Paper Group ANR 753
Towards Black-box Iterative Machine Teaching. Is Epicurus the father of Reinforcement Learning?. A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations. The Complexity of Graph-Based Reductions for Reachability in Markov Decision Processes. Method to Detect Eye Position Noise from Video-Oculography when Detection of …
Towards Black-box Iterative Machine Teaching
| Title | Towards Black-box Iterative Machine Teaching |
| Authors | Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James M. Rehg, Le Song |
| Abstract | In this paper, we make an important step towards the black-box machine teaching by considering the cross-space machine teaching, where the teacher and the learner use different feature representations and the teacher can not fully observe the learner’s model. In such scenario, we study how the teacher is still able to teach the learner to achieve faster convergence rate than the traditional passive learning. We propose an active teacher model that can actively query the learner (i.e., make the learner take exams) for estimating the learner’s status and provably guide the learner to achieve faster convergence. The sample complexities for both teaching and query are provided. In the experiments, we compare the proposed active teacher with the omniscient teacher and verify the effectiveness of the active teacher model. |
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| Published | 2017-10-21 |
| URL | http://arxiv.org/abs/1710.07742v3 |
| http://arxiv.org/pdf/1710.07742v3.pdf | |
| PWC | https://paperswithcode.com/paper/towards-black-box-iterative-machine-teaching |
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Is Epicurus the father of Reinforcement Learning?
| Title | Is Epicurus the father of Reinforcement Learning? |
| Authors | Eleni Vasilaki |
| Abstract | The Epicurean Philosophy is commonly thought as simplistic and hedonistic. Here I discuss how this is a misconception and explore its link to Reinforcement Learning. Based on the letters of Epicurus, I construct an objective function for hedonism which turns out to be equivalent of the Reinforcement Learning objective function when omitting the discount factor. I then discuss how Plato and Aristotle ‘s views that can be also loosely linked to Reinforcement Learning, as well as their weaknesses in relationship to it. Finally, I emphasise the close affinity of the Epicurean views and the Bellman equation. |
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| Published | 2017-10-12 |
| URL | http://arxiv.org/abs/1710.04582v1 |
| http://arxiv.org/pdf/1710.04582v1.pdf | |
| PWC | https://paperswithcode.com/paper/is-epicurus-the-father-of-reinforcement |
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A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations
| Title | A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations |
| Authors | Umang Gupta, Ankush Chatterjee, Radhakrishnan Srikanth, Puneet Agrawal |
| Abstract | Emotions are physiological states generated in humans in reaction to internal or external events. They are complex and studied across numerous fields including computer science. As humans, on reading “Why don’t you ever text me!” we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. Lack of facial expressions and voice modulations make detecting emotions from text a challenging problem. However, as humans increasingly communicate using text messaging applications, and digital agents gain popularity in our society, it is essential that these digital agents are emotion aware, and respond accordingly. In this paper, we propose a novel approach to detect emotions like happy, sad or angry in textual conversations using an LSTM based Deep Learning model. Our approach consists of semi-automated techniques to gather training data for our model. We exploit advantages of semantic and sentiment based embeddings and propose a solution combining both. Our work is evaluated on real-world conversations and significantly outperforms traditional Machine Learning baselines as well as other off-the-shelf Deep Learning models. |
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| Published | 2017-07-21 |
| URL | http://arxiv.org/abs/1707.06996v4 |
| http://arxiv.org/pdf/1707.06996v4.pdf | |
| PWC | https://paperswithcode.com/paper/a-sentiment-and-semantics-based-approach-for |
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The Complexity of Graph-Based Reductions for Reachability in Markov Decision Processes
| Title | The Complexity of Graph-Based Reductions for Reachability in Markov Decision Processes |
| Authors | Stephane Le Roux, Guillermo A. Perez |
| Abstract | We study the never-worse relation (NWR) for Markov decision processes with an infinite-horizon reachability objective. A state q is never worse than a state p if the maximal probability of reaching the target set of states from p is at most the same value from q, regard- less of the probabilities labelling the transitions. Extremal-probability states, end components, and essential states are all special cases of the equivalence relation induced by the NWR. Using the NWR, states in the same equivalence class can be collapsed. Then, actions leading to sub- optimal states can be removed. We show the natural decision problem associated to computing the NWR is coNP-complete. Finally, we ex- tend a previously known incomplete polynomial-time iterative algorithm to under-approximate the NWR. |
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| Published | 2017-10-22 |
| URL | http://arxiv.org/abs/1710.07903v4 |
| http://arxiv.org/pdf/1710.07903v4.pdf | |
| PWC | https://paperswithcode.com/paper/the-complexity-of-graph-based-reductions-for |
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Method to Detect Eye Position Noise from Video-Oculography when Detection of Pupil or Corneal Reflection Position Fails
| Title | Method to Detect Eye Position Noise from Video-Oculography when Detection of Pupil or Corneal Reflection Position Fails |
| Authors | Evgeny Abdulin, Lee Friedman, Oleg V. Komogortsev |
| Abstract | We present software to detect noise in eye position signals from video-based eye-tracking systems that depend on accurate pupil and corneal reflection position estimation. When such systems transiently fail to properly detect the pupil or the corneal reflection due to occlusion from eyelids, eye lashes or various shadows, the estimated gaze position is false. This produces an artifactual signal in the position trace that is rapidly, irregularly oscillating between true and false gaze positions. We refer to this noise as RIONEPS (Rapid Irregularly Oscillating Noise of the Eye Position Signal). Our method for detecting these periods automatically is based on an estimate of the relative inefficiency of the eye position signal. We look for RIONEPS in the horizontal and vertical traces separately, and although we typically use it offline, it is suitable to adaptation for real time use. This method requires a threshold to be set, and although we provide some guidance, thresholds will have to be estimated empirically. |
| Tasks | Eye Tracking |
| Published | 2017-09-08 |
| URL | http://arxiv.org/abs/1709.02700v1 |
| http://arxiv.org/pdf/1709.02700v1.pdf | |
| PWC | https://paperswithcode.com/paper/method-to-detect-eye-position-noise-from |
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Bag-of-Words Method Applied to Accelerometer Measurements for the Purpose of Classification and Energy Estimation
| Title | Bag-of-Words Method Applied to Accelerometer Measurements for the Purpose of Classification and Energy Estimation |
| Authors | Kevin M. Amaral, Ping Chen, Scott Crouter, Wei Ding |
| Abstract | Accelerometer measurements are the prime type of sensor information most think of when seeking to measure physical activity. On the market, there are many fitness measuring devices which aim to track calories burned and steps counted through the use of accelerometers. These measurements, though good enough for the average consumer, are noisy and unreliable in terms of the precision of measurement needed in a scientific setting. The contribution of this paper is an innovative and highly accurate regression method which uses an intermediary two-stage classification step to better direct the regression of energy expenditure values from accelerometer counts. We show that through an additional unsupervised layer of intermediate feature construction, we can leverage latent patterns within accelerometer counts to provide better grounds for activity classification than expert-constructed timeseries features. For this, our approach utilizes a mathematical model originating in natural language processing, the bag-of-words model, that has in the past years been appearing in diverse disciplines outside of the natural language processing field such as image processing. Further emphasizing the natural language connection to stochastics, we use a gaussian mixture model to learn the dictionary upon which the bag-of-words model is built. Moreover, we show that with the addition of these features, we’re able to improve regression root mean-squared error of energy expenditure by approximately 1.4 units over existing state-of-the-art methods. |
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| Published | 2017-04-05 |
| URL | http://arxiv.org/abs/1704.01574v2 |
| http://arxiv.org/pdf/1704.01574v2.pdf | |
| PWC | https://paperswithcode.com/paper/bag-of-words-method-applied-to-accelerometer |
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Teaching Machines to Describe Images via Natural Language Feedback
| Title | Teaching Machines to Describe Images via Natural Language Feedback |
| Authors | Huan Ling, Sanja Fidler |
| Abstract | Robots will eventually be part of every household. It is thus critical to enable algorithms to learn from and be guided by non-expert users. In this paper, we bring a human in the loop, and enable a human teacher to give feedback to a learning agent in the form of natural language. We argue that a descriptive sentence can provide a much stronger learning signal than a numeric reward in that it can easily point to where the mistakes are and how to correct them. We focus on the problem of image captioning in which the quality of the output can easily be judged by non-experts. We propose a hierarchical phrase-based captioning model trained with policy gradients, and design a feedback network that provides reward to the learner by conditioning on the human-provided feedback. We show that by exploiting descriptive feedback our model learns to perform better than when given independently written human captions. |
| Tasks | Image Captioning |
| Published | 2017-06-01 |
| URL | http://arxiv.org/abs/1706.00130v2 |
| http://arxiv.org/pdf/1706.00130v2.pdf | |
| PWC | https://paperswithcode.com/paper/teaching-machines-to-describe-images-via |
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Rapid point-of-care Hemoglobin measurement through low-cost optics and Convolutional Neural Network based validation
| Title | Rapid point-of-care Hemoglobin measurement through low-cost optics and Convolutional Neural Network based validation |
| Authors | Chris Wu, Tanay Tandon |
| Abstract | A low-cost, robust, and simple mechanism to measure hemoglobin would play a critical role in the modern health infrastructure. Consistent sample acquisition has been a long-standing technical hurdle for photometer-based portable hemoglobin detectors which rely on micro cuvettes and dry chemistry. Any particulates (e.g. intact red blood cells (RBCs), microbubbles, etc.) in a cuvette’s sensing area drastically impact optical absorption profile, and commercial hemoglobinometers lack the ability to automatically detect faulty samples. We present the ground-up development of a portable, low-cost and open platform with equivalent accuracy to medical-grade devices, with the addition of CNN-based image processing for rapid sample viability prechecks. The developed platform has demonstrated precision to the nearest $0.18[g/dL]$ of hemoglobin, an R^2 = 0.945 correlation to hemoglobin absorption curves reported in literature, and a 97% detection accuracy of poorly-prepared samples. We see the developed hemoglobin device/ML platform having massive implications in rural medicine, and consider it an excellent springboard for robust deep learning optical spectroscopy: a currently untapped source of data for detection of countless analytes. |
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| Published | 2017-12-01 |
| URL | http://arxiv.org/abs/1712.00174v1 |
| http://arxiv.org/pdf/1712.00174v1.pdf | |
| PWC | https://paperswithcode.com/paper/rapid-point-of-care-hemoglobin-measurement |
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Smoothness-based Edge Detection using Low-SNR Camera for Robot Navigation
| Title | Smoothness-based Edge Detection using Low-SNR Camera for Robot Navigation |
| Authors | Vu Hoang Minh, Tajwar Abrar Aleef, Usama Pervaiz, Yeman Brhane Hagos, Saed Khawaldeh |
| Abstract | In the emerging advancement in the branch of autonomous robotics, the ability of a robot to efficiently localize and construct maps of its surrounding is crucial. This paper deals with utilizing thermal-infrared cameras, as opposed to conventional cameras as the primary sensor to capture images of the robot’s surroundings. For localization, the images need to be further processed before feeding them to a navigational system. The main motivation of this paper was to develop an edge detection methodology capable of utilizing the low-SNR poor output from such a thermal camera and effectively detect smooth edges of the surrounding environment. The enhanced edge detector proposed in this paper takes the raw image from the thermal sensor, denoises the images, applies Canny edge detection followed by CSS method. The edges are ranked to remove any noise and only edges of the highest rank are kept. Then, the broken edges are linked by computing edge metrics and a smooth edge of the surrounding is displayed in a binary image. Several comparisons are also made in the paper between the proposed technique and the existing techniques. |
| Tasks | Edge Detection, Robot Navigation |
| Published | 2017-10-03 |
| URL | http://arxiv.org/abs/1710.01416v1 |
| http://arxiv.org/pdf/1710.01416v1.pdf | |
| PWC | https://paperswithcode.com/paper/smoothness-based-edge-detection-using-low-snr |
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Machine Learning for Structured Clinical Data
| Title | Machine Learning for Structured Clinical Data |
| Authors | Brett K. Beaulieu-Jones |
| Abstract | Research is a tertiary priority in the EHR, where the priorities are patient care and billing. Because of this, the data is not standardized or formatted in a manner easily adapted to machine learning approaches. Data may be missing for a large variety of reasons ranging from individual input styles to differences in clinical decision making, for example, which lab tests to issue. Few patients are annotated at a research quality, limiting sample size and presenting a moving gold standard. Patient progression over time is key to understanding many diseases but many machine learning algorithms require a snapshot, at a single time point, to create a usable vector form. Furthermore, algorithms that produce black box results do not provide the interpretability required for clinical adoption. This chapter discusses these challenges and others in applying machine learning techniques to the structured EHR (i.e. Patient Demographics, Family History, Medication Information, Vital Signs, Laboratory Tests, Genetic Testing). It does not cover feature extraction from additional sources such as imaging data or free text patient notes but the approaches discussed can include features extracted from these sources. |
| Tasks | Decision Making |
| Published | 2017-07-21 |
| URL | http://arxiv.org/abs/1707.06997v1 |
| http://arxiv.org/pdf/1707.06997v1.pdf | |
| PWC | https://paperswithcode.com/paper/machine-learning-for-structured-clinical-data |
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Out-of-focus Blur: Image De-blurring
| Title | Out-of-focus Blur: Image De-blurring |
| Authors | Yuzhen Lu |
| Abstract | Image de-blurring is important in many cases of imaging a real scene or object by a camera. This project focuses on de-blurring an image distorted by an out-of-focus blur through a simulation study. A pseudo-inverse filter is first explored but it fails because of severe noise amplification. Then Tikhonov regularization methods are employed, which produce greatly improved results compared to the pseudo-inverse filter. In Tikhonov regularization, the choice of the regularization parameter plays a critical rule in obtaining a high-quality image, and the regularized solutions possess a semi-convergence property. The best result, with the relative restoration error of 8.49%, is achieved when the prescribed discrepancy principle is used to decide an optimal value. Furthermore, an iterative method, Conjugated Gradient, is employed for image de-blurring, which is fast in computation and leads to an even better result with the relative restoration error of 8.22%. The number of iteration in CG acts as a regularization parameter, and the iterates have a semi-convergence property as well. |
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| Published | 2017-10-02 |
| URL | http://arxiv.org/abs/1710.00620v2 |
| http://arxiv.org/pdf/1710.00620v2.pdf | |
| PWC | https://paperswithcode.com/paper/out-of-focus-blur-image-de-blurring |
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Optimization Landscape and Expressivity of Deep CNNs
| Title | Optimization Landscape and Expressivity of Deep CNNs |
| Authors | Quynh Nguyen, Matthias Hein |
| Abstract | We analyze the loss landscape and expressiveness of practical deep convolutional neural networks (CNNs) with shared weights and max pooling layers. We show that such CNNs produce linearly independent features at a “wide” layer which has more neurons than the number of training samples. This condition holds e.g. for the VGG network. Furthermore, we provide for such wide CNNs necessary and sufficient conditions for global minima with zero training error. For the case where the wide layer is followed by a fully connected layer we show that almost every critical point of the empirical loss is a global minimum with zero training error. Our analysis suggests that both depth and width are very important in deep learning. While depth brings more representational power and allows the network to learn high level features, width smoothes the optimization landscape of the loss function in the sense that a sufficiently wide network has a well-behaved loss surface with almost no bad local minima. |
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| Published | 2017-10-30 |
| URL | http://arxiv.org/abs/1710.10928v2 |
| http://arxiv.org/pdf/1710.10928v2.pdf | |
| PWC | https://paperswithcode.com/paper/optimization-landscape-and-expressivity-of |
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Confidence estimation in Deep Neural networks via density modelling
| Title | Confidence estimation in Deep Neural networks via density modelling |
| Authors | Akshayvarun Subramanya, Suraj Srinivas, R. Venkatesh Babu |
| Abstract | State-of-the-art Deep Neural Networks can be easily fooled into providing incorrect high-confidence predictions for images with small amounts of adversarial noise. Does this expose a flaw with deep neural networks, or do we simply need a better way to estimate confidence? In this paper we consider the problem of accurately estimating predictive confidence. We formulate this problem as that of density modelling, and show how traditional methods such as softmax produce poor estimates. To address this issue, we propose a novel confidence measure based on density modelling approaches. We test these measures on images distorted by blur, JPEG compression, random noise and adversarial noise. Experiments show that our confidence measure consistently shows reduced confidence scores in the presence of such distortions - a property which softmax often lacks. |
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| Published | 2017-07-21 |
| URL | http://arxiv.org/abs/1707.07013v1 |
| http://arxiv.org/pdf/1707.07013v1.pdf | |
| PWC | https://paperswithcode.com/paper/confidence-estimation-in-deep-neural-networks |
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Can Machines Think in Radio Language?
| Title | Can Machines Think in Radio Language? |
| Authors | Yujian Li |
| Abstract | People can think in auditory, visual and tactile forms of language, so can machines principally. But is it possible for them to think in radio language? According to a first principle presented for general intelligence, i.e. the principle of language’s relativity, the answer may give an exceptional solution for robot astronauts to talk with each other in space exploration. |
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| Published | 2017-10-07 |
| URL | http://arxiv.org/abs/1710.02648v3 |
| http://arxiv.org/pdf/1710.02648v3.pdf | |
| PWC | https://paperswithcode.com/paper/can-machines-think-in-radio-language |
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Deep Neural Network with l2-norm Unit for Brain Lesions Detection
| Title | Deep Neural Network with l2-norm Unit for Brain Lesions Detection |
| Authors | Mina Rezaei, Haojin Yang, Christoph Meinel |
| Abstract | Automated brain lesions detection is an important and very challenging clinical diagnostic task because the lesions have different sizes, shapes, contrasts, and locations. Deep Learning recently has shown promising progress in many application fields, which motivates us to apply this technology for such important problem. In this paper, we propose a novel and end-to-end trainable approach for brain lesions classification and detection by using deep Convolutional Neural Network (CNN). In order to investigate the applicability, we applied our approach on several brain diseases including high and low-grade glioma tumor, ischemic stroke, Alzheimer diseases, by which the brain Magnetic Resonance Images (MRI) have been applied as an input for the analysis. We proposed a new operating unit which receives features from several projections of a subset units of the bottom layer and computes a normalized l2-norm for next layer. We evaluated the proposed approach on two different CNN architectures and number of popular benchmark datasets. The experimental results demonstrate the superior ability of the proposed approach. |
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| Published | 2017-08-17 |
| URL | http://arxiv.org/abs/1708.05221v1 |
| http://arxiv.org/pdf/1708.05221v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-neural-network-with-l2-norm-unit-for |
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