Paper Group ANR 496
Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces. Principal Component Properties of Adversarial Samples. Atrial Fibrillation Detection Using Deep Features and Convolutional Networks. Causal Belief Decomposition for Planning with Sensing: Completeness Results and Practical Approximation. Observe Before Play: Multi-armed B …
Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces
| Title | Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces |
| Authors | Ernesto De Vito, Nicole Mücke, Lorenzo Rosasco |
| Abstract | We study reproducing kernel Hilbert spaces (RKHS) on a Riemannian manifold. In particular, we discuss under which condition Sobolev spaces are RKHS and characterize their reproducing kernels. Further, we introduce and discuss a class of smoother RKHS that we call diffusion spaces. We illustrate the general results with a number of detailed examples. |
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| Published | 2019-05-27 |
| URL | https://arxiv.org/abs/1905.10913v1 |
| https://arxiv.org/pdf/1905.10913v1.pdf | |
| PWC | https://paperswithcode.com/paper/reproducing-kernel-hilbert-spaces-on |
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Principal Component Properties of Adversarial Samples
| Title | Principal Component Properties of Adversarial Samples |
| Authors | Malhar Jere, Sandro Herbig, Christine Lind, Farinaz Koushanfar |
| Abstract | Deep Neural Networks for image classification have been found to be vulnerable to adversarial samples, which consist of sub-perceptual noise added to a benign image that can easily fool trained neural networks, posing a significant risk to their commercial deployment. In this work, we analyze adversarial samples through the lens of their contributions to the principal components of each image, which is different than prior works in which authors performed PCA on the entire dataset. We investigate a number of state-of-the-art deep neural networks trained on ImageNet as well as several attacks for each of the networks. Our results demonstrate empirically that adversarial samples across several attacks have similar properties in their contributions to the principal components of neural network inputs. We propose a new metric for neural networks to measure their robustness to adversarial samples, termed the (k,p) point. We utilize this metric to achieve 93.36% accuracy in detecting adversarial samples independent of architecture and attack type for models trained on ImageNet. |
| Tasks | Image Classification |
| Published | 2019-12-07 |
| URL | https://arxiv.org/abs/1912.03406v1 |
| https://arxiv.org/pdf/1912.03406v1.pdf | |
| PWC | https://paperswithcode.com/paper/principal-component-properties-of-adversarial |
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Atrial Fibrillation Detection Using Deep Features and Convolutional Networks
| Title | Atrial Fibrillation Detection Using Deep Features and Convolutional Networks |
| Authors | Sara Ross-Howe, H. R. Tizhoosh |
| Abstract | Atrial fibrillation is a cardiac arrhythmia that affects an estimated 33.5 million people globally and is the potential cause of 1 in 3 strokes in people over the age of 60. Detection and diagnosis of atrial fibrillation (AFIB) is done noninvasively in the clinical environment through the evaluation of electrocardiograms (ECGs). Early research into automated methods for the detection of AFIB in ECG signals focused on traditional bio-medical signal analysis to extract important features for use in statistical classification models. Artificial intelligence models have more recently been used that employ convolutional and/or recurrent network architectures. In this work, significant time and frequency domain characteristics of the ECG signal are extracted by applying the short-time Fourier trans-form and then visually representing the information in a spectrogram. Two different classification approaches were investigated that utilized deep features in the spectrograms construct-ed from ECG segments. The first approach used a pretrained DenseNet model to extract features that were then classified using Support Vector Machines, and the second approach used the spectrograms as direct input into a convolutional network. Both approaches were evaluated against the MIT-BIH AFIB dataset, where the convolutional network approach achieved a classification accuracy of 93.16%. While these results do not surpass established automated atrial fibrillation detection methods, they are promising and warrant further investigation given they did not require any noise prefiltering, hand-crafted features, nor a reliance on beat detection. |
| Tasks | Arrhythmia Detection, Atrial Fibrillation Detection, Electrocardiography (ECG) |
| Published | 2019-03-28 |
| URL | http://arxiv.org/abs/1903.11775v1 |
| http://arxiv.org/pdf/1903.11775v1.pdf | |
| PWC | https://paperswithcode.com/paper/atrial-fibrillation-detection-using-deep |
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Causal Belief Decomposition for Planning with Sensing: Completeness Results and Practical Approximation
| Title | Causal Belief Decomposition for Planning with Sensing: Completeness Results and Practical Approximation |
| Authors | Blai Bonet, Hector Geffner |
| Abstract | Belief tracking is a basic problem in planning with sensing. While the problem is intractable, it has been recently shown that for both deterministic and non-deterministic systems expressed in compact form, it can be done in time and space that are exponential in the problem width. The width measures the maximum number of state variables that are all relevant to a given precondition or goal. In this work, we extend this result both theoretically and practically. First, we introduce an alternative decomposition scheme and algorithm with the same time complexity but different completeness guarantees, whose space complexity is much smaller: exponential in the causal width of the problem that measures the number of state variables that are causally relevant to a given precondition, goal, or observable. Second, we introduce a fast, meaningful, and powerful approximation that trades completeness by speed, and is both time and space exponential in the problem causal width. It is then shown empirically that the algorithm combined with simple heuristics yields state-of-the-art real-time performance in domains with high widths but low causal widths such as Minesweeper, Battleship, and Wumpus. |
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| Published | 2019-09-26 |
| URL | https://arxiv.org/abs/1909.13778v1 |
| https://arxiv.org/pdf/1909.13778v1.pdf | |
| PWC | https://paperswithcode.com/paper/causal-belief-decomposition-for-planning-with |
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Observe Before Play: Multi-armed Bandit with Pre-observations
| Title | Observe Before Play: Multi-armed Bandit with Pre-observations |
| Authors | Jinhang Zuo, Xiaoxi Zhang, Carlee Joe-Wong |
| Abstract | We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to pre-observe arm rewards before playing an arm in each round. Apart from the usual trade-off between exploring new arms to find the best one and exploiting the arm believed to offer the highest reward, we encounter an additional dilemma: pre-observing more arms gives a higher chance to play the best one, but incurs a larger cost. For the single-player setting, we design an Observe-Before-Play Upper Confidence Bound (OBP-UCB) algorithm for $K$ arms with Bernoulli rewards, and prove a $T$-round regret upper bound $O(K^2\log T)$. In the multi-player setting, collisions will occur when players select the same arm to play in the same round. We design a centralized algorithm, C-MP-OBP, and prove its $T$-round regret relative to an offline greedy strategy is upper bounded in $O(\frac{K^4}{M^2}\log T)$ for $K$ arms and $M$ players. We also propose distributed versions of the C-MP-OBP policy, called D-MP-OBP and D-MP-Adapt-OBP, achieving logarithmic regret with respect to collision-free target policies. Experiments on synthetic data and wireless channel traces show that C-MP-OBP and D-MP-OBP outperform random heuristics and offline optimal policies that do not allow pre-observations. |
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| Published | 2019-11-21 |
| URL | https://arxiv.org/abs/1911.09458v1 |
| https://arxiv.org/pdf/1911.09458v1.pdf | |
| PWC | https://paperswithcode.com/paper/observe-before-play-multi-armed-bandit-with |
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Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities
| Title | Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities |
| Authors | Wei Qian, Yuqian Zhang, Yudong Chen |
| Abstract | This work studies the location estimation problem for a mixture of two rotation invariant log-concave densities. We demonstrate that Least Squares EM, a variant of the EM algorithm, converges to the true location parameter from a randomly initialized point. We establish the explicit convergence rates and sample complexity bounds, revealing their dependence on the signal-to-noise ratio and the tail property of the log-concave distribution. Moreover, we show that this global convergence property is robust under model mis-specification. Our analysis generalizes previous techniques for proving the convergence results for Gaussian mixtures. In particular, we make use of an angle-decreasing property for establishing global convergence of Least Squares EM beyond Gaussian settings, as $\ell_2$ distance contraction no longer holds globally for general log-concave mixtures. |
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| Published | 2019-06-16 |
| URL | https://arxiv.org/abs/1906.06776v2 |
| https://arxiv.org/pdf/1906.06776v2.pdf | |
| PWC | https://paperswithcode.com/paper/global-convergence-of-least-squares-em-for |
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Marginalized Average Attentional Network for Weakly-Supervised Learning
| Title | Marginalized Average Attentional Network for Weakly-Supervised Learning |
| Authors | Yuan Yuan, Yueming Lyu, Xi Shen, Ivor W. Tsang, Dit-Yan Yeung |
| Abstract | In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions. To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the most salient regions in a principled manner. The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion. MAA samples multiple subsets from the video snippet features according to a set of latent discriminative probabilities and takes the expectation over all the averaged subset features. Theoretically, we prove that the MAA module with learned latent discriminative probabilities successfully reduces the difference in responses between the most salient regions and the others. Therefore, MAAN is able to generate better class activation sequences and identify dense and integral action regions in the videos. Moreover, we propose a fast algorithm to reduce the complexity of constructing MAA from O($2^T$) to O($T^2$). Extensive experiments on two large-scale video datasets show that our MAAN achieves superior performance on weakly-supervised temporal action localization |
| Tasks | Action Localization, Temporal Action Localization, Weakly Supervised Action Localization, Weakly-supervised Temporal Action Localization |
| Published | 2019-05-21 |
| URL | https://arxiv.org/abs/1905.08586v1 |
| https://arxiv.org/pdf/1905.08586v1.pdf | |
| PWC | https://paperswithcode.com/paper/marginalized-average-attentional-network-for-1 |
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SqueezeNAS: Fast neural architecture search for faster semantic segmentation
| Title | SqueezeNAS: Fast neural architecture search for faster semantic segmentation |
| Authors | Albert Shaw, Daniel Hunter, Forrest Iandola, Sammy Sidhu |
| Abstract | For real time applications utilizing Deep Neural Networks (DNNs), it is critical that the models achieve high-accuracy on the target task and low-latency inference on the target computing platform. While Neural Architecture Search (NAS) has been effectively used to develop low-latency networks for image classification, there has been relatively little effort to use NAS to optimize DNN architectures for other vision tasks. In this work, we present what we believe to be the first proxyless hardware-aware search targeted for dense semantic segmentation. With this approach, we advance the state-of-the-art accuracy for latency-optimized networks on the Cityscapes semantic segmentation dataset. Our latency-optimized small SqueezeNAS network achieves 68.02% validation class mIOU with less than 35 ms inference times on the NVIDIA AGX Xavier. Our latency-optimized large SqueezeNAS network achieves 73.62% class mIOU with less than 100 ms inference times. We demonstrate that significant performance gains are possible by utilizing NAS to find networks optimized for both the specific task and inference hardware. We also present detailed analysis comparing our networks to recent state-of-the-art architectures. |
| Tasks | Image Classification, Neural Architecture Search, Semantic Segmentation |
| Published | 2019-08-05 |
| URL | https://arxiv.org/abs/1908.01748v2 |
| https://arxiv.org/pdf/1908.01748v2.pdf | |
| PWC | https://paperswithcode.com/paper/squeezenas-fast-neural-architecture-search |
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Discrete and Continuous Action Representation for Practical RL in Video Games
| Title | Discrete and Continuous Action Representation for Practical RL in Video Games |
| Authors | Olivier Delalleau, Maxim Peter, Eloi Alonso, Adrien Logut |
| Abstract | While most current research in Reinforcement Learning (RL) focuses on improving the performance of the algorithms in controlled environments, the use of RL under constraints like those met in the video game industry is rarely studied. Operating under such constraints, we propose Hybrid SAC, an extension of the Soft Actor-Critic algorithm able to handle discrete, continuous and parameterized actions in a principled way. We show that Hybrid SAC can successfully solve a highspeed driving task in one of our games, and is competitive with the state-of-the-art on parameterized actions benchmark tasks. We also explore the impact of using normalizing flows to enrich the expressiveness of the policy at minimal computational cost, and identify a potential undesired effect of SAC when used with normalizing flows, that may be addressed by optimizing a different objective. |
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| Published | 2019-12-23 |
| URL | https://arxiv.org/abs/1912.11077v1 |
| https://arxiv.org/pdf/1912.11077v1.pdf | |
| PWC | https://paperswithcode.com/paper/discrete-and-continuous-action-representation |
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Exploiting Structure of Uncertainty for Efficient Matroid Semi-Bandits
| Title | Exploiting Structure of Uncertainty for Efficient Matroid Semi-Bandits |
| Authors | Pierre Perrault, Vianney Perchet, Michal Valko |
| Abstract | We improve the efficiency of algorithms for stochastic \emph{combinatorial semi-bandits}. In most interesting problems, state-of-the-art algorithms take advantage of structural properties of rewards, such as \emph{independence}. However, while being optimal in terms of asymptotic regret, these algorithms are inefficient. In our paper, we first reduce their implementation to a specific \emph{submodular maximization}. Then, in case of \emph{matroid} constraints, we design adapted approximation routines, thereby providing the first efficient algorithms that rely on reward structure to improve regret bound. In particular, we improve the state-of-the-art efficient gap-free regret bound by a factor $\sqrt{m}/\log m$, where $m$ is the maximum action size. Finally, we show how our improvement translates to more general \emph{budgeted combinatorial semi-bandits}. |
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| Published | 2019-02-11 |
| URL | https://arxiv.org/abs/1902.03794v2 |
| https://arxiv.org/pdf/1902.03794v2.pdf | |
| PWC | https://paperswithcode.com/paper/exploiting-structure-of-uncertainty-for |
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A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players
| Title | A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players |
| Authors | Etienne Boursier, Emilie Kaufmann, Abbas Mehrabian, Vianney Perchet |
| Abstract | We study a multiplayer stochastic multi-armed bandit problem in which players cannot communicate, and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. We consider the challenging heterogeneous setting, in which different arms may have different means for different players, and propose a new and efficient algorithm that combines the idea of leveraging forced collisions for implicit communication and that of performing matching eliminations. We present a finite-time analysis of our algorithm, giving the first sublinear minimax regret bound for this problem, and prove that if the optimal assignment of players to arms is unique, our algorithm attains the optimal $O(\ln(T))$ regret, solving an open question raised at NeurIPS 2018. |
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| Published | 2019-02-04 |
| URL | https://arxiv.org/abs/1902.01239v4 |
| https://arxiv.org/pdf/1902.01239v4.pdf | |
| PWC | https://paperswithcode.com/paper/new-algorithms-for-multiplayer-bandits-when |
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DPVis: Visual Exploration of Disease Progression Pathways
| Title | DPVis: Visual Exploration of Disease Progression Pathways |
| Authors | Bum Chul Kwon, Vibha Anand, Kristen A Severson, Soumya Ghosh, Zhaonan Sun, Brigitte I Frohnert, Markus Lundgren, Kenney Ng |
| Abstract | Clinical researchers use disease progression modeling algorithms to predict future patient status and characterize progression patterns. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make predictions concerning future states for new patients. HMMs can be trained using longitudinal observations of subjects from large-scale cohort studies, clinical trials, and electronic health records. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, complex modeling parameters, and clinically make sense of the patterns. To tackle this problem, we conducted a design study with physician scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of certain chronic diseases, namely type 1 diabetes (T1D), Huntington’s disease, Parkinson’s disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable, and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and designing and comparing clinically relevant subgroup cohorts by introducing a case study on observation data from clinical studies of T1D. |
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| Published | 2019-04-26 |
| URL | http://arxiv.org/abs/1904.11652v1 |
| http://arxiv.org/pdf/1904.11652v1.pdf | |
| PWC | https://paperswithcode.com/paper/dpvis-visual-exploration-of-disease |
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Event Ticket Price Prediction with Deep Neural Network on Spatial-Temporal Sparse Data
| Title | Event Ticket Price Prediction with Deep Neural Network on Spatial-Temporal Sparse Data |
| Authors | Fei Huang, Hao Huang |
| Abstract | Event ticket price prediction is important to marketing strategy for any sports team or musical ensemble. An accurate prediction model can help the marketing team to make promotion plan more effectively and efficiently. However, given all the historical transaction records, it is challenging to predict the sale price of the remaining seats at any future timestamp, not only because that the sale price is relevant to a lot of features (seat locations, date-to-event of the transaction, event date, team performance, etc.), but also because of the temporal and spatial sparsity in the dataset. For a game/concert, the ticket selling price of one seat is only observable once at the time of sale. Furthermore, some seats may not even be purchased (therefore no record available). In fact, data sparsity is commonly encountered in many prediction problems. Here, we propose a bi-level optimizing deep neural network to address the curse of spatio-temporal sparsity. Specifically, we introduce coarsening and refining layers, and design a bi-level loss function to integrate different level of loss for better prediction accuracy. Our model can discover the interrelations among ticket sale price, seat locations, selling time, event information, etc. Experiments show that our proposed model outperforms other benchmark methods in real-world ticket selling price prediction. |
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| Published | 2019-12-03 |
| URL | https://arxiv.org/abs/1912.01139v2 |
| https://arxiv.org/pdf/1912.01139v2.pdf | |
| PWC | https://paperswithcode.com/paper/event-ticket-price-prediction-with-deep |
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AI Safety for High Energy Physics
| Title | AI Safety for High Energy Physics |
| Authors | Benjamin Nachman, Chase Shimmin |
| Abstract | The field of high-energy physics (HEP), along with many scientific disciplines, is currently experiencing a dramatic influx of new methodologies powered by modern machine learning techniques. Over the last few years, a growing body of HEP literature has focused on identifying promising applications of deep learning in particular, and more recently these techniques are starting to be realized in an increasing number of experimental measurements. The overall conclusion from this impressive and extensive set of studies is that rarer and more complex physics signatures can be identified with the new set of powerful tools from deep learning. However, there is an unstudied systematic risk associated with combining the traditional HEP workflow and deep learning with high-dimensional data. In particular, calibrating and validating the response of deep neural networks is in general not experimentally feasible, and therefore current methods may be biased in ways that are not covered by current uncertainty estimates. By borrowing ideas from AI safety, we illustrate these potential issues and propose a method to bound the size of unaccounted for uncertainty. In addition to providing a pragmatic diagnostic, this work will hopefully begin a dialogue within the community about the robust application of deep learning to experimental analyses. |
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| Published | 2019-10-18 |
| URL | https://arxiv.org/abs/1910.08606v1 |
| https://arxiv.org/pdf/1910.08606v1.pdf | |
| PWC | https://paperswithcode.com/paper/ai-safety-for-high-energy-physics |
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Using local plasticity rules to train recurrent neural networks
| Title | Using local plasticity rules to train recurrent neural networks |
| Authors | Owen Marschall, Kyunghyun Cho, Cristina Savin |
| Abstract | To learn useful dynamics on long time scales, neurons must use plasticity rules that account for long-term, circuit-wide effects of synaptic changes. In other words, neural circuits must solve a credit assignment problem to appropriately assign responsibility for global network behavior to individual circuit components. Furthermore, biological constraints demand that plasticity rules are spatially and temporally local; that is, synaptic changes can depend only on variables accessible to the pre- and postsynaptic neurons. While artificial intelligence offers a computational solution for credit assignment, namely backpropagation through time (BPTT), this solution is wildly biologically implausible. It requires both nonlocal computations and unlimited memory capacity, as any synaptic change is a complicated function of the entire history of network activity. Similar nonlocality issues plague other approaches such as FORCE (Sussillo et al. 2009). Overall, we are still missing a model for learning in recurrent circuits that both works computationally and uses only local updates. Leveraging recent advances in machine learning on approximating gradients for BPTT, we derive biologically plausible plasticity rules that enable recurrent networks to accurately learn long-term dependencies in sequential data. The solution takes the form of neurons with segregated voltage compartments, with several synaptic sub-populations that have different functional properties. The network operates in distinct phases during which each synaptic sub-population is updated by its own local plasticity rule. Our results provide new insights into the potential roles of segregated dendritic compartments, branch-specific inhibition, and global circuit phases in learning. |
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| Published | 2019-05-28 |
| URL | https://arxiv.org/abs/1905.12100v1 |
| https://arxiv.org/pdf/1905.12100v1.pdf | |
| PWC | https://paperswithcode.com/paper/using-local-plasticity-rules-to-train |
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