Paper Group ANR 323
Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection. Learning Norms from Stories: A Prior for Value Aligned Agents. Deep Learning for Localization in the Lung. Sharp Analysis for Nonconvex SGD Escaping from Saddle Points. Stackelberg Punishment and Bully-Proofing Autonomous Vehicles …
Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection
| Title | Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection |
| Authors | Kang Liu, Haoyu Yang, Yuzhe Ma, Benjamin Tan, Bei Yu, Evangeline F. Y. Young, Ramesh Karri, Siddharth Garg |
| Abstract | There is substantial interest in the use of machine learning (ML) based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep learning. However, while deep learning methods have surpassed state-of-the-art performance in several applications, they have exhibited intrinsic susceptibility to adversarial perturbations — small but deliberate alterations to the input of a neural network, precipitating incorrect predictions. In this paper, we seek to investigate whether adversarial perturbations pose risks to ML-based CAD tools, and if so, how these risks can be mitigated. To this end, we use a motivating case study of lithographic hotspot detection, for which convolutional neural networks (CNN) have shown great promise. In this context, we show the first adversarial perturbation attacks on state-of-the-art CNN-based hotspot detectors; specifically, we show that small (on average 0.5% modified area), functionality preserving and design-constraint satisfying changes to a layout can nonetheless trick a CNN-based hotspot detector into predicting the modified layout as hotspot free (with up to 99.7% success). We propose an adversarial retraining strategy to improve the robustness of CNN-based hotspot detection and show that this strategy significantly improves robustness (by a factor of ~3) against adversarial attacks without compromising classification accuracy. |
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| Published | 2019-06-25 |
| URL | https://arxiv.org/abs/1906.10773v1 |
| https://arxiv.org/pdf/1906.10773v1.pdf | |
| PWC | https://paperswithcode.com/paper/are-adversarial-perturbations-a-showstopper |
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Learning Norms from Stories: A Prior for Value Aligned Agents
| Title | Learning Norms from Stories: A Prior for Value Aligned Agents |
| Authors | Spencer Frazier, Md Sultan Al Nahian, Mark Riedl, Brent Harrison |
| Abstract | Value alignment is a property of an intelligent agent indicating that it can only pursue goals and activities that are beneficial to humans. Traditional approaches to value alignment use imitation learning or preference learning to infer the values of humans by observing their behavior. We introduce a complementary technique in which a value aligned prior is learned from naturally occurring stories which encode societal norms. Training data is sourced from the childrens educational comic strip, Goofus and Gallant. In this work, we train multiple machine learning models to classify natural language descriptions of situations found in the comic strip as normative or non normative by identifying if they align with the main characters behavior. We also report the models performance when transferring to two unrelated tasks with little to no additional training on the new task. |
| Tasks | Imitation Learning |
| Published | 2019-12-07 |
| URL | https://arxiv.org/abs/1912.03553v1 |
| https://arxiv.org/pdf/1912.03553v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-norms-from-stories-a-prior-for-value |
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Deep Learning for Localization in the Lung
| Title | Deep Learning for Localization in the Lung |
| Authors | Jake Sganga, David Eng, Chauncey Graetzel, David B. Camarillo |
| Abstract | Lung cancer is the leading cause of cancer-related death worldwide, and early diagnosis is critical to improving patient outcomes. To diagnose cancer, a highly trained pulmonologist must navigate a flexible bronchoscope deep into the branched structure of the lung for biopsy. The biopsy fails to sample the target tissue in 26-33% of cases largely because of poor registration with the preoperative CT map. We developed two deep learning approaches to localize the bronchoscope in the preoperative CT map in real time and tested the algorithms across 13 trajectories in a lung phantom and 68 trajectories in 11 human cadaver lungs. In the lung phantom, we observe performance reaching 95% precision and recall of visible airways and 3 mm average position error. On a successful cadaver lung sequence, the algorithms trained on simulation alone achieved 77%-94% precision and recall of visible airways and 4-6 mm average position error. We also compare the effect of GAN-stylizing images and we look at aggregate statistics over the entire set of trajectories. |
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| Published | 2019-03-25 |
| URL | http://arxiv.org/abs/1903.10554v1 |
| http://arxiv.org/pdf/1903.10554v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-learning-for-localization-in-the-lung |
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Sharp Analysis for Nonconvex SGD Escaping from Saddle Points
| Title | Sharp Analysis for Nonconvex SGD Escaping from Saddle Points |
| Authors | Cong Fang, Zhouchen Lin, Tong Zhang |
| Abstract | In this paper, we give a sharp analysis for Stochastic Gradient Descent (SGD) and prove that SGD is able to efficiently escape from saddle points and find an $(\epsilon, O(\epsilon^{0.5}))$-approximate second-order stationary point in $\tilde{O}(\epsilon^{-3.5})$ stochastic gradient computations for generic nonconvex optimization problems, when the objective function satisfies gradient-Lipschitz, Hessian-Lipschitz, and dispersive noise assumptions. This result subverts the classical belief that SGD requires at least $O(\epsilon^{-4})$ stochastic gradient computations for obtaining an $(\epsilon,O(\epsilon^{0.5}))$-approximate second-order stationary point. Such SGD rate matches, up to a polylogarithmic factor of problem-dependent parameters, the rate of most accelerated nonconvex stochastic optimization algorithms that adopt additional techniques, such as Nesterov’s momentum acceleration, negative curvature search, as well as quadratic and cubic regularization tricks. Our novel analysis gives new insights into nonconvex SGD and can be potentially generalized to a broad class of stochastic optimization algorithms. |
| Tasks | Stochastic Optimization |
| Published | 2019-02-01 |
| URL | https://arxiv.org/abs/1902.00247v2 |
| https://arxiv.org/pdf/1902.00247v2.pdf | |
| PWC | https://paperswithcode.com/paper/sharp-analysis-for-nonconvex-sgd-escaping |
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Stackelberg Punishment and Bully-Proofing Autonomous Vehicles
| Title | Stackelberg Punishment and Bully-Proofing Autonomous Vehicles |
| Authors | Matt Cooper, Jun Ki Lee, Jacob Beck, Joshua D. Fishman, Michael Gillett, Zoë Papakipos, Aaron Zhang, Jerome Ramos, Aansh Shah, Michael L. Littman |
| Abstract | Mutually beneficial behavior in repeated games can be enforced via the threat of punishment, as enshrined in game theory’s well-known “folk theorem.” There is a cost, however, to a player for generating these disincentives. In this work, we seek to minimize this cost by computing a “Stackelberg punishment,” in which the player selects a behavior that sufficiently punishes the other player while maximizing its own score under the assumption that the other player will adopt a best response. This idea generalizes the concept of a Stackelberg equilibrium. Known efficient algorithms for computing a Stackelberg equilibrium can be adapted to efficiently produce a Stackelberg punishment. We demonstrate an application of this idea in an experiment involving a virtual autonomous vehicle and human participants. We find that a self-driving car with a Stackelberg punishment policy discourages human drivers from bullying in a driving scenario requiring social negotiation. |
| Tasks | Autonomous Vehicles |
| Published | 2019-08-23 |
| URL | https://arxiv.org/abs/1908.08641v1 |
| https://arxiv.org/pdf/1908.08641v1.pdf | |
| PWC | https://paperswithcode.com/paper/stackelberg-punishment-and-bully-proofing |
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Is Image Memorability Prediction Solved?
| Title | Is Image Memorability Prediction Solved? |
| Authors | Shay Perera, Ayellet Tal, Lihi Zelnik-Manor |
| Abstract | This paper deals with the prediction of the memorability of a given image. We start by proposing an algorithm that reaches human-level performance on the LaMem dataset - the only large scale benchmark for memorability prediction. The suggested algorithm is based on three observations we make regarding convolutional neural networks (CNNs) that affect memorability prediction. Having reached human-level performance we were humbled, and asked ourselves whether indeed we have resolved memorability prediction - and answered this question in the negative. We studied a few factors and made some recommendations that should be taken into account when designing the next benchmark. |
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| Published | 2019-01-31 |
| URL | http://arxiv.org/abs/1901.11420v1 |
| http://arxiv.org/pdf/1901.11420v1.pdf | |
| PWC | https://paperswithcode.com/paper/is-image-memorability-prediction-solved |
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Multi-resolution Graph Neural Network for Identifying Disease-specific Variations in Brain Connectivity
| Title | Multi-resolution Graph Neural Network for Identifying Disease-specific Variations in Brain Connectivity |
| Authors | Xin Ma, Guorong Wu, Won Hwa Kim |
| Abstract | Convolution Neural Network (CNN) recently have been adopted in several neuroimaging studies for diagnosis capturing disease-specific changes in the brain. While many of these methods are designed to work with images in $\mathbb R^n$ exploiting regular structure of the domain, they are not well-suited to analyze data with irregular structure such as brain connectivity. As there is significant interest in understanding the altered interactions between different brain regions that lead to neuro-disorders, it is important to develop data-driven methods that work with a population of graph data for traditional prediction tasks. In this regime, we propose a novel CNN-based framework with adaptive graph transforms to learn the most disease-relevant connectome feature maps which have the highest discrimination power across diagnostic categories. The backbone of our framework is a multi-resolution representation of the graph matrix which is steered by a set of wavelet-like graph transforms. In this context, our supervised graph learning framework outperforms conventional graph methods that predict diagnostic label only based on the underlying individual graph. Our extensive experiments on two real datasets of functional and structural brain networks show that our multi-resolution framework achieves significantly higher accuracy, precision and recall in predicting diagnostic labels and identifying disease-specific brain connectivities that are associated with brain disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD) and Alzheimer’s Disease (AD). |
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| Published | 2019-12-03 |
| URL | https://arxiv.org/abs/1912.01181v1 |
| https://arxiv.org/pdf/1912.01181v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-resolution-graph-neural-network-for |
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Quantifying Interpretability and Trust in Machine Learning Systems
| Title | Quantifying Interpretability and Trust in Machine Learning Systems |
| Authors | Philipp Schmidt, Felix Biessmann |
| Abstract | Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them. Hence interpretability methods for ML are an active focus of research. A central problem in this context is that both the quality of interpretability methods as well as trust in ML predictions are difficult to measure. Yet evaluations, comparisons and improvements of trust and interpretability require quantifiable measures. Here we propose a quantitative measure for the quality of interpretability methods. Based on that we derive a quantitative measure of trust in ML decisions. Building on previous work we propose to measure intuitive understanding of algorithmic decisions using the information transfer rate at which humans replicate ML model predictions. We provide empirical evidence from crowdsourcing experiments that the proposed metric robustly differentiates interpretability methods. The proposed metric also demonstrates the value of interpretability for ML assisted human decision making: in our experiments providing explanations more than doubled productivity in annotation tasks. However unbiased human judgement is critical for doctors, judges, policy makers and others. Here we derive a trust metric that identifies when human decisions are overly biased towards ML predictions. Our results complement existing qualitative work on trust and interpretability by quantifiable measures that can serve as objectives for further improving methods in this field of research. |
| Tasks | Decision Making |
| Published | 2019-01-20 |
| URL | http://arxiv.org/abs/1901.08558v1 |
| http://arxiv.org/pdf/1901.08558v1.pdf | |
| PWC | https://paperswithcode.com/paper/quantifying-interpretability-and-trust-in |
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Synthesizing Datalog Programs Using Numerical Relaxation
| Title | Synthesizing Datalog Programs Using Numerical Relaxation |
| Authors | Xujie Si, Mukund Raghothaman, Kihong Heo, Mayur Naik |
| Abstract | The problem of learning logical rules from examples arises in diverse fields, including program synthesis, logic programming, and machine learning. Existing approaches either involve solving computationally difficult combinatorial problems, or performing parameter estimation in complex statistical models. In this paper, we present Difflog, a technique to extend the logic programming language Datalog to the continuous setting. By attaching real-valued weights to individual rules of a Datalog program, we naturally associate numerical values with individual conclusions of the program. Analogous to the strategy of numerical relaxation in optimization problems, we can now first determine the rule weights which cause the best agreement between the training labels and the induced values of output tuples, and subsequently recover the classical discrete-valued target program from the continuous optimum. We evaluate Difflog on a suite of 34 benchmark problems from recent literature in knowledge discovery, formal verification, and database query-by-example, and demonstrate significant improvements in learning complex programs with recursive rules, invented predicates, and relations of arbitrary arity. |
| Tasks | Program Synthesis |
| Published | 2019-06-01 |
| URL | https://arxiv.org/abs/1906.00163v2 |
| https://arxiv.org/pdf/1906.00163v2.pdf | |
| PWC | https://paperswithcode.com/paper/190600163 |
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Using Context Information to Enhance Simple Question Answering
| Title | Using Context Information to Enhance Simple Question Answering |
| Authors | Lin Li, Mengjing Zhang, Zhaohui Chao, Jianwen Xiang |
| Abstract | With the rapid development of knowledge bases(KBs),question answering(QA)based on KBs has become a hot research issue. In this paper,we propose two frameworks(i.e.,pipeline framework,an end-to-end framework)to focus answering single-relation factoid question. In both of two frameworks,we study the effect of context information on the quality of QA,such as the entity’s notable type,out-degree. In the end-to-end framework,we combine char-level encoding and self-attention mechanisms,using weight sharing and multi-task strategies to enhance the accuracy of QA. Experimental results show that context information can get better results of simple QA whether it is the pipeline framework or the end-to-end framework. In addition,we find that the end-to-end framework achieves results competitive with state-of-the-art approaches in terms of accuracy and take much shorter time than them. |
| Tasks | Question Answering |
| Published | 2019-04-27 |
| URL | http://arxiv.org/abs/1905.01995v1 |
| http://arxiv.org/pdf/1905.01995v1.pdf | |
| PWC | https://paperswithcode.com/paper/190501995 |
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Introducing Astrocytes on a Neuromorphic Processor: Synchronization, Local Plasticity and Edge of Chaos
| Title | Introducing Astrocytes on a Neuromorphic Processor: Synchronization, Local Plasticity and Edge of Chaos |
| Authors | Guangzhi Tang, Ioannis E. Polykretis, Vladimir A. Ivanov, Arpit Shah, Konstantinos P. Michmizos |
| Abstract | While there is still a lot to learn about astrocytes and their neuromodulatory role in the spatial and temporal integration of neuronal activity, their introduction to neuromorphic hardware is timely, facilitating their computational exploration in basic science questions as well as their exploitation in real-world applications. Here, we present an astrocytic module that enables the development of a spiking Neuronal-Astrocytic Network (SNAN) into Intel’s Loihi neuromorphic chip. The basis of the Loihi module is an end-to-end biophysically plausible compartmental model of an astrocyte that simulates the intracellular activity in response to the synaptic activity in space and time. To demonstrate the functional role of astrocytes in SNAN, we describe how an astrocyte may sense and induce activity-dependent neuronal synchronization, switch on and off spike-time-dependent plasticity (STDP) to introduce single-shot learning, and monitor the transition between ordered and chaotic activity at the synaptic space. Our module may serve as an extension for neuromorphic hardware, by either replicating or exploring the distinct computational roles that astrocytes have in forming biological intelligence. |
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| Published | 2019-07-02 |
| URL | https://arxiv.org/abs/1907.01620v2 |
| https://arxiv.org/pdf/1907.01620v2.pdf | |
| PWC | https://paperswithcode.com/paper/introducing-astrocytes-on-a-neuromorphic |
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Agnostic data debiasing through a local sanitizer learnt from an adversarial network approach
| Title | Agnostic data debiasing through a local sanitizer learnt from an adversarial network approach |
| Authors | Ulrich Aïvodji, François Bidet, Sébastien Gambs, Rosin Claude Ngueveu, Alain Tapp |
| Abstract | The widespread use of automated decision processes in many areas of our society raises serious ethical issues concerning the fairness of the process and the possible resulting discriminations. In this work, we propose a novel approach called GANsan whose objective is to prevent the possibility of any discrimination i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our sanitization algorithm GANsan is partially inspired by the powerful framework of generative adversarial networks (in particular the Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible and thus preserving the interpretability of the sanitized data. As a consequence, once the sanitizer is trained, it can be applied to new data, such as for instance, locally by an individual on his profile before releasing it. Finally, experiments on a real dataset demonstrate the effectiveness of the proposed approach as well as the achievable trade-off between fairness and utility. |
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| Published | 2019-06-19 |
| URL | https://arxiv.org/abs/1906.07858v2 |
| https://arxiv.org/pdf/1906.07858v2.pdf | |
| PWC | https://paperswithcode.com/paper/agnostic-data-debiasing-through-a-local |
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Comparing and Combining Lexicase Selection and Novelty Search
| Title | Comparing and Combining Lexicase Selection and Novelty Search |
| Authors | Lia Jundt, Thomas Helmuth |
| Abstract | Lexicase selection and novelty search, two parent selection methods used in evolutionary computation, emphasize exploring widely in the search space more than traditional methods such as tournament selection. However, lexicase selection is not explicitly driven to select for novelty in the population, and novelty search suffers from lack of direction toward a goal, especially in unconstrained, highly-dimensional spaces. We combine the strengths of lexicase selection and novelty search by creating a novelty score for each test case, and adding those novelty scores to the normal error values used in lexicase selection. We use this new novelty-lexicase selection to solve automatic program synthesis problems, and find it significantly outperforms both novelty search and lexicase selection. Additionally, we find that novelty search has very little success in the problem domain of program synthesis. We explore the effects of each of these methods on population diversity and long-term problem solving performance, and give evidence to support the hypothesis that novelty-lexicase selection resists converging to local optima better than lexicase selection. |
| Tasks | Program Synthesis |
| Published | 2019-05-22 |
| URL | https://arxiv.org/abs/1905.09374v2 |
| https://arxiv.org/pdf/1905.09374v2.pdf | |
| PWC | https://paperswithcode.com/paper/comparing-and-combining-lexicase-selection |
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Provable Low Rank Phase Retrieval and Compressive PCA
| Title | Provable Low Rank Phase Retrieval and Compressive PCA |
| Authors | Seyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani |
| Abstract | We study the Low Rank Phase Retrieval (LRPR) problem defined as follows: recover an $n \times q$ matrix $X^$ of rank $r$ from a different and independent set of $m$ phaseless (magnitude-only) linear projections of each of its columns. To be precise, we need to recover $X^$ from $y_k := A_k{}’ x^*_k, k=1,2,\dots, q$ when the measurement matrices $A_k$ are mutually independent. Here $y_k \in \mathbb{R}^m$. The question is when can we solve LRPR with $m \ll n$? Our work introduces the first provably correct solution, Alternating Minimization for Low-Rank Phase Retrieval (AltMinLowRaP), for solving LRPR. We demonstrate its advantage over existing work via extensive simulation, and some partly real data, experiments. Our guarantee for AltMinLowRaP shows that it can solve LRPR to $\epsilon$ accuracy if $m q \ge C n r^4 \log(1/\epsilon)$, the matrices $A_k$ contain i.i.d. standard Gaussian entries, the condition number of $X^*$ is bounded by a numerical constant, and its right singular vectors satisfy the incoherence (denseness) assumption from matrix completion literature. Its time complexity is only $ C mq nr \log^2(1/\epsilon)$. In the regime of small $r$, our sample complexity is much better than what standard PR methods need; and it is only about $r^3$ times worse than its order-optimal value of $(n + q) r$. Moreover, if we replace $m$ by its lower bound for each approach, then the same can be said for the time complexity comparison with standard PR. We also briefly study the dynamic extension of LRPR. The LRPR problem occurs in phaseless dynamic imaging, e.g., Fourier ptychographic imaging of live biological specimens, where acquiring measurements is expensive. We should point out that LRPR is a very different problem than its $A_k =A$ version, or its $A_k = A$ and with-phase (linear) version, both of which have been extensively studied in the literature. |
| Tasks | Matrix Completion |
| Published | 2019-02-13 |
| URL | https://arxiv.org/abs/1902.04972v3 |
| https://arxiv.org/pdf/1902.04972v3.pdf | |
| PWC | https://paperswithcode.com/paper/phaseless-low-rank-matrix-recovery-and |
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Learning to Control in Metric Space with Optimal Regret
| Title | Learning to Control in Metric Space with Optimal Regret |
| Authors | Lin F. Yang, Chengzhuo Ni, Mengdi Wang |
| Abstract | We study online reinforcement learning for finite-horizon deterministic control systems with {\it arbitrary} state and action spaces. Suppose that the transition dynamics and reward function is unknown, but the state and action space is endowed with a metric that characterizes the proximity between different states and actions. We provide a surprisingly simple upper-confidence reinforcement learning algorithm that uses a function approximation oracle to estimate optimistic Q functions from experiences. We show that the regret of the algorithm after $K$ episodes is $O(HL(KH)^{\frac{d-1}{d}}) $ where $L$ is a smoothness parameter, and $d$ is the doubling dimension of the state-action space with respect to the given metric. We also establish a near-matching regret lower bound. The proposed method can be adapted to work for more structured transition systems, including the finite-state case and the case where value functions are linear combinations of features, where the method also achieve the optimal regret. |
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| Published | 2019-05-05 |
| URL | https://arxiv.org/abs/1905.01576v1 |
| https://arxiv.org/pdf/1905.01576v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-control-in-metric-space-with |
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