Paper Group ANR 80
Ranking medical jargon in electronic health record notes by adapted distant supervision. Weakly supervised learning of actions from transcripts. UsingWord Embeddings for Query Translation for Hindi to English Cross Language Information Retrieval. Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Mult …
Ranking medical jargon in electronic health record notes by adapted distant supervision
| Title | Ranking medical jargon in electronic health record notes by adapted distant supervision |
| Authors | Jinying Chen, Abhyuday N. Jagannatha, Samah J. Jarad, Hong Yu |
| Abstract | Objective: Allowing patients to access their own electronic health record (EHR) notes through online patient portals has the potential to improve patient-centered care. However, medical jargon, which abounds in EHR notes, has been shown to be a barrier for patient EHR comprehension. Existing knowledge bases that link medical jargon to lay terms or definitions play an important role in alleviating this problem but have low coverage of medical jargon in EHRs. We developed a data-driven approach that mines EHRs to identify and rank medical jargon based on its importance to patients, to support the building of EHR-centric lay language resources. Methods: We developed an innovative adapted distant supervision (ADS) model based on support vector machines to rank medical jargon from EHRs. For distant supervision, we utilized the open-access, collaborative consumer health vocabulary, a large, publicly available resource that links lay terms to medical jargon. We explored both knowledge-based features from the Unified Medical Language System and distributed word representations learned from unlabeled large corpora. We evaluated the ADS model using physician-identified important medical terms. Results: Our ADS model significantly surpassed two state-of-the-art automatic term recognition methods, TF*IDF and C-Value, yielding 0.810 ROC-AUC versus 0.710 and 0.667, respectively. Our model identified 10K important medical jargon terms after ranking over 100K candidate terms mined from over 7,500 EHR narratives. Conclusion: Our work is an important step towards enriching lexical resources that link medical jargon to lay terms/definitions to support patient EHR comprehension. The identified medical jargon terms and their rankings are available upon request. |
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| Published | 2016-11-14 |
| URL | http://arxiv.org/abs/1611.04491v1 |
| http://arxiv.org/pdf/1611.04491v1.pdf | |
| PWC | https://paperswithcode.com/paper/ranking-medical-jargon-in-electronic-health |
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Weakly supervised learning of actions from transcripts
| Title | Weakly supervised learning of actions from transcripts |
| Authors | Hilde Kuehne, Alexander Richard, Juergen Gall |
| Abstract | We present an approach for weakly supervised learning of human actions from video transcriptions. Our system is based on the idea that, given a sequence of input data and a transcript, i.e. a list of the order the actions occur in the video, it is possible to infer the actions within the video stream, and thus, learn the related action models without the need for any frame-based annotation. Starting from the transcript information at hand, we split the given data sequences uniformly based on the number of expected actions. We then learn action models for each class by maximizing the probability that the training video sequences are generated by the action models given the sequence order as defined by the transcripts. The learned model can be used to temporally segment an unseen video with or without transcript. We evaluate our approach on four distinct activity datasets, namely Hollywood Extended, MPII Cooking, Breakfast and CRIM13. We show that our system is able to align the scripted actions with the video data and that the learned models localize and classify actions competitively in comparison to models trained with full supervision, i.e. with frame level annotations, and that they outperform any current state-of-the-art approach for aligning transcripts with video data. |
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| Published | 2016-10-07 |
| URL | http://arxiv.org/abs/1610.02237v2 |
| http://arxiv.org/pdf/1610.02237v2.pdf | |
| PWC | https://paperswithcode.com/paper/weakly-supervised-learning-of-actions-from |
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UsingWord Embeddings for Query Translation for Hindi to English Cross Language Information Retrieval
| Title | UsingWord Embeddings for Query Translation for Hindi to English Cross Language Information Retrieval |
| Authors | Paheli Bhattacharya, Pawan Goyal, Sudeshna Sarkar |
| Abstract | Cross-Language Information Retrieval (CLIR) has become an important problem to solve in the recent years due to the growth of content in multiple languages in the Web. One of the standard methods is to use query translation from source to target language. In this paper, we propose an approach based on word embeddings, a method that captures contextual clues for a particular word in the source language and gives those words as translations that occur in a similar context in the target language. Once we obtain the word embeddings of the source and target language pairs, we learn a projection from source to target word embeddings, making use of a dictionary with word translation pairs.We then propose various methods of query translation and aggregation. The advantage of this approach is that it does not require the corpora to be aligned (which is difficult to obtain for resource-scarce languages), a dictionary with word translation pairs is enough to train the word vectors for translation. We experiment with Forum for Information Retrieval and Evaluation (FIRE) 2008 and 2012 datasets for Hindi to English CLIR. The proposed word embedding based approach outperforms the basic dictionary based approach by 70% and when the word embeddings are combined with the dictionary, the hybrid approach beats the baseline dictionary based method by 77%. It outperforms the English monolingual baseline by 15%, when combined with the translations obtained from Google Translate and Dictionary. |
| Tasks | Information Retrieval, Word Embeddings |
| Published | 2016-08-04 |
| URL | http://arxiv.org/abs/1608.01561v1 |
| http://arxiv.org/pdf/1608.01561v1.pdf | |
| PWC | https://paperswithcode.com/paper/usingword-embeddings-for-query-translation |
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Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization
| Title | Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization |
| Authors | Abhishek Gupta, Yew-Soon Ong |
| Abstract | Evolutionary multitasking has recently emerged as a novel paradigm that enables the similarities and/or latent complementarities (if present) between distinct optimization tasks to be exploited in an autonomous manner simply by solving them together with a unified solution representation scheme. An important matter underpinning future algorithmic advancements is to develop a better understanding of the driving force behind successful multitask problem-solving. In this regard, two (seemingly disparate) ideas have been put forward, namely, (a) implicit genetic transfer as the key ingredient facilitating the exchange of high-quality genetic material across tasks, and (b) population diversification resulting in effective global search of the unified search space encompassing all tasks. In this paper, we present some empirical results that provide a clearer picture of the relationship between the two aforementioned propositions. For the numerical experiments we make use of Sudoku puzzles as case studies, mainly because of their feature that outwardly unlike puzzle statements can often have nearly identical final solutions. The experiments reveal that while on many occasions genetic transfer and population diversity may be viewed as two sides of the same coin, the wider implication of genetic transfer, as shall be shown herein, captures the true essence of evolutionary multitasking to the fullest. |
| Tasks | |
| Published | 2016-07-19 |
| URL | http://arxiv.org/abs/1607.05390v1 |
| http://arxiv.org/pdf/1607.05390v1.pdf | |
| PWC | https://paperswithcode.com/paper/genetic-transfer-or-population |
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Learning to Gather Information via Imitation
| Title | Learning to Gather Information via Imitation |
| Authors | Sanjiban Choudhury, Ashish Kapoor, Gireeja Ranade, Debadeepta Dey |
| Abstract | The budgeted information gathering problem - where a robot with a fixed fuel budget is required to maximize the amount of information gathered from the world - appears in practice across a wide range of applications in autonomous exploration and inspection with mobile robots. Although there is an extensive amount of prior work investigating effective approximations of the problem, these methods do not address the fact that their performance is heavily dependent on distribution of objects in the world. In this paper, we attempt to address this issue by proposing a novel data-driven imitation learning framework. We present an efficient algorithm, EXPLORE, that trains a policy on the target distribution to imitate a clairvoyant oracle - an oracle that has full information about the world and computes non-myopic solutions to maximize information gathered. We validate the approach on a spectrum of results on a number of 2D and 3D exploration problems that demonstrates the ability of EXPLORE to adapt to different object distributions. Additionally, our analysis provides theoretical insight into the behavior of EXPLORE. Our approach paves the way forward for efficiently applying data-driven methods to the domain of information gathering. |
| Tasks | Imitation Learning |
| Published | 2016-11-13 |
| URL | http://arxiv.org/abs/1611.04180v1 |
| http://arxiv.org/pdf/1611.04180v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-gather-information-via-imitation |
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Incorporating Structural Alignment Biases into an Attentional Neural Translation Model
| Title | Incorporating Structural Alignment Biases into an Attentional Neural Translation Model |
| Authors | Trevor Cohn, Cong Duy Vu Hoang, Ekaterina Vymolova, Kaisheng Yao, Chris Dyer, Gholamreza Haffari |
| Abstract | Neural encoder-decoder models of machine translation have achieved impressive results, rivalling traditional translation models. However their modelling formulation is overly simplistic, and omits several key inductive biases built into traditional models. In this paper we extend the attentional neural translation model to include structural biases from word based alignment models, including positional bias, Markov conditioning, fertility and agreement over translation directions. We show improvements over a baseline attentional model and standard phrase-based model over several language pairs, evaluating on difficult languages in a low resource setting. |
| Tasks | Machine Translation |
| Published | 2016-01-06 |
| URL | http://arxiv.org/abs/1601.01085v1 |
| http://arxiv.org/pdf/1601.01085v1.pdf | |
| PWC | https://paperswithcode.com/paper/incorporating-structural-alignment-biases |
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Novel Views of Objects from a Single Image
| Title | Novel Views of Objects from a Single Image |
| Authors | Konstantinos Rematas, Chuong Nguyen, Tobias Ritschel, Mario Fritz, Tinne Tuytelaars |
| Abstract | Taking an image of an object is at its core a lossy process. The rich information about the three-dimensional structure of the world is flattened to an image plane and decisions such as viewpoint and camera parameters are final and not easily revertible. As a consequence, possibilities of changing viewpoint are limited. Given a single image depicting an object, novel-view synthesis is the task of generating new images that render the object from a different viewpoint than the one given. The main difficulty is to synthesize the parts that are disoccluded; disocclusion occurs when parts of an object are hidden by the object itself under a specific viewpoint. In this work, we show how to improve novel-view synthesis by making use of the correlations observed in 3D models and applying them to new image instances. We propose a technique to use the structural information extracted from a 3D model that matches the image object in terms of viewpoint and shape. For the latter part, we propose an efficient 2D-to-3D alignment method that associates precisely the image appearance with the 3D model geometry with minimal user interaction. Our technique is able to simulate plausible viewpoint changes for a variety of object classes within seconds. Additionally, we show that our synthesized images can be used as additional training data that improves the performance of standard object detectors. |
| Tasks | Novel View Synthesis |
| Published | 2016-01-31 |
| URL | http://arxiv.org/abs/1602.00328v2 |
| http://arxiv.org/pdf/1602.00328v2.pdf | |
| PWC | https://paperswithcode.com/paper/novel-views-of-objects-from-a-single-image |
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Discriminative models for robust image classification
| Title | Discriminative models for robust image classification |
| Authors | Umamahesh Srinivas |
| Abstract | A variety of real-world tasks involve the classification of images into pre-determined categories. Designing image classification algorithms that exhibit robustness to acquisition noise and image distortions, particularly when the available training data are insufficient to learn accurate models, is a significant challenge. This dissertation explores the development of discriminative models for robust image classification that exploit underlying signal structure, via probabilistic graphical models and sparse signal representations. Probabilistic graphical models are widely used in many applications to approximate high-dimensional data in a reduced complexity set-up. Learning graphical structures to approximate probability distributions is an area of active research. Recent work has focused on learning graphs in a discriminative manner with the goal of minimizing classification error. In the first part of the dissertation, we develop a discriminative learning framework that exploits the complementary yet correlated information offered by multiple representations (or projections) of a given signal/image. Specifically, we propose a discriminative tree-based scheme for feature fusion by explicitly learning the conditional correlations among such multiple projections in an iterative manner. Experiments reveal the robustness of the resulting graphical model classifier to training insufficiency. |
| Tasks | Image Classification |
| Published | 2016-03-08 |
| URL | http://arxiv.org/abs/1603.02736v1 |
| http://arxiv.org/pdf/1603.02736v1.pdf | |
| PWC | https://paperswithcode.com/paper/discriminative-models-for-robust-image |
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Exploring phrase-compositionality in skip-gram models
| Title | Exploring phrase-compositionality in skip-gram models |
| Authors | Xiaochang Peng, Daniel Gildea |
| Abstract | In this paper, we introduce a variation of the skip-gram model which jointly learns distributed word vector representations and their way of composing to form phrase embeddings. In particular, we propose a learning procedure that incorporates a phrase-compositionality function which can capture how we want to compose phrases vectors from their component word vectors. Our experiments show improvement in word and phrase similarity tasks as well as syntactic tasks like dependency parsing using the proposed joint models. |
| Tasks | Dependency Parsing |
| Published | 2016-07-21 |
| URL | http://arxiv.org/abs/1607.06208v1 |
| http://arxiv.org/pdf/1607.06208v1.pdf | |
| PWC | https://paperswithcode.com/paper/exploring-phrase-compositionality-in-skip |
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Link Prediction via Matrix Completion
| Title | Link Prediction via Matrix Completion |
| Authors | Ratha Pech, Dong Hao, Liming Pan, Hong Cheng, Tao Zhou |
| Abstract | Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in the recent years. Link prediction is a fundamental issue to understand the mechanisms by which new links are added to the networks. We introduce the method of robust principal component analysis (robust PCA) into link prediction, and estimate the missing entries of the adjacency matrix. On one hand, our algorithm is based on the sparsity and low rank property of the matrix, on the other hand, it also performs very well when the network is dense. This is because a relatively dense real network is also sparse in comparison to the complete graph. According to extensive experiments on real networks from disparate fields, when the target network is connected and sufficiently dense, whatever it is weighted or unweighted, our method is demonstrated to be very effective and with prediction accuracy being considerably improved comparing with many state-of-the-art algorithms. |
| Tasks | Link Prediction, Matrix Completion |
| Published | 2016-06-22 |
| URL | http://arxiv.org/abs/1606.06812v2 |
| http://arxiv.org/pdf/1606.06812v2.pdf | |
| PWC | https://paperswithcode.com/paper/link-prediction-via-matrix-completion |
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Estimating Causal Direction and Confounding of Two Discrete Variables
| Title | Estimating Causal Direction and Confounding of Two Discrete Variables |
| Authors | Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona |
| Abstract | We propose a method to classify the causal relationship between two discrete variables given only the joint distribution of the variables, acknowledging that the method is subject to an inherent baseline error. We assume that the causal system is acyclicity, but we do allow for hidden common causes. Our algorithm presupposes that the probability distributions $P(C)$ of a cause $C$ is independent from the probability distribution $P(E\mid C)$ of the cause-effect mechanism. While our classifier is trained with a Bayesian assumption of flat hyperpriors, we do not make this assumption about our test data. This work connects to recent developments on the identifiability of causal models over continuous variables under the assumption of “independent mechanisms”. Carefully-commented Python notebooks that reproduce all our experiments are available online at http://vision.caltech.edu/~kchalupk/code.html. |
| Tasks | |
| Published | 2016-11-04 |
| URL | http://arxiv.org/abs/1611.01504v1 |
| http://arxiv.org/pdf/1611.01504v1.pdf | |
| PWC | https://paperswithcode.com/paper/estimating-causal-direction-and-confounding |
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Less is More: Micro-expression Recognition from Video using Apex Frame
| Title | Less is More: Micro-expression Recognition from Video using Apex Frame |
| Authors | Sze-Teng Liong, John See, KokSheik Wong, Raphael C. -W. Phan |
| Abstract | Despite recent interest and advances in facial micro-expression research, there is still plenty room for improvement in terms of micro-expression recognition. Conventional feature extraction approaches for micro-expression video consider either the whole video sequence or a part of it, for representation. However, with the high-speed video capture of micro-expressions (100-200 fps), are all frames necessary to provide a sufficiently meaningful representation? Is the luxury of data a bane to accurate recognition? A novel proposition is presented in this paper, whereby we utilize only two images per video: the apex frame and the onset frame. The apex frame of a video contains the highest intensity of expression changes among all frames, while the onset is the perfect choice of a reference frame with neutral expression. A new feature extractor, Bi-Weighted Oriented Optical Flow (Bi-WOOF) is proposed to encode essential expressiveness of the apex frame. We evaluated the proposed method on five micro-expression databases: CAS(ME)$^2$, CASME II, SMIC-HS, SMIC-NIR and SMIC-VIS. Our experiments lend credence to our hypothesis, with our proposed technique achieving a state-of-the-art F1-score recognition performance of 61% and 62% in the high frame rate CASME II and SMIC-HS databases respectively. |
| Tasks | Optical Flow Estimation |
| Published | 2016-06-06 |
| URL | http://arxiv.org/abs/1606.01721v3 |
| http://arxiv.org/pdf/1606.01721v3.pdf | |
| PWC | https://paperswithcode.com/paper/less-is-more-micro-expression-recognition |
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Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
| Title | Not Just a Black Box: Learning Important Features Through Propagating Activation Differences |
| Authors | Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, Anshul Kundaje |
| Abstract | Note: This paper describes an older version of DeepLIFT. See https://arxiv.org/abs/1704.02685 for the newer version. Original abstract follows: The purported “black box” nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Learning Important FeaTures), an efficient and effective method for computing importance scores in a neural network. DeepLIFT compares the activation of each neuron to its ‘reference activation’ and assigns contribution scores according to the difference. We apply DeepLIFT to models trained on natural images and genomic data, and show significant advantages over gradient-based methods. |
| Tasks | |
| Published | 2016-05-05 |
| URL | http://arxiv.org/abs/1605.01713v3 |
| http://arxiv.org/pdf/1605.01713v3.pdf | |
| PWC | https://paperswithcode.com/paper/not-just-a-black-box-learning-important |
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Equation Parsing: Mapping Sentences to Grounded Equations
| Title | Equation Parsing: Mapping Sentences to Grounded Equations |
| Authors | Subhro Roy, Shyam Upadhyay, Dan Roth |
| Abstract | Identifying mathematical relations expressed in text is essential to understanding a broad range of natural language text from election reports, to financial news, to sport commentaries to mathematical word problems. This paper focuses on identifying and understanding mathematical relations described within a single sentence. We introduce the problem of Equation Parsing – given a sentence, identify noun phrases which represent variables, and generate the mathematical equation expressing the relation described in the sentence. We introduce the notion of projective equation parsing and provide an efficient algorithm to parse text to projective equations. Our system makes use of a high precision lexicon of mathematical expressions and a pipeline of structured predictors, and generates correct equations in $70%$ of the cases. In $60%$ of the time, it also identifies the correct noun phrase $\rightarrow$ variables mapping, significantly outperforming baselines. We also release a new annotated dataset for task evaluation. |
| Tasks | |
| Published | 2016-09-28 |
| URL | http://arxiv.org/abs/1609.08824v1 |
| http://arxiv.org/pdf/1609.08824v1.pdf | |
| PWC | https://paperswithcode.com/paper/equation-parsing-mapping-sentences-to |
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Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
| Title | Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example |
| Authors | Alexandre Abraham, Michael Milham, Adriana Di Martino, R. Cameron Craddock, Dimitris Samaras, Bertrand Thirion, Gaël Varoquaux |
| Abstract | Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such biomarkers is challenging for complex multi-faceted neuropatholo-gies, such as autism spectrum disorders. Large multi-site datasets increase sample sizes to compensate for this complexity, at the cost of uncontrolled heterogeneity. This heterogeneity raises new challenges, akin to those face in realistic diagnostic applications. Here, we demonstrate the feasibility of inter-site classification of neuropsychiatric status, with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large (N=871) multi-site autism dataset. For this purpose, we investigate pipelines that extract the most predictive biomarkers from the data. These R-fMRI pipelines build participant-specific connectomes from functionally-defined brain areas. Connectomes are then compared across participants to learn patterns of connectivity that differentiate typical controls from individuals with autism. We predict this neuropsychiatric status for participants from the same acquisition sites or different, unseen, ones. Good choices of methods for the various steps of the pipeline lead to 67% prediction accuracy on the full ABIDE data, which is significantly better than previously reported results. We perform extensive validation on multiple subsets of the data defined by different inclusion criteria. These enables detailed analysis of the factors contributing to successful connectome-based prediction. First, prediction accuracy improves as we include more subjects, up to the maximum amount of subjects available. Second, the definition of functional brain areas is of paramount importance for biomarker discovery: brain areas extracted from large R-fMRI datasets outperform reference atlases in the classification tasks. |
| Tasks | |
| Published | 2016-11-18 |
| URL | http://arxiv.org/abs/1611.06066v1 |
| http://arxiv.org/pdf/1611.06066v1.pdf | |
| PWC | https://paperswithcode.com/paper/deriving-reproducible-biomarkers-from-multi |
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