Paper Group ANR 483
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier. Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier. Quality Measures for Speaker Verification with Short Utterances. A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free. Improving Feature Attribution …
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
| Title | Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier |
| Authors | Poojan Oza, Vishal M. Patel |
| Abstract | Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at: github.com/otkupjnoz/oc-acnn. |
| Tasks | One-class classifier |
| Published | 2019-03-04 |
| URL | http://arxiv.org/abs/1903.01031v1 |
| http://arxiv.org/pdf/1903.01031v1.pdf | |
| PWC | https://paperswithcode.com/paper/active-authentication-using-an-autoencoder |
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Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier
| Title | Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier |
| Authors | Joshua J. Engelsma, Anil K. Jain |
| Abstract | Prevailing fingerprint recognition systems are vulnerable to spoof attacks. To mitigate these attacks, automated spoof detectors are trained to distinguish a set of live or bona fide fingerprints from a set of known spoof fingerprints. Despite their success, spoof detectors remain vulnerable when exposed to attacks from spoofs made with materials not seen during training of the detector. To alleviate this shortcoming, we approach spoof detection as a one-class classification problem. The goal is to train a spoof detector on only the live fingerprints such that once the concept of “live” has been learned, spoofs of any material can be rejected. We accomplish this through training multiple generative adversarial networks (GANS) on live fingerprint images acquired with the open source, dual-camera, 1900 ppi RaspiReader fingerprint reader. Our experimental results, conducted on 5.5K spoof images (from 12 materials) and 11.8K live images show that the proposed approach improves the cross-material spoof detection performance over state-of-the-art one-class and binary class spoof detectors on 11 of 12 testing materials and 7 of 12 testing materials, respectively. |
| Tasks | One-class classifier |
| Published | 2019-01-13 |
| URL | http://arxiv.org/abs/1901.03918v2 |
| http://arxiv.org/pdf/1901.03918v2.pdf | |
| PWC | https://paperswithcode.com/paper/generalizing-fingerprint-spoof-detector |
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Quality Measures for Speaker Verification with Short Utterances
| Title | Quality Measures for Speaker Verification with Short Utterances |
| Authors | Arnab Poddar, Md Sahidullah, Goutam Saha |
| Abstract | The performances of the automatic speaker verification (ASV) systems degrade due to the reduction in the amount of speech used for enrollment and verification. Combining multiple systems based on different features and classifiers considerably reduces speaker verification error rate with short utterances. This work attempts to incorporate supplementary information during the system combination process. We use quality of the estimated model parameters as supplementary information. We introduce a class of novel quality measures formulated using the zero-order sufficient statistics used during the i-vector extraction process. We have used the proposed quality measures as side information for combining ASV systems based on Gaussian mixture model-universal background model (GMM-UBM) and i-vector. The proposed methods demonstrate considerable improvement in speaker recognition performance on NIST SRE corpora, especially in short duration conditions. We have also observed improvement over existing systems based on different duration-based quality measures. |
| Tasks | Speaker Recognition, Speaker Verification |
| Published | 2019-01-29 |
| URL | http://arxiv.org/abs/1901.10345v2 |
| http://arxiv.org/pdf/1901.10345v2.pdf | |
| PWC | https://paperswithcode.com/paper/quality-measures-for-speaker-verification |
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A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free
| Title | A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free |
| Authors | Yifang Chen, Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei |
| Abstract | We propose the first contextual bandit algorithm that is parameter-free, efficient, and optimal in terms of dynamic regret. Specifically, our algorithm achieves dynamic regret $\mathcal{O}(\min{\sqrt{ST}, \Delta^{\frac{1}{3}}T^{\frac{2}{3}}})$ for a contextual bandit problem with $T$ rounds, $S$ switches and $\Delta$ total variation in data distributions. Importantly, our algorithm is adaptive and does not need to know $S$ or $\Delta$ ahead of time, and can be implemented efficiently assuming access to an ERM oracle. Our results strictly improve the $\mathcal{O}(\min {S^{\frac{1}{4}}T^{\frac{3}{4}}, \Delta^{\frac{1}{5}}T^{\frac{4}{5}}})$ bound of (Luo et al., 2018), and greatly generalize and improve the $\mathcal{O}(\sqrt{ST})$ result of (Auer et al, 2018) that holds only for the two-armed bandit problem without contextual information. The key novelty of our algorithm is to introduce replay phases, in which the algorithm acts according to its previous decisions for a certain amount of time in order to detect non-stationarity while maintaining a good balance between exploration and exploitation. |
| Tasks | Multi-Armed Bandits |
| Published | 2019-02-03 |
| URL | https://arxiv.org/abs/1902.00980v3 |
| https://arxiv.org/pdf/1902.00980v3.pdf | |
| PWC | https://paperswithcode.com/paper/a-new-algorithm-for-non-stationary-contextual |
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Improving Feature Attribution through Input-specific Network Pruning
| Title | Improving Feature Attribution through Input-specific Network Pruning |
| Authors | Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, Nassir Navab |
| Abstract | Attributing the output of a neural network to the contribution of given input elements is a way of shedding light on the black-box nature of neural networks. Due to the complexity of current network architectures, current gradient-based attribution methods provide very noisy or coarse results. We propose to prune a neural network for a given single input to keep only neurons that highly contribute to the prediction. We show that by input-specific pruning, network gradients change from reflecting local (noisy) importance information to global importance. Our proposed method is efficient and generates fine-grained attribution maps. We further provide a theoretical justification of the pruning approach relating it to perturbations and validate it through a novel experimental setup. Our method is evaluated by multiple benchmarks: sanity checks, pixel perturbation, and Remove-and-Retrain (ROAR). These benchmarks evaluate the method from different perspectives and our method performs better than other methods across all evaluations. |
| Tasks | Network Pruning |
| Published | 2019-11-25 |
| URL | https://arxiv.org/abs/1911.11081v2 |
| https://arxiv.org/pdf/1911.11081v2.pdf | |
| PWC | https://paperswithcode.com/paper/explaining-neural-networks-via-perturbing |
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Real-time Bidding campaigns optimization using attribute selection
| Title | Real-time Bidding campaigns optimization using attribute selection |
| Authors | Luis Miralles, M. Atif Qureshi, Brian Mac Namee |
| Abstract | Real-Time Bidding is nowadays one of the most promising systems in the online advertising ecosystem. In the presented study, the performance of RTB campaigns is improved by optimising the parameters of the users’ profiles and the publishers’ websites. Most studies about optimising RTB campaigns are focused on the bidding strategy. In contrast, the objective of our research consists of optimising RTB campaigns by finding out configurations that maximise both the number of impressions and their average profitability. The experiments demonstrate that, when the number of required visits by advertisers is low, it is easy to find configurations with high average profitability, but as the required number of visits increases, the average profitability tends to go down. Additionally, configuration optimisation has been combined with other interesting strategies to increase, even more, the campaigns’ profitability. Along with parameter configuration the study considers the following complementary strategies to increase profitability: i) selecting multiple configurations with a small number of visits instead of a unique configuration with a large number, ii) discarding visits according to the thresholds of cost and profitability, iii) analysing a reduced space of the dataset and extrapolating the solution, and iv) increasing the search space by including solutions below the required number of visits. The developed campaign optimisation methodology could be offered by RTB platforms to advertisers to make their campaigns more profitable. |
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| Published | 2019-10-29 |
| URL | https://arxiv.org/abs/1910.13292v1 |
| https://arxiv.org/pdf/1910.13292v1.pdf | |
| PWC | https://paperswithcode.com/paper/191013292 |
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Beyond Exponentially Discounted Sum: Automatic Learning of Return Function
| Title | Beyond Exponentially Discounted Sum: Automatic Learning of Return Function |
| Authors | Yufei Wang, Qiwei Ye, Tie-Yan Liu |
| Abstract | In reinforcement learning, Return, which is the weighted accumulated future rewards, and Value, which is the expected return, serve as the objective that guides the learning of the policy. In classic RL, return is defined as the exponentially discounted sum of future rewards. One key insight is that there could be many feasible ways to define the form of the return function (and thus the value), from which the same optimal policy can be derived, yet these different forms might render dramatically different speeds of learning this policy. In this paper, we research how to modify the form of the return function to enhance the learning towards the optimal policy. We propose to use a general mathematical form for return function, and employ meta-learning to learn the optimal return function in an end-to-end manner. We test our methods on a specially designed maze environment and several Atari games, and our experimental results clearly indicate the advantages of automatically learning optimal return functions in reinforcement learning. |
| Tasks | Atari Games, Meta-Learning |
| Published | 2019-05-28 |
| URL | https://arxiv.org/abs/1905.11591v1 |
| https://arxiv.org/pdf/1905.11591v1.pdf | |
| PWC | https://paperswithcode.com/paper/beyond-exponentially-discounted-sum-automatic |
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A Machine Learning Framework for Authorship Identification From Texts
| Title | A Machine Learning Framework for Authorship Identification From Texts |
| Authors | Rahul Radhakrishnan Iyer, Carolyn Penstein Rose |
| Abstract | Authorship identification is a process in which the author of a text is identified. Most known literary texts can easily be attributed to a certain author because they are, for example, signed. Yet sometimes we find unfinished pieces of work or a whole bunch of manuscripts with a wide variety of possible authors. In order to assess the importance of such a manuscript, it is vital to know who wrote it. In this work, we aim to develop a machine learning framework to effectively determine authorship. We formulate the task as a single-label multi-class text categorization problem and propose a supervised machine learning framework incorporating stylometric features. This task is highly interdisciplinary in that it takes advantage of machine learning, information retrieval, and natural language processing. We present an approach and a model which learns the differences in writing style between $50$ different authors and is able to predict the author of a new text with high accuracy. The accuracy is seen to increase significantly after introducing certain linguistic stylometric features along with text features. |
| Tasks | Information Retrieval, Text Categorization |
| Published | 2019-12-21 |
| URL | https://arxiv.org/abs/1912.10204v1 |
| https://arxiv.org/pdf/1912.10204v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-machine-learning-framework-for-authorship |
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Iteratively Training Look-Up Tables for Network Quantization
| Title | Iteratively Training Look-Up Tables for Network Quantization |
| Authors | Fabien Cardinaux, Stefan Uhlich, Kazuki Yoshiyama, Javier Alonso Garcia, Lukas Mauch, Stephen Tiedemann, Thomas Kemp, Akira Nakamura |
| Abstract | Operating deep neural networks (DNNs) on devices with limited resources requires the reduction of their memory as well as computational footprint. Popular reduction methods are network quantization or pruning, which either reduce the word length of the network parameters or remove weights from the network if they are not needed. In this article we discuss a general framework for network reduction which we call Look-Up Table Quantization (LUT-Q). For each layer, we learn a value dictionary and an assignment matrix to represent the network weights. We propose a special solver which combines gradient descent and a one-step k-means update to learn both the value dictionaries and assignment matrices iteratively. This method is very flexible: by constraining the value dictionary, many different reduction problems such as non-uniform network quantization, training of multiplierless networks, network pruning or simultaneous quantization and pruning can be implemented without changing the solver. This flexibility of the LUT-Q method allows us to use the same method to train networks for different hardware capabilities. |
| Tasks | Network Pruning, Quantization |
| Published | 2019-11-12 |
| URL | https://arxiv.org/abs/1911.04951v1 |
| https://arxiv.org/pdf/1911.04951v1.pdf | |
| PWC | https://paperswithcode.com/paper/iteratively-training-look-up-tables-for-1 |
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Report on the First HIPstIR Workshop on the Future of Information Retrieval
| Title | Report on the First HIPstIR Workshop on the Future of Information Retrieval |
| Authors | Laura Dietz, Bhaskar Mitra, Jeremy Pickens, Hana Anber, Sandeep Avula, Asia Biega, Adrian Boteanu, Shubham Chatterjee, Jeff Dalton, Shiri Dori-Hacohen, John Foley, Henry Feild, Ben Gamari, Rosie Jones, Pallika Kanani, Sumanta Kashyapi, Widad Machmouchi, Matthew Mitsui, Steve Nole, Alexandre Tachard Passos, Jordan Ramsdell, Adam Roegiest, David Smith, Alessandro Sordoni |
| Abstract | The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR. The first iteration of this vision materialized in the form of a three day workshop in Portsmouth, New Hampshire attended by 24 researchers across academia and industry. Attendees pre-submitted one or more topics that they want to pitch at the meeting. Then over the three days during the workshop, we self-organized into groups and worked on six specific proposals of common interest. In this report, we present an overview of the workshop and brief summaries of the six proposals that resulted from the workshop. |
| Tasks | Information Retrieval |
| Published | 2019-12-20 |
| URL | https://arxiv.org/abs/1912.09910v1 |
| https://arxiv.org/pdf/1912.09910v1.pdf | |
| PWC | https://paperswithcode.com/paper/report-on-the-first-hipstir-workshop-on-the |
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On Event Causality Detection in Tweets
| Title | On Event Causality Detection in Tweets |
| Authors | Humayun Kayesh, Md. Saiful Islam, Junhu Wang |
| Abstract | Nowadays, Twitter has become a great source of user-generated information about events. Very often people report causal relationships between events in their tweets. Automatic detection of causality information in these events might play an important role in predictive event analytics. Existing approaches include both rule-based and data-driven supervised methods. However, it is challenging to correctly identify event causality using only linguistic rules due to the highly unstructured nature and grammatical incorrectness of social media short text such as tweets. Also, it is difficult to develop a data-driven supervised method for event causality detection in tweets due to insufficient contextual information. This paper proposes a novel event context word extension technique based on background knowledge. To demonstrate the effectiveness of our proposed event context word extension technique, we develop a feed-forward neural network based approach to detect event causality from tweets. Extensive experiments demonstrate the superiority of our approach. |
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| Published | 2019-01-11 |
| URL | http://arxiv.org/abs/1901.03526v1 |
| http://arxiv.org/pdf/1901.03526v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-event-causality-detection-in-tweets |
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A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning
| Title | A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning |
| Authors | Nicolas Carion, Gabriel Synnaeve, Alessandro Lazaric, Nicolas Usunier |
| Abstract | Effective coordination is crucial to solve multi-agent collaborative (MAC) problems. While centralized reinforcement learning methods can optimally solve small MAC instances, they do not scale to large problems and they fail to generalize to scenarios different from those seen during training. In this paper, we consider MAC problems with some intrinsic notion of locality (e.g., geographic proximity) such that interactions between agents and tasks are locally limited. By leveraging this property, we introduce a novel structured prediction approach to assign agents to tasks. At each step, the assignment is obtained by solving a centralized optimization problem (the inference procedure) whose objective function is parameterized by a learned scoring model. We propose different combinations of inference procedures and scoring models able to represent coordination patterns of increasing complexity. The resulting assignment policy can be efficiently learned on small problem instances and readily reused in problems with more agents and tasks (i.e., zero-shot generalization). We report experimental results on a toy search and rescue problem and on several target selection scenarios in StarCraft: Brood War, in which our model significantly outperforms strong rule-based baselines on instances with 5 times more agents and tasks than those seen during training. |
| Tasks | Multi-agent Reinforcement Learning, Starcraft, Structured Prediction |
| Published | 2019-10-19 |
| URL | https://arxiv.org/abs/1910.08809v1 |
| https://arxiv.org/pdf/1910.08809v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-structured-prediction-approach-for |
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Sensor-Augmented Neural Adaptive Bitrate Video Streaming on UAVs
| Title | Sensor-Augmented Neural Adaptive Bitrate Video Streaming on UAVs |
| Authors | Xuedou Xiao, Wei Wang, Taobin Chen, Yang Cao, Tao Jiang, Qian Zhang |
| Abstract | Recent advances in unmanned aerial vehicle (UAV) technology have revolutionized a broad class of civil and military applications. However, the designs of wireless technologies that enable real-time streaming of high-definition video between UAVs and ground clients present a conundrum. Most existing adaptive bitrate (ABR) algorithms are not optimized for the air-to-ground links, which usually fluctuate dramatically due to the dynamic flight states of the UAV. In this paper, we present SA-ABR, a new sensor-augmented system that generates ABR video streaming algorithms with the assistance of various kinds of inherent sensor data that are used to pilot UAVs. By incorporating the inherent sensor data with network observations, SA-ABR trains a deep reinforcement learning (DRL) model to extract salient features from the flight state information and automatically learn an ABR algorithm to adapt to the varying UAV channel capacity through the training process. SA-ABR does not rely on any assumptions or models about UAV’s flight states or the environment, but instead, it makes decisions by exploiting temporal properties of past throughput through the long short-term memory (LSTM) to adapt itself to a wide range of highly dynamic environments. We have implemented SA-ABR in a commercial UAV and evaluated it in the wild. We compare SA-ABR with a variety of existing state-of-the-art ABR algorithms, and the results show that our system outperforms the best known existing ABR algorithm by 21.4% in terms of the average quality of experience (QoE) reward. |
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| Published | 2019-09-23 |
| URL | https://arxiv.org/abs/1909.10914v1 |
| https://arxiv.org/pdf/1909.10914v1.pdf | |
| PWC | https://paperswithcode.com/paper/sensor-augmented-neural-adaptive-bitrate |
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IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election
| Title | IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election |
| Authors | Indu Manickam, Andrew S. Lan, Gautam Dasarathy, Richard G. Baraniuk |
| Abstract | The 2016 United States presidential election has been characterized as a period of extreme divisiveness that was exacerbated on social media by the influence of fake news, trolls, and social bots. However, the extent to which the public became more polarized in response to these influences over the course of the election is not well understood. In this paper we propose IdeoTrace, a framework for (i) jointly estimating the ideology of social media users and news websites and (ii) tracing changes in user ideology over time. We apply this framework to the last two months of the election period for a group of 47508 Twitter users and demonstrate that both liberal and conservative users became more polarized over time. |
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| Published | 2019-05-21 |
| URL | https://arxiv.org/abs/1905.08831v2 |
| https://arxiv.org/pdf/1905.08831v2.pdf | |
| PWC | https://paperswithcode.com/paper/ideotrace-a-framework-for-ideology-tracing |
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Fast Transient Simulation of High-Speed Channels Using Recurrent Neural Network
| Title | Fast Transient Simulation of High-Speed Channels Using Recurrent Neural Network |
| Authors | Thong Nguyen, Tianjian Lu, Ken Wu, Jose Schutt-Aine |
| Abstract | Generating eye diagrams by using a circuit simulator can be very computationally intensive, especially in the presence of nonlinearities. It often involves multiple Newton-like iterations at every time step when a SPICE-like circuit simulator handles a nonlinear system in the transient regime. In this paper, we leverage machine learning methods, to be specific, the recurrent neural network (RNN), to generate black-box macromodels and achieve significant reduction of computation time. Through the proposed approach, an RNN model is first trained and then validated on a relatively short sequence generated from a circuit simulator. Once the training completes, the RNN can be used to make predictions on the remaining sequence in order to generate an eye diagram. The training cost can also be amortized when the trained RNN starts making predictions. Besides, the proposed approach requires no complex circuit simulations nor substantial domain knowledge. We use two high-speed link examples to demonstrate that the proposed approach provides adequate accuracy while the computation time can be dramatically reduced. In the high-speed link example with a PAM4 driver, the eye diagram generated by RNN models shows good agreement with that obtained from a commercial circuit simulator. This paper also investigates the impacts of various RNN topologies, training schemes, and tunable parameters on both the accuracy and the generalization capability of an RNN model. It is found out that the long short-term memory (LSTM) network outperforms the vanilla RNN in terms of the accuracy in predicting transient waveforms. |
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| Published | 2019-01-25 |
| URL | http://arxiv.org/abs/1902.02627v2 |
| http://arxiv.org/pdf/1902.02627v2.pdf | |
| PWC | https://paperswithcode.com/paper/fast-transient-simulation-of-high-speed |
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