Paper Group ANR 877
An Ensemble Model for Sentiment Analysis of Hindi-English Code-Mixed Data. The Deconfounded Recommender: A Causal Inference Approach to Recommendation. Motion Invariance in Visual Environments. Improved TDNNs using Deep Kernels and Frequency Dependent Grid-RNNs. Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility. A new system …
An Ensemble Model for Sentiment Analysis of Hindi-English Code-Mixed Data
| Title | An Ensemble Model for Sentiment Analysis of Hindi-English Code-Mixed Data |
| Authors | Madan Gopal Jhanwar, Arpita Das |
| Abstract | In multilingual societies like India, code-mixed social media texts comprise the majority of the Internet. Detecting the sentiment of the code-mixed user opinions plays a crucial role in understanding social, economic and political trends. In this paper, we propose an ensemble of character-trigrams based LSTM model and word-ngrams based Multinomial Naive Bayes (MNB) model to identify the sentiments of Hindi-English (Hi-En) code-mixed data. The ensemble model combines the strengths of rich sequential patterns from the LSTM model and polarity of keywords from the probabilistic ngram model to identify sentiments in sparse and inconsistent code-mixed data. Experiments on reallife user code-mixed data reveals that our approach yields state-of-the-art results as compared to several baselines and other deep learning based proposed methods. |
| Tasks | Sentiment Analysis |
| Published | 2018-06-12 |
| URL | http://arxiv.org/abs/1806.04450v1 |
| http://arxiv.org/pdf/1806.04450v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-ensemble-model-for-sentiment-analysis-of |
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The Deconfounded Recommender: A Causal Inference Approach to Recommendation
| Title | The Deconfounded Recommender: A Causal Inference Approach to Recommendation |
| Authors | Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei |
| Abstract | The goal of recommendation is to show users items that they will like. Though usually framed as a prediction, the spirit of recommendation is to answer an interventional question—for each user and movie, what would the rating be if we “forced” the user to watch the movie? To this end, we develop a causal approach to recommendation, one where watching a movie is a “treatment” and a user’s rating is an “outcome.” The problem is there may be unobserved confounders, variables that affect both which movies the users watch and how they rate them; unobserved confounders impede causal predictions with observational data. To solve this problem, we develop the deconfounded recommender, a way to use classical recommendation models for causal recommendation. Following Wang & Blei [23], the deconfounded recommender involves two probabilistic models. The first models which movies the users watch; it provides a substitute for the unobserved confounders. The second one models how each user rates each movie; it employs the substitute to help account for confounders. This two-stage approach removes bias due to confounding. It improves recommendation and enjoys stable performance against interventions on test sets. |
| Tasks | Causal Inference, Recommendation Systems |
| Published | 2018-08-20 |
| URL | https://arxiv.org/abs/1808.06581v2 |
| https://arxiv.org/pdf/1808.06581v2.pdf | |
| PWC | https://paperswithcode.com/paper/the-deconfounded-recommender-a-causal |
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Motion Invariance in Visual Environments
| Title | Motion Invariance in Visual Environments |
| Authors | Alessandro Betti, Marco Gori, Stefano Melacci |
| Abstract | The puzzle of computer vision might find new challenging solutions when we realize that most successful methods are working at image level, which is remarkably more difficult than processing directly visual streams, just as happens in nature. In this paper, we claim that their processing naturally leads to formulate the motion invariance principle, which enables the construction of a new theory of visual learning based on convolutional features. The theory addresses a number of intriguing questions that arise in natural vision, and offers a well-posed computational scheme for the discovery of convolutional filters over the retina. They are driven by the Euler-Lagrange differential equations derived from the principle of least cognitive action, that parallels laws of mechanics. Unlike traditional convolutional networks, which need massive supervision, the proposed theory offers a truly new scenario in which feature learning takes place by unsupervised processing of video signals. An experimental report of the theory is presented where we show that features extracted under motion invariance yield an improvement that can be assessed by measuring information-based indexes. |
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| Published | 2018-07-14 |
| URL | http://arxiv.org/abs/1807.06450v1 |
| http://arxiv.org/pdf/1807.06450v1.pdf | |
| PWC | https://paperswithcode.com/paper/motion-invariance-in-visual-environments |
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Improved TDNNs using Deep Kernels and Frequency Dependent Grid-RNNs
| Title | Improved TDNNs using Deep Kernels and Frequency Dependent Grid-RNNs |
| Authors | Florian Kreyssig, Chao Zhang, Philip Woodland |
| Abstract | Time delay neural networks (TDNNs) are an effective acoustic model for large vocabulary speech recognition. The strength of the model can be attributed to its ability to effectively model long temporal contexts. However, current TDNN models are relatively shallow, which limits the modelling capability. This paper proposes a method of increasing the network depth by deepening the kernel used in the TDNN temporal convolutions. The best performing kernel consists of three fully connected layers with a residual (ResNet) connection from the output of the first to the output of the third. The addition of spectro-temporal processing as the input to the TDNN in the form of a convolutional neural network (CNN) and a newly designed Grid-RNN was investigated. The Grid-RNN strongly outperforms a CNN if different sets of parameters for different frequency bands are used and can be further enhanced by using a bi-directional Grid-RNN. Experiments using the multi-genre broadcast (MGB3) English data (275h) show that deep kernel TDNNs reduces the word error rate (WER) by 6% relative and when combined with the frequency dependent Grid-RNN gives a relative WER reduction of 9%. |
| Tasks | Large Vocabulary Continuous Speech Recognition, Speech Recognition |
| Published | 2018-02-18 |
| URL | http://arxiv.org/abs/1802.06412v2 |
| http://arxiv.org/pdf/1802.06412v2.pdf | |
| PWC | https://paperswithcode.com/paper/improved-tdnns-using-deep-kernels-and |
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Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility
| Title | Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility |
| Authors | Benjamin Hepp, Debadeepta Dey, Sudipta N. Sinha, Ashish Kapoor, Neel Joshi, Otmar Hilliges |
| Abstract | Camera equipped drones are nowadays being used to explore large scenes and reconstruct detailed 3D maps. When free space in the scene is approximately known, an offline planner can generate optimal plans to efficiently explore the scene. However, for exploring unknown scenes, the planner must predict and maximize usefulness of where to go on the fly. Traditionally, this has been achieved using handcrafted utility functions. We propose to learn a better utility function that predicts the usefulness of future viewpoints. Our learned utility function is based on a 3D convolutional neural network. This network takes as input a novel volumetric scene representation that implicitly captures previously visited viewpoints and generalizes to new scenes. We evaluate our method on several large 3D models of urban scenes using simulated depth cameras. We show that our method outperforms existing utility measures in terms of reconstruction performance and is robust to sensor noise. |
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| Published | 2018-06-27 |
| URL | http://arxiv.org/abs/1806.10354v2 |
| http://arxiv.org/pdf/1806.10354v2.pdf | |
| PWC | https://paperswithcode.com/paper/learn-to-score-efficient-3d-scene-exploration |
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A new system-wide diversity measure for recommendations with efficient algorithms
| Title | A new system-wide diversity measure for recommendations with efficient algorithms |
| Authors | Arda Antikacioglu, Tanvi Bajpai, R. Ravi |
| Abstract | Recommender systems often operate on item catalogs clustered by genres, and user bases that have natural clusterings into user types by demographic or psychographic attributes. Prior work on system-wide diversity has mainly focused on defining intent-aware metrics among such categories and maximizing relevance of the resulting recommendations, but has not combined the notions of diversity from the two point of views of items and users. In this work, (1) we introduce two new system-wide diversity metrics to simultaneously address the problems of diversifying the categories of items that each user sees, diversifying the types of users that each item is shown, and maintaining high recommendation quality. We model this as a subgraph selection problem on the bipartite graph of candidate recommendations between users and items. (2) In the case of disjoint item categories and user types, we show that the resulting problems can be solved exactly in polynomial time, by a reduction to a minimum cost flow problem. (3) In the case of non-disjoint categories and user types, we prove NP-completeness of the objective and present efficient approximation algorithms using the submodularity of the objective. (4) Finally, we validate the effectiveness of our algorithms on the MovieLens-1m and Netflix datasets, and show that algorithms designed for our objective also perform well on sales diversity metrics, and even some intent-aware diversity metrics. Our experimental results justify the validity of our new composite diversity metrics. |
| Tasks | Recommendation Systems |
| Published | 2018-11-30 |
| URL | https://arxiv.org/abs/1812.03030v2 |
| https://arxiv.org/pdf/1812.03030v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-new-system-wide-diversity-measure-for |
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Dynamic Transfer Learning for Named Entity Recognition
| Title | Dynamic Transfer Learning for Named Entity Recognition |
| Authors | Parminder Bhatia, Kristjan Arumae, Busra Celikkaya |
| Abstract | State-of-the-art named entity recognition (NER) systems have been improving continuously using neural architectures over the past several years. However, many tasks including NER require large sets of annotated data to achieve such performance. In particular, we focus on NER from clinical notes, which is one of the most fundamental and critical problems for medical text analysis. Our work centers on effectively adapting these neural architectures towards low-resource settings using parameter transfer methods. We complement a standard hierarchical NER model with a general transfer learning framework consisting of parameter sharing between the source and target tasks, and showcase scores significantly above the baseline architecture. These sharing schemes require an exponential search over tied parameter sets to generate an optimal configuration. To mitigate the problem of exhaustively searching for model optimization, we propose the Dynamic Transfer Networks (DTN), a gated architecture which learns the appropriate parameter sharing scheme between source and target datasets. DTN achieves the improvements of the optimized transfer learning framework with just a single training setting, effectively removing the need for exponential search. |
| Tasks | Named Entity Recognition, Transfer Learning |
| Published | 2018-12-13 |
| URL | https://arxiv.org/abs/1812.05288v4 |
| https://arxiv.org/pdf/1812.05288v4.pdf | |
| PWC | https://paperswithcode.com/paper/dynamic-transfer-learning-for-named-entity |
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Advanced machine learning informatics modeling using clinical and radiological imaging metrics for characterizing breast tumor characteristics with the OncotypeDX gene array
| Title | Advanced machine learning informatics modeling using clinical and radiological imaging metrics for characterizing breast tumor characteristics with the OncotypeDX gene array |
| Authors | Michael A. Jacobs, Christopher Umbricht, Vishwa Parekh, Riham El Khouli, Leslie Cope, Katarzyna J. Macura, Susan Harvey, Antonio C. Wolff |
| Abstract | Purpose-Optimal use of established and imaging methods, such as multiparametric magnetic resonance imaging(mpMRI) can simultaneously identify key functional parameters and provide unique imaging phenotypes of breast cancer. Therefore, we have developed and implemented a new machine-learning informatic system that integrates clinical variables, derived from imaging and clinical health records, to compare with the 21-gene array assay, OncotypeDX. Materials and methods-We tested our informatics modeling in a subset of patients (n=81) who had ER+ disease and underwent OncotypeDX gene expression and breast mpMRI testing. The machine-learning informatic method is termed Integrated Radiomic Informatic System-IRIS was applied to the mpMRI, clinical and pathologic descriptors, as well as a gene array analysis. The IRIS method using an advanced graph theoretic model and quantitative metrics. Summary statistics (mean and standard deviations) for the quantitative imaging parameters were obtained. Sensitivity and specificity and Area Under the Curve were calculated for the classification of the patients. Results-The OncotypeDX classification by IRIS model had sensitivity of 95% and specificity of 89% with AUC of 0.92. The breast lesion size was larger for the high-risk groups and lower for both low risk and intermediate risk groups. There were significant differences in PK-DCE and ADC map values in each group. The ADC map values for high- and intermediate-risk groups were significantly lower than the low-risk group. Conclusion-These initial studies provide deeper understandings of imaging features and molecular gene array OncotypeDX score. This insight provides the foundation to relate these imaging features to the assessment of treatment response for improved personalized medicine. |
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| Published | 2018-11-08 |
| URL | http://arxiv.org/abs/1811.03218v1 |
| http://arxiv.org/pdf/1811.03218v1.pdf | |
| PWC | https://paperswithcode.com/paper/advanced-machine-learning-informatics |
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Balanced multi-shot EPI for accelerated Cartesian MRF: An alternative to spiral MRF
| Title | Balanced multi-shot EPI for accelerated Cartesian MRF: An alternative to spiral MRF |
| Authors | Arnold Julian Vinoj Benjamin, Pedro A. Gómez, Mohammad Golbabaee, Tim Sprenger, Marion I. Menzel, Mike E. Davies, Ian Marshall |
| Abstract | The main purpose of this study is to show that a highly accelerated Cartesian MRF scheme using a multi-shot EPI readout (i.e. multi-shot EPI-MRF) can produce good quality multi-parametric maps such as T1, T2 and proton density (PD) in a sufficiently short scan duration that is similar to conventional MRF. This multi-shot approach allows considerable subsampling while traversing the entire k-space trajectory, can yield better SNR, reduced blurring, less distortion and can also be used to collect higher resolution data compared to existing single-shot EPI-MRF implementations. The generated parametric maps are compared to an accelerated spiral MRF implementation with the same acquisition parameters to evaluate the performance of this method. Additionally, an iterative reconstruction algorithm is applied to improve the accuracy of parametric map estimations and the fast convergence of EPI-MRF is also demonstrated. |
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| Published | 2018-09-06 |
| URL | http://arxiv.org/abs/1809.02506v1 |
| http://arxiv.org/pdf/1809.02506v1.pdf | |
| PWC | https://paperswithcode.com/paper/balanced-multi-shot-epi-for-accelerated |
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Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning
| Title | Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning |
| Authors | Jihyung Moon, Hyochang Yang, Sungzoon Cho |
| Abstract | To solve the text-based question and answering task that requires relational reasoning, it is necessary to memorize a large amount of information and find out the question relevant information from the memory. Most approaches were based on external memory and four components proposed by Memory Network. The distinctive component among them was the way of finding the necessary information and it contributes to the performance. Recently, a simple but powerful neural network module for reasoning called Relation Network (RN) has been introduced. We analyzed RN from the view of Memory Network, and realized that its MLP component is able to reveal the complicate relation between question and object pair. Motivated from it, we introduce which uses MLP to find out relevant information on Memory Network architecture. It shows new state-of-the-art results in jointly trained bAbI-10k story-based question answering tasks and bAbI dialog-based question answering tasks. |
| Tasks | Question Answering, Relational Reasoning |
| Published | 2018-01-25 |
| URL | http://arxiv.org/abs/1801.08459v2 |
| http://arxiv.org/pdf/1801.08459v2.pdf | |
| PWC | https://paperswithcode.com/paper/finding-remo-related-memory-object-a-simple |
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Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces
| Title | Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces |
| Authors | Jing Lei |
| Abstract | We provide upper bounds of the expected Wasserstein distance between a probability measure and its empirical version, generalizing recent results for finite dimensional Euclidean spaces and bounded functional spaces. Such a generalization can cover Euclidean spaces with large dimensionality, with the optimal dependence on the dimensionality. Our method also covers the important case of Gaussian processes in separable Hilbert spaces, with rate-optimal upper bounds for functional data distributions whose coordinates decay geometrically or polynomially. Moreover, our bounds of the expected value can be combined with mean-concentration results to yield improved exponential tail probability bounds for the Wasserstein error of empirical measures under Bernstein-type or log Sobolev-type conditions. |
| Tasks | Gaussian Processes |
| Published | 2018-04-27 |
| URL | https://arxiv.org/abs/1804.10556v2 |
| https://arxiv.org/pdf/1804.10556v2.pdf | |
| PWC | https://paperswithcode.com/paper/convergence-and-concentration-of-empirical |
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Value-based Search in Execution Space for Mapping Instructions to Programs
| Title | Value-based Search in Execution Space for Mapping Instructions to Programs |
| Authors | Dor Muhlgay, Jonathan Herzig, Jonathan Berant |
| Abstract | Training models to map natural language instructions to programs given target world supervision only requires searching for good programs at training time. Search is commonly done using beam search in the space of partial programs or program trees, but as the length of the instructions grows finding a good program becomes difficult. In this work, we propose a search algorithm that uses the target world state, known at training time, to train a critic network that predicts the expected reward of every search state. We then score search states on the beam by interpolating their expected reward with the likelihood of programs represented by the search state. Moreover, we search not in the space of programs but in a more compressed state of program executions, augmented with recent entities and actions. On the SCONE dataset, we show that our algorithm dramatically improves performance on all three domains compared to standard beam search and other baselines. |
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| Published | 2018-11-02 |
| URL | http://arxiv.org/abs/1811.01090v2 |
| http://arxiv.org/pdf/1811.01090v2.pdf | |
| PWC | https://paperswithcode.com/paper/value-based-search-in-execution-space-for |
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A Framework towards Domain Specific Video Summarization
| Title | A Framework towards Domain Specific Video Summarization |
| Authors | Vishal Kaushal, Sandeep Subramanian, Suraj Kothawade, Rishabh Iyer, Ganesh Ramakrishnan |
| Abstract | In the light of exponentially increasing video content, video summarization has attracted a lot of attention recently due to its ability to optimize time and storage. Characteristics of a good summary of a video depend on the particular domain under question. We propose a novel framework for domain specific video summarization. Given a video of a particular domain, our system can produce a summary based on what is important for that domain in addition to possessing other desired characteristics like representativeness, coverage, diversity etc. as suitable to that domain. Past related work has focused either on using supervised approaches for ranking the snippets to produce summary or on using unsupervised approaches of generating the summary as a subset of snippets with the above characteristics. We look at the joint problem of learning domain specific importance of segments as well as the desired summary characteristic for that domain. Our studies show that the more efficient way of incorporating domain specific relevances into a summary is by obtaining ratings of shots as opposed to binary inclusion/exclusion information. We also argue that ratings can be seen as unified representation of all possible ground truth summaries of a video, taking us one step closer in dealing with challenges associated with multiple ground truth summaries of a video. We also propose a novel evaluation measure which is more naturally suited in assessing the quality of video summary for the task at hand than F1 like measures. It leverages the ratings information and is richer in appropriately modeling desirable and undesirable characteristics of a summary. Lastly, we release a gold standard dataset for furthering research in domain specific video summarization, which to our knowledge is the first dataset with long videos across several domains with rating annotations. |
| Tasks | Video Summarization |
| Published | 2018-09-24 |
| URL | http://arxiv.org/abs/1809.08854v2 |
| http://arxiv.org/pdf/1809.08854v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-framework-towards-domain-specific-video |
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Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading
| Title | Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading |
| Authors | Martin Raison, Pierre-Emmanuel Mazaré, Rajarshi Das, Antoine Bordes |
| Abstract | This paper aims at improving how machines can answer questions directly from text, with the focus of having models that can answer correctly multiple types of questions and from various types of texts, documents or even from large collections of them. To that end, we introduce the Weaver model that uses a new way to relate a question to a textual context by weaving layers of recurrent networks, with the goal of making as few assumptions as possible as to how the information from both question and context should be combined to form the answer. We show empirically on six datasets that Weaver performs well in multiple conditions. For instance, it produces solid results on the very popular SQuAD dataset (Rajpurkar et al., 2016), solves almost all bAbI tasks (Weston et al., 2015) and greatly outperforms state-of-the-art methods for open domain question answering from text (Chen et al., 2017). |
| Tasks | Open-Domain Question Answering, Question Answering, Reading Comprehension |
| Published | 2018-04-27 |
| URL | http://arxiv.org/abs/1804.10490v1 |
| http://arxiv.org/pdf/1804.10490v1.pdf | |
| PWC | https://paperswithcode.com/paper/weaver-deep-co-encoding-of-questions-and |
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Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion
| Title | Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion |
| Authors | N. Anantrasirichai, Alin Achim, David Bull |
| Abstract | This paper describes a new method for mitigating the effects of atmospheric distortion on observed sequences that include large moving objects. In order to provide accurate detail from objects behind the distorting layer, we solve the space-variant distortion problem using recursive image fusion based on the Dual Tree Complex Wavelet Transform (DT-CWT). The moving objects are detected and tracked using the improved Gaussian mixture models (GMM) and Kalman filtering. New fusion rules are introduced which work on the magnitudes and angles of the DT-CWT coefficients independently to achieve a sharp image and to reduce atmospheric distortion, respectively. The subjective results show that the proposed method achieves better video quality than other existing methods with competitive speed. |
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| Published | 2018-08-10 |
| URL | http://arxiv.org/abs/1808.03550v1 |
| http://arxiv.org/pdf/1808.03550v1.pdf | |
| PWC | https://paperswithcode.com/paper/atmospheric-turbulence-mitigation-for |
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