Paper Group ANR 823
Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning. Fairness and Diversity in the Recommendation and Ranking of Participatory Media Content. ProLFA: Representative Prototype Selection for Local Feature Aggregation. JUMT at WMT2019 News Translation Task: A Hybrid approach to Machine Translation for Lithuanian to …
Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning
| Title | Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning |
| Authors | Smitha Milli, Anca D. Dragan |
| Abstract | It is incredibly easy for a system designer to misspecify the objective for an autonomous system (“robot’'), thus motivating the desire to have the robot learn the objective from human behavior instead. Recent work has suggested that people have an interest in the robot performing well, and will thus behave pedagogically, choosing actions that are informative to the robot. In turn, robots benefit from interpreting the behavior by accounting for this pedagogy. In this work, we focus on misspecification: we argue that robots might not know whether people are being pedagogic or literal and that it is important to ask which assumption is safer to make. We cast objective learning into the more general form of a common-payoff game between the robot and human, and prove that in any such game literal interpretation is more robust to misspecification. Experiments with human data support our theoretical results and point to the sensitivity of the pedagogic assumption. |
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| Published | 2019-03-09 |
| URL | https://arxiv.org/abs/1903.03877v2 |
| https://arxiv.org/pdf/1903.03877v2.pdf | |
| PWC | https://paperswithcode.com/paper/literal-or-pedagogic-human-analyzing-human |
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Fairness and Diversity in the Recommendation and Ranking of Participatory Media Content
| Title | Fairness and Diversity in the Recommendation and Ranking of Participatory Media Content |
| Authors | Muskaan, Mehak Preet Dhaliwal, Aaditeshwar Seth |
| Abstract | Online participatory media platforms that enable one-to-many communication among users, see a significant amount of user generated content and consequently face a problem of being able to recommend a subset of this content to its users. We address the problem of recommending and ranking this content such that different viewpoints about a topic get exposure in a fair and diverse manner. We build our model in the context of a voice-based participatory media platform running in rural central India, for low-income and less-literate communities, that plays audio messages in a ranked list to users over a phone call and allows them to contribute their own messages. In this paper, we describe our model and evaluate it using call-logs from the platform, to compare the fairness and diversity performance of our model with the manual editorial processes currently being followed. Our models are generic and can be adapted and applied to other participatory media platforms as well. |
| Tasks | |
| Published | 2019-07-16 |
| URL | https://arxiv.org/abs/1907.07253v1 |
| https://arxiv.org/pdf/1907.07253v1.pdf | |
| PWC | https://paperswithcode.com/paper/fairness-and-diversity-in-the-recommendation |
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ProLFA: Representative Prototype Selection for Local Feature Aggregation
| Title | ProLFA: Representative Prototype Selection for Local Feature Aggregation |
| Authors | Xingxing Zhang, Zhenfeng Zhu, Yao Zhao |
| Abstract | Given a set of hand-crafted local features, acquiring a global representation via aggregation is a promising technique to boost computational efficiency and improve task performance. Existing feature aggregation (FA) approaches, including Bag of Words and Fisher Vectors, usually fail to capture the desired information due to their pipeline mode. In this paper, we propose a generic formulation to provide a systematical solution (named ProLFA) to aggregate local descriptors. It is capable of producing compact yet interpretable representations by selecting representative prototypes from numerous descriptors, under relaxed exclusivity constraint. Meanwhile, to strengthen the discriminability of the aggregated representation, we rationally enforce the domain-invariant projection of bundled descriptors along a task-specific direction. Furthermore, ProLFA is also provided with a powerful generalization ability to deal flexibly with the semi-supervised and fully supervised scenarios in local feature aggregation. Experimental results on various descriptors and tasks demonstrate that the proposed ProLFA is considerably superior over currently available alternatives about feature aggregation. |
| Tasks | |
| Published | 2019-10-24 |
| URL | https://arxiv.org/abs/1910.11010v1 |
| https://arxiv.org/pdf/1910.11010v1.pdf | |
| PWC | https://paperswithcode.com/paper/prolfa-representative-prototype-selection-for |
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JUMT at WMT2019 News Translation Task: A Hybrid approach to Machine Translation for Lithuanian to English
| Title | JUMT at WMT2019 News Translation Task: A Hybrid approach to Machine Translation for Lithuanian to English |
| Authors | Sainik Kumar Mahata, Avishek Garain, Adityar Rayala, Dipankar Das, Sivaji Bandyopadhyay |
| Abstract | In the current work, we present a description of the system submitted to WMT 2019 News Translation Shared task. The system was created to translate news text from Lithuanian to English. To accomplish the given task, our system used a Word Embedding based Neural Machine Translation model to post edit the outputs generated by a Statistical Machine Translation model. The current paper documents the architecture of our model, descriptions of the various modules and the results produced using the same. Our system garnered a BLEU score of 17.6. |
| Tasks | Machine Translation |
| Published | 2019-08-01 |
| URL | https://arxiv.org/abs/1908.01349v1 |
| https://arxiv.org/pdf/1908.01349v1.pdf | |
| PWC | https://paperswithcode.com/paper/jumt-at-wmt2019-news-translation-task-a |
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A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders
| Title | A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders |
| Authors | Rohit Voleti, Julie M. Liss, Visar Berisha |
| Abstract | It is widely accepted that information derived from analyzing speech (the acoustic signal) and language production (words and sentences) serves as a useful window into the health of an individual’s cognitive ability. In fact, most neuropsychological testing batteries have a component related to speech and language where clinicians elicit speech from patients for subjective evaluation across a broad set of dimensions. With advances in speech signal processing and natural language processing, there has been recent interest in developing tools to detect more subtle changes in cognitive-linguistic function. This work relies on extracting a set of features from recorded and transcribed speech for objective assessments of speech and language, early diagnosis of neurological disease, and tracking of disease after diagnosis. With an emphasis on cognitive and thought disorders, in this paper we provide a review of existing speech and language features used in this domain, discuss their clinical application, and highlight their advantages and disadvantages. Broadly speaking, the review is split into two categories: language features based on natural language processing and speech features based on speech signal processing. Within each category, we consider features that aim to measure complementary dimensions of cognitive-linguistics, including language diversity, syntactic complexity, semantic coherence, and timing. We conclude the review with a proposal of new research directions to further advance the field. |
| Tasks | |
| Published | 2019-06-04 |
| URL | https://arxiv.org/abs/1906.01157v2 |
| https://arxiv.org/pdf/1906.01157v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-review-of-language-and-speech-features-for |
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Virtual-Blind-Road Following Based Wearable Navigation Device for Blind People
| Title | Virtual-Blind-Road Following Based Wearable Navigation Device for Blind People |
| Authors | Jinqiang Bai, Shiguo Lian, Zhaoxiang Liu, Kai Wang, Dijun Liu |
| Abstract | To help the blind people walk to the destination efficiently and safely in indoor environment, a novel wearable navigation device is presented in this paper. The locating, way-finding, route following and obstacle avoiding modules are the essential components in a navigation system, while it remains a challenging task to consider obstacle avoiding during route following, as the indoor environment is complex, changeable and possibly with dynamic objects. To address this issue, we propose a novel scheme which utilizes a dynamic sub-goal selecting strategy to guide the users to the destination and help them bypass obstacles at the same time. This scheme serves as the key component of a complete navigation system deployed on a pair of wearable optical see-through glasses for the ease of use of blind people’s daily walks. The proposed navigation device has been tested on a collection of individuals and proved to be effective on indoor navigation tasks. The sensors embedded are of low cost, small volume and easy integration, making it possible for the glasses to be widely used as a wearable consumer device. |
| Tasks | |
| Published | 2019-04-30 |
| URL | http://arxiv.org/abs/1904.13028v1 |
| http://arxiv.org/pdf/1904.13028v1.pdf | |
| PWC | https://paperswithcode.com/paper/virtual-blind-road-following-based-wearable |
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On Efficient Multilevel Clustering via Wasserstein Distances
| Title | On Efficient Multilevel Clustering via Wasserstein Distances |
| Authors | Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, and Dinh Phung |
| Abstract | We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a potentially large hierarchically structured corpus of data. Our method involves a joint optimization formulation over several spaces of discrete probability measures, which are endowed with Wasserstein distance metrics. We propose several variants of this problem, which admit fast optimization algorithms, by exploiting the connection to the problem of finding Wasserstein barycenters. Consistency properties are established for the estimates of both local and global clusters. Finally, the experimental results with both synthetic and real data are presented to demonstrate the flexibility and scalability of the proposed approach. |
| Tasks | |
| Published | 2019-09-19 |
| URL | https://arxiv.org/abs/1909.08787v1 |
| https://arxiv.org/pdf/1909.08787v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-efficient-multilevel-clustering-via |
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Truthful and Faithful Monetary Policy for a Stablecoin Conducted by a Decentralised, Encrypted Artificial Intelligence
| Title | Truthful and Faithful Monetary Policy for a Stablecoin Conducted by a Decentralised, Encrypted Artificial Intelligence |
| Authors | David Cerezo Sánchez |
| Abstract | The Holy Grail of a decentralised stablecoin is achieved on rigorous mathematical frameworks, obtaining multiple advantageous proofs: stability, convergence, truthfulness, faithfulness, and malicious-security. These properties could only be attained by the novel and interdisciplinary combination of previously unrelated fields: model predictive control, deep learning, alternating direction method of multipliers (consensus-ADMM), mechanism design, secure multi-party computation, and zero-knowledge proofs. For the first time, this paper proves: - the feasibility of decentralising the central bank while securely preserving its independence in a decentralised computation setting - the benefits for price stability of combining mechanism design, provable security, and control theory, unlike the heuristics of previous stablecoins - the implementation of complex monetary policies on a stablecoin, equivalent to the ones used by central banks and beyond the current fixed rules of cryptocurrencies that hinder their price stability - methods to circumvent the impossibilities of Guaranteed Output Delivery (G.O.D.) and fairness: standing on truthfulness and faithfulness, we reach G.O.D. and fairness under the assumption of rational parties As a corollary, a decentralised artificial intelligence is able to conduct the monetary policy of a stablecoin, minimising human intervention. |
| Tasks | |
| Published | 2019-09-16 |
| URL | https://arxiv.org/abs/1909.07445v1 |
| https://arxiv.org/pdf/1909.07445v1.pdf | |
| PWC | https://paperswithcode.com/paper/truthful-and-faithful-monetary-policy-for-a |
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Enhancing the long-term performance of recommender system
| Title | Enhancing the long-term performance of recommender system |
| Authors | Leyang Xue, Peng Zhang, An Zeng |
| Abstract | Recommender system is a critically important tool in online commercial system and provide users with personalized recommendation on items. So far, numerous recommendation algorithms have been made to further improve the recommendation performance in a single-step recommendation, while the long-term recommendation performance is neglected. In this paper, we proposed an approach called Adjustment of Recommendation List (ARL) to enhance the long-term recommendation accuracy. In order to observe the long-term accuracy, we developed an evolution model of network to simulate the interaction between the recommender system and user’s behaviour. The result shows that not only long-term recommendation accuracy can be enhanced significantly but the diversity of item in online system maintains healthy. Notably, an optimal parameter n* of ARL existed in long-term recommendation, indicating that there is a trade-off between keeping diversity of item and user’s preference to maximize the long-term recommendation accuracy. Finally, we confirmed that the optimal parameter n* is stable during evolving network, which reveals the robustness of ARL method. |
| Tasks | Recommendation Systems |
| Published | 2019-04-01 |
| URL | http://arxiv.org/abs/1904.00672v1 |
| http://arxiv.org/pdf/1904.00672v1.pdf | |
| PWC | https://paperswithcode.com/paper/enhancing-the-long-term-performance-of |
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Minimizing the Negative Side Effects of Planning with Reduced Models
| Title | Minimizing the Negative Side Effects of Planning with Reduced Models |
| Authors | Sandhya Saisubramanian, Shlomo Zilberstein |
| Abstract | Reduced models of large Markov decision processes accelerate planning by considering a subset of outcomes for each state-action pair. This reduction in reachable states leads to replanning when the agent encounters states without a precomputed action during plan execution. However, not all states are suitable for replanning. In the worst case, the agent may not be able to reach the goal from the newly encountered state. Agents should be better prepared to handle such risky situations and avoid replanning in risky states. Hence, we consider replanning in states that are unsafe for deliberation as a negative side effect of planning with reduced models. While the negative side effects can be minimized by always using the full model, this defeats the purpose of using reduced models. The challenge is to plan with reduced models, but somehow account for the possibility of encountering risky situations. An agent should thus only replan in states that the user has approved as safe for replanning. To that end, we propose planning using a portfolio of reduced models, a planning paradigm that minimizes the negative side effects of planning using reduced models by alternating between different outcome selection approaches. We empirically demonstrate the effectiveness of our approach on three domains: an electric vehicle charging domain using real-world data from a university campus and two benchmark planning problems. |
| Tasks | |
| Published | 2019-05-22 |
| URL | https://arxiv.org/abs/1905.09355v1 |
| https://arxiv.org/pdf/1905.09355v1.pdf | |
| PWC | https://paperswithcode.com/paper/minimizing-the-negative-side-effects-of |
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Robust and High Performance Face Detector
| Title | Robust and High Performance Face Detector |
| Authors | Yundong Zhang, Xiang Xu, Xiaotao Liu |
| Abstract | In recent years, face detection has experienced significant performance improvement with the boost of deep convolutional neural networks. In this report, we reimplement the state-of-the-art detector SRN and apply some tricks proposed in the recent literatures to obtain an extremely strong face detector, named VIM-FD. In specific, we exploit more powerful backbone network like DenseNet-121, revisit the data augmentation based on data-anchor-sampling proposed in PyramidBox, and use the max-in-out label and anchor matching strategy in SFD. In addition, we also introduce the attention mechanism to provide additional supervision. Over the most popular and challenging face detection benchmark, i.e., WIDER FACE, the proposed VIM-FD achieves state-of-the-art performance. |
| Tasks | Data Augmentation, Face Detection |
| Published | 2019-01-06 |
| URL | http://arxiv.org/abs/1901.02350v1 |
| http://arxiv.org/pdf/1901.02350v1.pdf | |
| PWC | https://paperswithcode.com/paper/robust-and-high-performance-face-detector |
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Latent Semantic Search and Information Extraction Architecture
| Title | Latent Semantic Search and Information Extraction Architecture |
| Authors | Anton Kolonin |
| Abstract | The motivation, concept, design and implementation of latent semantic search for search engines have limited semantic search, entity extraction and property attribution features, have insufficient accuracy and response time of latent search, may impose privacy concerns and the search results are unavailable in offline mode for robotic search operations. The alternative suggestion involves autonomous search engine with adaptive storage consumption, configurable search scope and latent search response time with built-in options for entity extraction and property attribution available as open source platform for mobile, desktop and server solutions. The suggested architecture attempts to implement artificial general intelligence (AGI) principles as long as autonomous behaviour constrained by limited resources is concerned, and it is applied for specific task of enabling Web search for artificial agents implementing the AGI. |
| Tasks | Entity Extraction |
| Published | 2019-11-30 |
| URL | https://arxiv.org/abs/1912.00180v1 |
| https://arxiv.org/pdf/1912.00180v1.pdf | |
| PWC | https://paperswithcode.com/paper/latent-semantic-search-and-information |
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Precise Performance Analysis of the Box-Elastic Net under Matrix Uncertainties
| Title | Precise Performance Analysis of the Box-Elastic Net under Matrix Uncertainties |
| Authors | Ayed M. Alrashdi, Ismail Ben Atitallah, Tareq Y. Al-Naffouri |
| Abstract | In this letter, we consider the problem of recovering an unknown sparse signal from noisy linear measurements, using an enhanced version of the popular Elastic-Net (EN) method. We modify the EN by adding a box-constraint, and we call it the Box-Elastic Net (Box-EN). We assume independent identically distributed (iid) real Gaussian measurement matrix with additive Gaussian noise. In many practical situations, the measurement matrix is not perfectly known, and so we only have a noisy estimate of it. In this work, we precisely characterize the mean squared error and the probability of support recovery of the Box-Elastic Net in the high-dimensional asymptotic regime. Numerical simulations validate the theoretical predictions derived in the paper and also show that the boxed variant outperforms the standard EN. |
| Tasks | |
| Published | 2019-01-11 |
| URL | http://arxiv.org/abs/1901.04469v3 |
| http://arxiv.org/pdf/1901.04469v3.pdf | |
| PWC | https://paperswithcode.com/paper/precise-performance-analysis-of-the-box |
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Function Space Pooling For Graph Convolutional Networks
| Title | Function Space Pooling For Graph Convolutional Networks |
| Authors | Padraig Corcoran |
| Abstract | Convolutional layers in graph neural networks are a fundamental type of layer which output a representation or embedding of each graph vertex. The representation typically encodes information about the vertex in question and its neighbourhood. If one wishes to perform a graph centric task such as graph classification the set of vertex representations must be integrated or pooled to form a graph representation. We propose a novel pooling method which transforms a set of vertex representations into a function space representation. Experiential results demonstrate that the proposed method outperforms standard pooling methods of computing the sum and mean vertex representation. |
| Tasks | Graph Classification |
| Published | 2019-05-15 |
| URL | https://arxiv.org/abs/1905.06259v1 |
| https://arxiv.org/pdf/1905.06259v1.pdf | |
| PWC | https://paperswithcode.com/paper/function-space-pooling-for-graph |
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Deep learning based mood tagging for Chinese song lyrics
| Title | Deep learning based mood tagging for Chinese song lyrics |
| Authors | Jie Wang, Yilin Yang |
| Abstract | Nowadays, listening music has been and will always be an indispensable part of our daily life. In recent years, sentiment analysis of music has been widely used in the information retrieval systems, personalized recommendation systems and so on. Due to the development of deep learning, this paper commits to find an effective approach for mood tagging of Chinese song lyrics. To achieve this goal, both machine-learning and deep-learning models have been studied and compared. Eventually, a CNN-based model with pre-trained word embedding has been demonstrated to effectively extract the distribution of emotional features of Chinese lyrics, with at least 15 percentage points higher than traditional machine-learning methods (i.e. TF-IDF+SVM and LIWC+SVM), and 7 percentage points higher than other deep-learning models (i.e. RNN, LSTM). In this paper, more than 160,000 lyrics corpus has been leveraged for pre-training word embedding for mood tagging boost. |
| Tasks | Information Retrieval, Recommendation Systems, Sentiment Analysis |
| Published | 2019-05-23 |
| URL | https://arxiv.org/abs/1906.02135v2 |
| https://arxiv.org/pdf/1906.02135v2.pdf | |
| PWC | https://paperswithcode.com/paper/190602135 |
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