Paper Group ANR 1132
Generating superpixels using deep image representations. On Quantified Modal Theorem Proving for Modeling Ethics. Testing Mixtures of Discrete Distributions. Efficient Algorithms for Smooth Minimax Optimization. Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded Variation Functions by Optimal Decision Trees. Two Computational Mod …
Generating superpixels using deep image representations
| Title | Generating superpixels using deep image representations |
| Authors | Thomas Verelst, Matthew Blaschko, Maxim Berman |
| Abstract | Superpixel algorithms are a common pre-processing step for computer vision algorithms such as segmentation, object tracking and localization. Many superpixel methods only rely on colors features for segmentation, limiting performance in low-contrast regions and applicability to infrared or medical images where object boundaries have wide appearance variability. We study the inclusion of deep image features in the SLIC superpixel algorithm to exploit higher-level image representations. In addition, we devise a trainable superpixel algorithm, yielding an intermediate domain-specific image representation that can be applied to different tasks. A clustering-based superpixel algorithm is transformed into a pixel-wise classification task and superpixel training data is derived from semantic segmentation datasets. Our results demonstrate that this approach is able to improve superpixel quality consistently. |
| Tasks | Object Tracking, Semantic Segmentation |
| Published | 2019-03-11 |
| URL | http://arxiv.org/abs/1903.04586v1 |
| http://arxiv.org/pdf/1903.04586v1.pdf | |
| PWC | https://paperswithcode.com/paper/generating-superpixels-using-deep-image |
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On Quantified Modal Theorem Proving for Modeling Ethics
| Title | On Quantified Modal Theorem Proving for Modeling Ethics |
| Authors | Naveen Sundar Govindarajulu, Selmer Bringsjord, Matthew Peveler |
| Abstract | In the last decade, formal logics have been used to model a wide range of ethical theories and principles with the goal of using these models within autonomous systems. Logics for modeling ethical theories, and their automated reasoners, have requirements that are different from modal logics used for other purposes, e.g. for temporal reasoning. Meeting these requirements necessitates investigation of new approaches for proof automation. Particularly, a quantified modal logic, the deontic cognitive event calculus (DCEC), has been used to model various versions of the doctrine of double effect, akrasia, and virtue ethics. Using a fragment of DCEC, we outline these distinct characteristics and present a sketches of an algorithm that can help with some aspects proof automation for DCEC. |
| Tasks | Automated Theorem Proving |
| Published | 2019-12-30 |
| URL | https://arxiv.org/abs/1912.12959v1 |
| https://arxiv.org/pdf/1912.12959v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-quantified-modal-theorem-proving-for |
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Testing Mixtures of Discrete Distributions
| Title | Testing Mixtures of Discrete Distributions |
| Authors | Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld |
| Abstract | There has been significant study on the sample complexity of testing properties of distributions over large domains. For many properties, it is known that the sample complexity can be substantially smaller than the domain size. For example, over a domain of size $n$, distinguishing the uniform distribution from distributions that are far from uniform in $\ell_1$-distance uses only $O(\sqrt{n})$ samples. However, the picture is very different in the presence of arbitrary noise, even when the amount of noise is quite small. In this case, one must distinguish if samples are coming from a distribution that is $\epsilon$-close to uniform from the case where the distribution is $(1-\epsilon)$-far from uniform. The latter task requires nearly linear in $n$ samples [Valiant 2008, Valian and Valiant 2011]. In this work, we present a noise model that on one hand is more tractable for the testing problem, and on the other hand represents a rich class of noise families. In our model, the noisy distribution is a mixture of the original distribution and noise, where the latter is known to the tester either explicitly or via sample access; the form of the noise is also known a priori. Focusing on the identity and closeness testing problems leads to the following mixture testing question: Given samples of distributions $p, q_1,q_2$, can we test if $p$ is a mixture of $q_1$ and $q_2$? We consider this general question in various scenarios that differ in terms of how the tester can access the distributions, and show that indeed this problem is more tractable. Our results show that the sample complexity of our testers are exactly the same as for the classical non-mixture case. |
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| Published | 2019-07-06 |
| URL | https://arxiv.org/abs/1907.03190v1 |
| https://arxiv.org/pdf/1907.03190v1.pdf | |
| PWC | https://paperswithcode.com/paper/testing-mixtures-of-discrete-distributions |
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Efficient Algorithms for Smooth Minimax Optimization
| Title | Efficient Algorithms for Smooth Minimax Optimization |
| Authors | Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh |
| Abstract | This paper studies first order methods for solving smooth minimax optimization problems $\min_x \max_y g(x,y)$ where $g(\cdot,\cdot)$ is smooth and $g(x,\cdot)$ is concave for each $x$. In terms of $g(\cdot,y)$, we consider two settings – strongly convex and nonconvex – and improve upon the best known rates in both. For strongly-convex $g(\cdot, y),\ \forall y$, we propose a new algorithm combining Mirror-Prox and Nesterov’s AGD, and show that it can find global optimum in $\tilde{O}(1/k^2)$ iterations, improving over current state-of-the-art rate of $O(1/k)$. We use this result along with an inexact proximal point method to provide $\tilde{O}(1/k^{1/3})$ rate for finding stationary points in the nonconvex setting where $g(\cdot, y)$ can be nonconvex. This improves over current best-known rate of $O(1/k^{1/5})$. Finally, we instantiate our result for finite nonconvex minimax problems, i.e., $\min_x \max_{1\leq i\leq m} f_i(x)$, with nonconvex $f_i(\cdot)$, to obtain convergence rate of $O(m(\log m)^{3/2}/k^{1/3})$ total gradient evaluations for finding a stationary point. |
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| Published | 2019-07-02 |
| URL | https://arxiv.org/abs/1907.01543v1 |
| https://arxiv.org/pdf/1907.01543v1.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-algorithms-for-smooth-minimax |
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Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded Variation Functions by Optimal Decision Trees
| Title | Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded Variation Functions by Optimal Decision Trees |
| Authors | Sabyasachi Chatterjee, Subhajit Goswami |
| Abstract | Proposed by \cite{donoho1997cart}, Dyadic CART is a nonparametric regression method which computes a globally optimal dyadic decision tree and fits piecewise constant functions in two dimensions. In this article we define and study Dyadic CART and a closely related estimator, namely Optimal Regression Tree (ORT), in the context of estimating piecewise smooth functions in general dimensions. More precisely, these optimal decision tree estimators fit piecewise polynomials of any given degree. Like Dyadic CART in two dimensions, we reason that these estimators can also be computed in polynomial time in the sample size via dynamic programming. We prove oracle inequalities for the finite sample risk of Dyadic CART and ORT which imply tight {risk} bounds for several function classes of interest. Firstly, they imply that the finite sample risk of ORT of {\em order} $r \geq 0$ is always bounded by $C k \frac{\log N}{N}$ ($N$ is the sample size) whenever the regression function is piecewise polynomial of degree $r$ on some reasonably regular axis aligned rectangular partition of the domain with at most $k$ rectangles. Beyond the univariate case, such guarantees are scarcely available in the literature for computationally efficient estimators. Secondly, our oracle inequalities uncover minimax rate optimality and adaptivity of the Dyadic CART estimator for function spaces with bounded variation. We consider two function spaces of recent interest where multivariate total variation denoising and univariate trend filtering are the state of the art methods. We show that Dyadic CART enjoys certain advantages over these estimators while still maintaining all their known guarantees. |
| Tasks | Denoising |
| Published | 2019-11-26 |
| URL | https://arxiv.org/abs/1911.11562v2 |
| https://arxiv.org/pdf/1911.11562v2.pdf | |
| PWC | https://paperswithcode.com/paper/adaptive-estimation-of-multivariate-piecewise |
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Two Computational Models for Analyzing Political Attention in Social Media
| Title | Two Computational Models for Analyzing Political Attention in Social Media |
| Authors | Libby Hemphill, Angela M. Schöpke-Gonzalez |
| Abstract | Understanding how political attention is divided and over what subjects is crucial for research on areas such as agenda setting, framing, and political rhetoric. Existing methods for measuring attention, such as manual labeling according to established codebooks, are expensive and can be restrictive. We describe two computational models that automatically distinguish topics in politicians’ social media content. Our models—one supervised classifier and one unsupervised topic model—provide different benefits. The supervised classifier reduces the labor required to classify content according to pre-determined topic list. However, tweets do more than communicate policy positions. Our unsupervised model uncovers both political topics and other Twitter uses (e.g., constituent service). These models are effective, inexpensive computational tools for political communication and social media research. We demonstrate their utility and discuss the different analyses they afford by applying both models to the tweets posted by members of the 115th U.S. Congress. |
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| Published | 2019-09-17 |
| URL | https://arxiv.org/abs/1909.08189v1 |
| https://arxiv.org/pdf/1909.08189v1.pdf | |
| PWC | https://paperswithcode.com/paper/two-computational-models-for-analyzing |
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Predicting detection filters for small footprint open-vocabulary keyword spotting
| Title | Predicting detection filters for small footprint open-vocabulary keyword spotting |
| Authors | Theodore Bluche, Thibault Gisselbrecht |
| Abstract | In many scenarios, detecting keywords from natural language queries is sufficient to understand the intent of the user. In this paper, we propose a fully-neural approach to open-vocabulary keyword spotting, allowing a user to include a voice interface to its device without having to retrain a model on task-specific data. We present a keyword detection neural network weighing less than 550KB, in which the topmost layer performing keyword detection is predicted by an auxiliary network, that may be run offline to generate a detector for any keyword. |
| Tasks | Keyword Spotting |
| Published | 2019-12-16 |
| URL | https://arxiv.org/abs/1912.07575v1 |
| https://arxiv.org/pdf/1912.07575v1.pdf | |
| PWC | https://paperswithcode.com/paper/predicting-detection-filters-for-small |
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Parsimonious Morpheme Segmentation with an Application to Enriching Word Embeddings
| Title | Parsimonious Morpheme Segmentation with an Application to Enriching Word Embeddings |
| Authors | Ahmed El-Kishky, Frank Xu, Aston Zhang, Jiawei Han |
| Abstract | Traditionally, many text-mining tasks treat individual word-tokens as the finest meaningful semantic granularity. However, in many languages and specialized corpora, words are composed by concatenating semantically meaningful subword structures. Word-level analysis cannot leverage the semantic information present in such subword structures. With regard to word embedding techniques, this leads to not only poor embeddings for infrequent words in long-tailed text corpora but also weak capabilities for handling out-of-vocabulary words. In this paper we propose MorphMine for unsupervised morpheme segmentation. MorphMine applies a parsimony criterion to hierarchically segment words into the fewest number of morphemes at each level of the hierarchy. This leads to longer shared morphemes at each level of segmentation. Experiments show that MorphMine segments words in a variety of languages into human-verified morphemes. Additionally, we experimentally demonstrate that utilizing MorphMine morphemes to enrich word embeddings consistently improves embedding quality on a variety of of embedding evaluations and a downstream language modeling task. |
| Tasks | Language Modelling, Word Embeddings |
| Published | 2019-08-18 |
| URL | https://arxiv.org/abs/1908.07832v2 |
| https://arxiv.org/pdf/1908.07832v2.pdf | |
| PWC | https://paperswithcode.com/paper/190807832 |
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Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference
| Title | Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference |
| Authors | He Bai, Yu Zhou, Jiajun Zhang, Chengqing Zong |
| Abstract | Dialogue contexts are proven helpful in the spoken language understanding (SLU) system and they are typically encoded with explicit memory representations. However, most of the previous models learn the context memory with only one objective to maximizing the SLU performance, leaving the context memory under-exploited. In this paper, we propose a new dialogue logistic inference (DLI) task to consolidate the context memory jointly with SLU in the multi-task framework. DLI is defined as sorting a shuffled dialogue session into its original logical order and shares the same memory encoder and retrieval mechanism as the SLU model. Our experimental results show that various popular contextual SLU models can benefit from our approach, and improvements are quite impressive, especially in slot filling. |
| Tasks | Slot Filling, Spoken Language Understanding |
| Published | 2019-06-05 |
| URL | https://arxiv.org/abs/1906.01788v1 |
| https://arxiv.org/pdf/1906.01788v1.pdf | |
| PWC | https://paperswithcode.com/paper/memory-consolidation-for-contextual-spoken |
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A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning
| Title | A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning |
| Authors | Nicholas C. Landolfi, Garrett Thomas, Tengyu Ma |
| Abstract | The aim of multi-task reinforcement learning is two-fold: (1) efficiently learn by training against multiple tasks and (2) quickly adapt, using limited samples, to a variety of new tasks. In this work, the tasks correspond to reward functions for environments with the same (or similar) dynamical models. We propose to learn a dynamical model during the training process and use this model to perform sample-efficient adaptation to new tasks at test time. We use significantly fewer samples by performing policy optimization only in a “virtual” environment whose transitions are given by our learned dynamical model. Our algorithm sequentially trains against several tasks. Upon encountering a new task, we first warm-up a policy on our learned dynamical model, which requires no new samples from the environment. We then adapt the dynamical model with samples from this policy in the real environment. We evaluate our approach on several continuous control benchmarks and demonstrate its efficacy over MAML, a state-of-the-art meta-learning algorithm, on these tasks. |
| Tasks | Continuous Control, Meta-Learning |
| Published | 2019-07-11 |
| URL | https://arxiv.org/abs/1907.04964v3 |
| https://arxiv.org/pdf/1907.04964v3.pdf | |
| PWC | https://paperswithcode.com/paper/a-model-based-approach-for-sample-efficient |
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SecureGBM: Secure Multi-Party Gradient Boosting
| Title | SecureGBM: Secure Multi-Party Gradient Boosting |
| Authors | Zhi Fengy, Haoyi Xiong, Chuanyuan Song, Sijia Yang, Baoxin Zhao, Licheng Wang, Zeyu Chen, Shengwen Yang, Liping Liu, Jun Huan |
| Abstract | Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust each others. In this paper, we proposed a novel Gradient Boosting Machines (GBM) framework SecureGBM built-up with a multi-party computation model based on semi-homomorphic encryption, where every involved party can jointly obtain a shared Gradient Boosting machines model while protecting their own data from the potential privacy leakage and inferential identification. More specific, our work focused on a specific “dual–party” secure learning scenario based on two parties – both party own an unique view (i.e., attributes or features) to the sample group of samples while only one party owns the labels. In such scenario, feature and label data are not allowed to share with others. To achieve the above goal, we firstly extent – LightGBM – a well known implementation of tree-based GBM through covering its key operations for training and inference with SEAL homomorphic encryption schemes. However, the performance of such re-implementation is significantly bottle-necked by the explosive inflation of the communication payloads, based on ciphertexts subject to the increasing length of plaintexts. In this way, we then proposed to use stochastic approximation techniques to reduced the communication payloads while accelerating the overall training procedure in a statistical manner. Our experiments using the real-world data showed that SecureGBM can well secure the communication and computation of LightGBM training and inference procedures for the both parties while only losing less than 3% AUC, using the same number of iterations for gradient boosting, on a wide range of benchmark datasets. |
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| Published | 2019-11-27 |
| URL | https://arxiv.org/abs/1911.11997v1 |
| https://arxiv.org/pdf/1911.11997v1.pdf | |
| PWC | https://paperswithcode.com/paper/securegbm-secure-multi-party-gradient |
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End-to-end sensor modeling for LiDAR Point Cloud
| Title | End-to-end sensor modeling for LiDAR Point Cloud |
| Authors | Khaled Elmadawi, Moemen Abdelrazek, Mohamed Elsobky, Hesham M. Eraqi, Mohamed Zahran |
| Abstract | Advanced sensors are a key to enable self-driving cars technology. Laser scanner sensors (LiDAR, Light Detection And Ranging) became a fundamental choice due to its long-range and robustness to low light driving conditions. The problem of designing a control software for self-driving cars is a complex task to explicitly formulate in rule-based systems, thus recent approaches rely on machine learning that can learn those rules from data. The major problem with such approaches is that the amount of training data required for generalizing a machine learning model is big, and on the other hand LiDAR data annotation is very costly compared to other car sensors. An accurate LiDAR sensor model can cope with such problem. Moreover, its value goes beyond this because existing LiDAR development, validation, and evaluation platforms and processes are very costly, and virtual testing and development environments are still immature in terms of physical properties representation. In this work we propose a novel Deep Learning-based LiDAR sensor model. This method models the sensor echos, using a Deep Neural Network to model echo pulse widths learned from real data using Polar Grid Maps (PGM). We benchmark our model performance against comprehensive real sensor data and very promising results are achieved that sets a baseline for future works. |
| Tasks | Self-Driving Cars, Sensor Modeling |
| Published | 2019-07-17 |
| URL | https://arxiv.org/abs/1907.07748v1 |
| https://arxiv.org/pdf/1907.07748v1.pdf | |
| PWC | https://paperswithcode.com/paper/end-to-end-sensor-modeling-for-lidar-point |
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Modeling Semantic Relationship in Multi-turn Conversations with Hierarchical Latent Variables
| Title | Modeling Semantic Relationship in Multi-turn Conversations with Hierarchical Latent Variables |
| Authors | Lei Shen, Yang Feng, Haolan Zhan |
| Abstract | Multi-turn conversations consist of complex semantic structures, and it is still a challenge to generate coherent and diverse responses given previous utterances. It’s practical that a conversation takes place under a background, meanwhile, the query and response are usually most related and they are consistent in topic but also different in content. However, little work focuses on such hierarchical relationship among utterances. To address this problem, we propose a Conversational Semantic Relationship RNN (CSRR) model to construct the dependency explicitly. The model contains latent variables in three hierarchies. The discourse-level one captures the global background, the pair-level one stands for the common topic information between query and response, and the utterance-level ones try to represent differences in content. Experimental results show that our model significantly improves the quality of responses in terms of fluency, coherence and diversity compared to baseline methods. |
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| Published | 2019-06-18 |
| URL | https://arxiv.org/abs/1906.07429v1 |
| https://arxiv.org/pdf/1906.07429v1.pdf | |
| PWC | https://paperswithcode.com/paper/modeling-semantic-relationship-in-multi-turn |
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Gamma-Nets: Generalizing Value Estimation over Timescale
| Title | Gamma-Nets: Generalizing Value Estimation over Timescale |
| Authors | Craig Sherstan, Shibhansh Dohare, James MacGlashan, Johannes Günther, Patrick M. Pilarski |
| Abstract | We present $\Gamma$-nets, a method for generalizing value function estimation over timescale. By using the timescale as one of the estimator’s inputs we can estimate value for arbitrary timescales. As a result, the prediction target for any timescale is available and we are free to train on multiple timescales at each timestep. Here we empirically evaluate $\Gamma$-nets in the policy evaluation setting. We first demonstrate the approach on a square wave and then on a robot arm using linear function approximation. Next, we consider the deep reinforcement learning setting using several Atari video games. Our results show that $\Gamma$-nets can be effective for predicting arbitrary timescales, with only a small cost in accuracy as compared to learning estimators for fixed timescales. $\Gamma$-nets provide a method for compactly making predictions at many timescales without requiring a priori knowledge of the task, making it a valuable contribution to ongoing work on model-based planning, representation learning, and lifelong learning algorithms. |
| Tasks | Representation Learning |
| Published | 2019-11-18 |
| URL | https://arxiv.org/abs/1911.07794v4 |
| https://arxiv.org/pdf/1911.07794v4.pdf | |
| PWC | https://paperswithcode.com/paper/gamma-nets-generalizing-value-estimation-over |
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Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving
| Title | Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving |
| Authors | Yulong Cao, Chaowei Xiao, Benjamin Cyr, Yimeng Zhou, Won Park, Sara Rampazzi, Qi Alfred Chen, Kevin Fu, Z. Morley Mao |
| Abstract | In Autonomous Vehicles (AVs), one fundamental pillar is perception, which leverages sensors like cameras and LiDARs (Light Detection and Ranging) to understand the driving environment. Due to its direct impact on road safety, multiple prior efforts have been made to study its the security of perception systems. In contrast to prior work that concentrates on camera-based perception, in this work we perform the first security study of LiDAR-based perception in AV settings, which is highly important but unexplored. We consider LiDAR spoofing attacks as the threat model and set the attack goal as spoofing obstacles close to the front of a victim AV. We find that blindly applying LiDAR spoofing is insufficient to achieve this goal due to the machine learning-based object detection process. Thus, we then explore the possibility of strategically controlling the spoofed attack to fool the machine learning model. We formulate this task as an optimization problem and design modeling methods for the input perturbation function and the objective function. We also identify the inherent limitations of directly solving the problem using optimization and design an algorithm that combines optimization and global sampling, which improves the attack success rates to around 75%. As a case study to understand the attack impact at the AV driving decision level, we construct and evaluate two attack scenarios that may damage road safety and mobility. We also discuss defense directions at the AV system, sensor, and machine learning model levels. |
| Tasks | Autonomous Driving, Autonomous Vehicles, Object Detection |
| Published | 2019-07-16 |
| URL | https://arxiv.org/abs/1907.06826v2 |
| https://arxiv.org/pdf/1907.06826v2.pdf | |
| PWC | https://paperswithcode.com/paper/adversarial-sensor-attack-on-lidar-based |
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