227v2.pdf
PWC	https://paperswithcode.com/paper/a-general-dichotomy-of-evolutionary
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Predicting Group Cohesiveness in Images


Title	Predicting Group Cohesiveness in Images
Authors	Shreya Ghosh, Abhinav Dhall, Nicu Sebe, Tom Gedeon
Abstract	The cohesiveness of a group is an essential indicator of the emotional state, structure and success of a group of people. We study the factors that influence the perception of group-level cohesion and propose methods for estimating the human-perceived cohesion on the group cohesiveness scale. In order to identify the visual cues (attributes) for cohesion, we conducted a user survey. Image analysis is performed at a group-level via a multi-task convolutional neural network. For analyzing the contribution of facial expressions of the group members for predicting the Group Cohesion Score (GCS), a capsule network is explored. We add GCS to the Group Affect database and propose the `GAF-Cohesion database’. The proposed model performs well on the database and is able to achieve near human-level performance in predicting a group’s cohesion score. It is interesting to note that group cohesion as an attribute, when jointly trained for group-level emotion prediction, helps in increasing the performance for the later task. This suggests that group-level emotion and cohesion are correlated. \|
Tasks
Published	2018-12-31
URL	http://arxiv.org/abs/1812.11771v4
PDF	http://arxiv.org/pdf/1812.11771v4.pdf
PWC	https://paperswithcode.com/paper/predicting-group-cohesiveness-in-images
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A Sparse Coding Multi-Scale Precise-Timing Machine Learning Algorithm for Neuromorphic Event-Based Sensors


Title	A Sparse Coding Multi-Scale Precise-Timing Machine Learning Algorithm for Neuromorphic Event-Based Sensors
Authors	Germain Haessig, Ryad Benosman
Abstract	This paper introduces an unsupervised compact architecture that can extract features and classify the contents of dynamic scenes from the temporal output of a neuromorphic asynchronous event-based camera. Event-based cameras are clock-less sensors where each pixel asynchronously reports intensity changes encoded in time at the microsecond precision. While this technology is gaining more attention, there is still a lack of methodology and understanding of their temporal properties. This paper introduces an unsupervised time-oriented event-based machine learning algorithm building on the concept of hierarchy of temporal descriptors called time surfaces. In this work we show that the use of sparse coding allows for a very compact yet efficient time-based machine learning that lowers both the computational cost and memory need. We show that we can represent visual scene temporal dynamics with a finite set of elementary time surfaces while providing similar recognition rates as an uncompressed version by storing the most representative time surfaces using clustering techniques. Experiments will illustrate the main optimizations and trade-offs to consider when implementing the method for online continuous vs. offline learning. We report results on the same previously published 36 class character recognition task and a 4 class canonical dynamic card pip task, achieving 100% accuracy on each.
Tasks
Published	2018-04-24
URL	http://arxiv.org/abs/1804.09236v1
PDF	http://arxiv.org/pdf/1804.09236v1.pdf
PWC	https://paperswithcode.com/paper/a-sparse-coding-multi-scale-precise-timing
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ABMOF: A Novel Optical Flow Algorithm for Dynamic Vision Sensors


Title	ABMOF: A Novel Optical Flow Algorithm for Dynamic Vision Sensors
Authors	Min Liu, Tobi Delbruck
Abstract	Dynamic Vision Sensors (DVS), which output asynchronous log intensity change events, have potential applications in high-speed robotics, autonomous cars and drones. The precise event timing, sparse output, and wide dynamic range of the events are well suited for optical flow, but conventional optical flow (OF) algorithms are not well matched to the event stream data. This paper proposes an event-driven OF algorithm called adaptive block-matching optical flow (ABMOF). ABMOF uses time slices of accumulated DVS events. The time slices are adaptively rotated based on the input events and OF results. Compared with other methods such as gradient-based OF, ABMOF can efficiently be implemented in compact logic circuits. Results show that ABMOF achieves comparable accuracy to conventional standards such as Lucas-Kanade (LK). The main contributions of our paper are new adaptive time-slice rotation methods that ensure the generated slices have sufficient features for matching,including a feedback mechanism that controls the generated slices to have average slice displacement within the block search range. An LK method using our adapted slices is also implemented. The ABMOF accuracy is compared with this LK method on natural scene data including sparse and dense texture, high dynamic range, and fast motion exceeding 30,000 pixels per second.The paper dataset and source code are available from http://sensors.ini.uzh.ch/databases.html.
Tasks	Optical Flow Estimation
Published	2018-05-10
URL	http://arxiv.org/abs/1805.03988v1
PDF	http://arxiv.org/pdf/1805.03988v1.pdf
PWC	https://paperswithcode.com/paper/abmof-a-novel-optical-flow-algorithm-for
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See before you see: Real-time high speed motion prediction using fast aperture-robust event-driven visual flow


Title	See before you see: Real-time high speed motion prediction using fast aperture-robust event-driven visual flow
Authors	Himanshu Akolkar, SioHoi Ieng, Ryad Benosman
Abstract	Optical flow is a crucial component of the feature space for early visual processing of dynamic scenes especially in new applications such as self-driving vehicles, drones and autonomous robots. The dynamic vision sensors are well suited for such applications because of their asynchronous, sparse and temporally precise representation of the visual dynamics. Many algorithms proposed for computing visual flow for these sensors suffer from the aperture problem as the direction of the estimated flow is governed by the curvature of the object rather than the true motion direction. Some methods that do overcome this problem by temporal windowing under-utilize the true precise temporal nature of the dynamic sensors. In this paper, we propose a novel multi-scale plane fitting based visual flow algorithm that is robust to the aperture problem and also computationally fast and efficient. Our algorithm performs well in many scenarios ranging from fixed camera recording simple geometric shapes to real world scenarios such as camera mounted on a moving car and can successfully perform event-by-event motion estimation of objects in the scene to allow for predictions of upto 500 ms i.e. equivalent to 10 to 25 frames with traditional cameras.
Tasks	Motion Estimation, motion prediction, Optical Flow Estimation
Published	2018-11-27
URL	http://arxiv.org/abs/1811.11135v1
PDF	http://arxiv.org/pdf/1811.11135v1.pdf
PWC	https://paperswithcode.com/paper/see-before-you-see-real-time-high-speed
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Multi-hierarchical Independent Correlation Filters for Visual Tracking


Title	Multi-hierarchical Independent Correlation Filters for Visual Tracking
Authors	Shuai Bai, Zhiqun He, Ting-Bing Xu, Zheng Zhu, Yuan Dong, Hongliang Bai
Abstract	For visual tracking, most of the traditional correlation filters (CF) based methods suffer from the bottleneck of feature redundancy and lack of motion information. In this paper, we design a novel tracking framework, called multi-hierarchical independent correlation filters (MHIT). The framework consists of motion estimation module, hierarchical features selection, independent CF online learning, and adaptive multi-branch CF fusion. Specifically, the motion estimation module is introduced to capture motion information, which effectively alleviates the object partial occlusion in the temporal video. The multi-hierarchical deep features of CNN representing different semantic information can be fully excavated to track multi-scale objects. To better overcome the deep feature redundancy, each hierarchical features are independently fed into a single branch to implement the online learning of parameters. Finally, an adaptive weight scheme is integrated into the framework to fuse these independent multi-branch CFs for the better and more robust visual object tracking. Extensive experiments on OTB and VOT datasets show that the proposed MHIT tracker can significantly improve the tracking performance. Especially, it obtains a 20.1% relative performance gain compared to the top trackers on the VOT2017 challenge, and also achieves new state-of-the-art performance on the VOT2018 challenge.
Tasks	Motion Estimation, Object Tracking, Visual Object Tracking, Visual Tracking
Published	2018-11-26
URL	http://arxiv.org/abs/1811.10302v2
PDF	http://arxiv.org/pdf/1811.10302v2.pdf
PWC	https://paperswithcode.com/paper/multi-hierarchical-independent-correlation
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Learning under Misspecified Objective Spaces


Title	Learning under Misspecified Objective Spaces
Authors	Andreea Bobu, Andrea Bajcsy, Jaime F. Fisac, Anca D. Dragan
Abstract	Learning robot objective functions from human input has become increasingly important, but state-of-the-art techniques assume that the human’s desired objective lies within the robot’s hypothesis space. When this is not true, even methods that keep track of uncertainty over the objective fail because they reason about which hypothesis might be correct, and not whether any of the hypotheses are correct. We focus specifically on learning from physical human corrections during the robot’s task execution, where not having a rich enough hypothesis space leads to the robot updating its objective in ways that the person did not actually intend. We observe that such corrections appear irrelevant to the robot, because they are not the best way of achieving any of the candidate objectives. Instead of naively trusting and learning from every human interaction, we propose robots learn conservatively by reasoning in real time about how relevant the human’s correction is for the robot’s hypothesis space. We test our inference method in an experiment with human interaction data, and demonstrate that this alleviates unintended learning in an in-person user study with a 7DoF robot manipulator.
Tasks
Published	2018-10-11
URL	http://arxiv.org/abs/1810.05157v4
PDF	http://arxiv.org/pdf/1810.05157v4.pdf
PWC	https://paperswithcode.com/paper/learning-under-misspecified-objective-spaces
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Compressing Neural Networks using the Variational Information Bottleneck


Title	Compressing Neural Networks using the Variational Information Bottleneck
Authors	Bin Dai, Chen Zhu, David Wipf
Abstract	Neural networks can be compressed to reduce memory and computational requirements, or to increase accuracy by facilitating the use of a larger base architecture. In this paper we focus on pruning individual neurons, which can simultaneously trim model size, FLOPs, and run-time memory. To improve upon the performance of existing compression algorithms we utilize the information bottleneck principle instantiated via a tractable variational bound. Minimization of this information theoretic bound reduces the redundancy between adjacent layers by aggregating useful information into a subset of neurons that can be preserved. In contrast, the activations of disposable neurons are shut off via an attractive form of sparse regularization that emerges naturally from this framework, providing tangible advantages over traditional sparsity penalties without contributing additional tuning parameters to the energy landscape. We demonstrate state-of-the-art compression rates across an array of datasets and network architectures.
Tasks
Published	2018-02-28
URL	http://arxiv.org/abs/1802.10399v3
PDF	http://arxiv.org/pdf/1802.10399v3.pdf
PWC	https://paperswithcode.com/paper/compressing-neural-networks-using-the-1
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Improving Natural Language Inference Using External Knowledge in the Science Questions Domain


Title	Improving Natural Language Inference Using External Knowledge in the Science Questions Domain
Authors	Xiaoyan Wang, Pavan Kapanipathi, Ryan Musa, Mo Yu, Kartik Talamadupula, Ibrahim Abdelaziz, Maria Chang, Achille Fokoue, Bassem Makni, Nicholas Mattei, Michael Witbrock
Abstract	Natural Language Inference (NLI) is fundamental to many Natural Language Processing (NLP) applications including semantic search and question answering. The NLI problem has gained significant attention thanks to the release of large scale, challenging datasets. Present approaches to the problem largely focus on learning-based methods that use only textual information in order to classify whether a given premise entails, contradicts, or is neutral with respect to a given hypothesis. Surprisingly, the use of methods based on structured knowledge – a central topic in artificial intelligence – has not received much attention vis-a-vis the NLI problem. While there are many open knowledge bases that contain various types of reasoning information, their use for NLI has not been well explored. To address this, we present a combination of techniques that harness knowledge graphs to improve performance on the NLI problem in the science questions domain. We present the results of applying our techniques on text, graph, and text-to-graph based models, and discuss implications for the use of external knowledge in solving the NLI problem. Our model achieves the new state-of-the-art performance on the NLI problem over the SciTail science questions dataset.
Tasks	Knowledge Graphs, Natural Language Inference, Question Answering
Published	2018-09-15
URL	http://arxiv.org/abs/1809.05724v2
PDF	http://arxiv.org/pdf/1809.05724v2.pdf
PWC	https://paperswithcode.com/paper/improving-natural-language-inference-using
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Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations


Title	Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations
Authors	Prashanth Gurunath Shivakumar, Panayiotis Georgiou
Abstract	Children speech recognition is challenging mainly due to the inherent high variability in children’s physical and articulatory characteristics and expressions. This variability manifests in both acoustic constructs and linguistic usage due to the rapidly changing developmental stage in children’s life. Part of the challenge is due to the lack of large amounts of available children speech data for efficient modeling. This work attempts to address the key challenges using transfer learning from adult’s models to children’s models in a Deep Neural Network (DNN) framework for children’s Automatic Speech Recognition (ASR) task evaluating on multiple children’s speech corpora with a large vocabulary. The paper presents a systematic and an extensive analysis of the proposed transfer learning technique considering the key factors affecting children’s speech recognition from prior literature. Evaluations are presented on (i) comparisons of earlier GMM-HMM and the newer DNN Models, (ii) effectiveness of standard adaptation techniques versus transfer learning, (iii) various adaptation configurations in tackling the variabilities present in children speech, in terms of (a) acoustic spectral variability, and (b) pronunciation variability and linguistic constraints. Our Analysis spans over (i) number of DNN model parameters (for adaptation), (ii) amount of adaptation data, (iii) ages of children, (iv) age dependent-independent adaptation. Finally, we provide Recommendations on (i) the favorable strategies over various aforementioned - analyzed parameters, and (ii) potential future research directions and relevant challenges/problems persisting in DNN based ASR for children’s speech.
Tasks	Speech Recognition, Transfer Learning
Published	2018-05-08
URL	http://arxiv.org/abs/1805.03322v1
PDF	http://arxiv.org/pdf/1805.03322v1.pdf
PWC	https://paperswithcode.com/paper/transfer-learning-from-adult-to-children-for
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Convex Relaxations for Pose Graph Optimization with Outliers


Title	Convex Relaxations for Pose Graph Optimization with Outliers
Authors	Luca Carlone, Giuseppe C. Calafiore
Abstract	Pose Graph Optimization involves the estimation of a set of poses from pairwise measurements and provides a formalization for many problems arising in mobile robotics and geometric computer vision. In this paper, we consider the case in which a subset of the measurements fed to pose graph optimization is spurious. Our first contribution is to develop robust estimators that can cope with heavy-tailed measurement noise, hence increasing robustness to the presence of outliers. Since the resulting estimators require solving nonconvex optimization problems, we further develop convex relaxations that approximately solve those problems via semidefinite programming. We then provide conditions under which the proposed relaxations are exact. Contrarily to existing approaches, our convex relaxations do not rely on the availability of an initial guess for the unknown poses, hence they are more suitable for setups in which such guess is not available (e.g., multi-robot localization, recovery after localization failure). We tested the proposed techniques in extensive simulations, and we show that some of the proposed relaxations are indeed tight (i.e., they solve the original nonconvex problem 10 exactly) and ensure accurate estimation in the face of a large number of outliers.
Tasks
Published	2018-01-07
URL	http://arxiv.org/abs/1801.02112v1
PDF	http://arxiv.org/pdf/1801.02112v1.pdf
PWC	https://paperswithcode.com/paper/convex-relaxations-for-pose-graph
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D-PAGE: Diverse Paraphrase Generation


Title	D-PAGE: Diverse Paraphrase Generation
Authors	Qiongkai Xu, Juyan Zhang, Lizhen Qu, Lexing Xie, Richard Nock
Abstract	In this paper, we investigate the diversity aspect of paraphrase generation. Prior deep learning models employ either decoding methods or add random input noise for varying outputs. We propose a simple method Diverse Paraphrase Generation (D-PAGE), which extends neural machine translation (NMT) models to support the generation of diverse paraphrases with implicit rewriting patterns. Our experimental results on two real-world benchmark datasets demonstrate that our model generates at least one order of magnitude more diverse outputs than the baselines in terms of a new evaluation metric Jeffrey’s Divergence. We have also conducted extensive experiments to understand various properties of our model with a focus on diversity.
Tasks	Machine Translation, Paraphrase Generation
Published	2018-08-13
URL	http://arxiv.org/abs/1808.04364v1
PDF	http://arxiv.org/pdf/1808.04364v1.pdf
PWC	https://paperswithcode.com/paper/d-page-diverse-paraphrase-generation
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Multi-party Poisoning through Generalized $p$-Tampering


Title	Multi-party Poisoning through Generalized $p$-Tampering
Authors	Saeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed
Abstract	In a poisoning attack against a learning algorithm, an adversary tampers with a fraction of the training data $T$ with the goal of increasing the classification error of the constructed hypothesis/model over the final test distribution. In the distributed setting, $T$ might be gathered gradually from $m$ data providers $P_1,\dots,P_m$ who generate and submit their shares of $T$ in an online way. In this work, we initiate a formal study of $(k,p)$-poisoning attacks in which an adversary controls $k\in[n]$ of the parties, and even for each corrupted party $P_i$, the adversary submits some poisoned data $T’_i$ on behalf of $P_i$ that is still “$(1-p)$-close” to the correct data $T_i$ (e.g., $1-p$ fraction of $T’_i$ is still honestly generated). For $k=m$, this model becomes the traditional notion of poisoning, and for $p=1$ it coincides with the standard notion of corruption in multi-party computation. We prove that if there is an initial constant error for the generated hypothesis $h$, there is always a $(k,p)$-poisoning attacker who can decrease the confidence of $h$ (to have a small error), or alternatively increase the error of $h$, by $\Omega(p \cdot k/m)$. Our attacks can be implemented in polynomial time given samples from the correct data, and they use no wrong labels if the original distributions are not noisy. At a technical level, we prove a general lemma about biasing bounded functions $f(x_1,\dots,x_n)\in[0,1]$ through an attack model in which each block $x_i$ might be controlled by an adversary with marginal probability $p$ in an online way. When the probabilities are independent, this coincides with the model of $p$-tampering attacks, thus we call our model generalized $p$-tampering. We prove the power of such attacks by incorporating ideas from the context of coin-flipping attacks into the $p$-tampering model and generalize the results in both of these areas.
Tasks
Published	2018-09-10
URL	http://arxiv.org/abs/1809.03474v2
PDF	http://arxiv.org/pdf/1809.03474v2.pdf
PWC	https://paperswithcode.com/paper/multi-party-poisoning-through-generalized-p
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