Paper Group ANR 733
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans. Evaluation of Machine Learning Algorithms for Intrusion Detection System. Automated Diagnosis of Clinic Workflows. Effects of Dataset properties on the training of GANs. Unsupervised Domain Adaptation: A Multi-task Learning-based Method. ToyBox: Better Atari Envi …
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans
| Title | RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans |
| Authors | Qiangguo Jin, Zhaopeng Meng, Changming Sun, Leyi Wei, Ran Su |
| Abstract | Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D and 3D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, 3D networks have some drawbacks due to their high cost on computational resources. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, named RA-UNet, to precisely extract the liver volume of interests (VOI) and segment tumors from the liver VOI. The proposed network has a basic architecture as a 3D U-Net which extracts contextual information combining low-level feature maps with high-level ones. Attention modules are stacked so that the attention-aware features change adaptively as the network goes “very deep” and this is made possible by residual learning. This is the first work that an attention residual mechanism is used to process medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and the 3DIRCADb dataset. The results show that our architecture outperforms other state-of-the-art methods. We also extend our RA-UNet to brain tumor segmentation on the BraTS2018 and BraTS2017 datasets, and the results indicate that RA-UNet achieves good performance on a brain tumor segmentation task as well. |
| Tasks | Brain Tumor Segmentation, Deep Attention, Medical Image Segmentation, Semantic Segmentation |
| Published | 2018-11-04 |
| URL | http://arxiv.org/abs/1811.01328v1 |
| http://arxiv.org/pdf/1811.01328v1.pdf | |
| PWC | https://paperswithcode.com/paper/ra-unet-a-hybrid-deep-attention-aware-network |
| Repo | |
| Framework | |
Evaluation of Machine Learning Algorithms for Intrusion Detection System
| Title | Evaluation of Machine Learning Algorithms for Intrusion Detection System |
| Authors | Mohammad Almseidin, Maen Alzubi, Szilveszter Kovacs, Mouhammd Alkasassbeh |
| Abstract | Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate. |
| Tasks | Intrusion Detection |
| Published | 2018-01-08 |
| URL | http://arxiv.org/abs/1801.02330v1 |
| http://arxiv.org/pdf/1801.02330v1.pdf | |
| PWC | https://paperswithcode.com/paper/evaluation-of-machine-learning-algorithms-for |
| Repo | |
| Framework | |
Automated Diagnosis of Clinic Workflows
| Title | Automated Diagnosis of Clinic Workflows |
| Authors | Alex Cheng, Jules White |
| Abstract | Outpatient clinics often run behind schedule due to patients who arrive late or appointments that run longer than expected. We sought to develop a generalizable method that would allow healthcare providers to diagnose problems in workflow that disrupt the schedule on any given provider clinic day. We use a constraint optimization problem to identify the least number of appointment modifications that make the rest of the schedule run on-time. We apply this method to an outpatient clinic at Vanderbilt. For patient seen in this clinic between March 27, 2017 and April 21, 2017, long cycle times tended to affect the overall schedule more than late patients. Results from this workflow diagnosis method could be used to inform interventions to help clinics run smoothly, thus decreasing patient wait times and increasing provider utilization. |
| Tasks | |
| Published | 2018-05-06 |
| URL | http://arxiv.org/abs/1805.02264v1 |
| http://arxiv.org/pdf/1805.02264v1.pdf | |
| PWC | https://paperswithcode.com/paper/automated-diagnosis-of-clinic-workflows |
| Repo | |
| Framework | |
Effects of Dataset properties on the training of GANs
| Title | Effects of Dataset properties on the training of GANs |
| Authors | Ilya Kamenshchikov, Matthias Krauledat |
| Abstract | Generative Adversarial Networks are a new family of generative models, frequently used for generating photorealistic images. The theory promises for the GAN to eventually reach an equilibrium where generator produces pictures indistinguishable for the training set. In practice, however, a range of problems frequently prevents the system from reaching this equilibrium, with training not progressing ahead due to instabilities or mode collapse. This paper describes a series of experiments trying to identify patterns in regard to the effect of the training set on the dynamics and eventual outcome of the training. |
| Tasks | |
| Published | 2018-11-07 |
| URL | http://arxiv.org/abs/1811.02850v3 |
| http://arxiv.org/pdf/1811.02850v3.pdf | |
| PWC | https://paperswithcode.com/paper/effects-of-dataset-properties-on-the-training |
| Repo | |
| Framework | |
Unsupervised Domain Adaptation: A Multi-task Learning-based Method
| Title | Unsupervised Domain Adaptation: A Multi-task Learning-based Method |
| Authors | Jing Zhang, Wanqing Li, Philip Ogunbona |
| Abstract | This paper presents a novel multi-task learning-based method for unsupervised domain adaptation. Specifically, the source and target domain classifiers are jointly learned by considering the geometry of target domain and the divergence between the source and target domains based on the concept of multi-task learning. Two novel algorithms are proposed upon the method using Regularized Least Squares and Support Vector Machines respectively. Experiments on both synthetic and real world cross domain recognition tasks have shown that the proposed methods outperform several state-of-the-art domain adaptation methods. |
| Tasks | Domain Adaptation, Multi-Task Learning, Unsupervised Domain Adaptation |
| Published | 2018-03-25 |
| URL | http://arxiv.org/abs/1803.09208v1 |
| http://arxiv.org/pdf/1803.09208v1.pdf | |
| PWC | https://paperswithcode.com/paper/unsupervised-domain-adaptation-a-multi-task |
| Repo | |
| Framework | |
ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents
| Title | ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents |
| Authors | John Foley, Emma Tosch, Kaleigh Clary, David Jensen |
| Abstract | It is a widely accepted principle that software without tests has bugs. Testing reinforcement learning agents is especially difficult because of the stochastic nature of both agents and environments, the complexity of state-of-the-art models, and the sequential nature of their predictions. Recently, the Arcade Learning Environment (ALE) has become one of the most widely used benchmark suites for deep learning research, and state-of-the-art Reinforcement Learning (RL) agents have been shown to routinely equal or exceed human performance on many ALE tasks. Since ALE is based on emulation of original Atari games, the environment does not provide semantically meaningful representations of internal game state. This means that ALE has limited utility as an environment for supporting testing or model introspection. We propose ToyBox, a collection of reimplementations of these games that solves this critical problem and enables robust testing of RL agents. |
| Tasks | Atari Games |
| Published | 2018-12-06 |
| URL | http://arxiv.org/abs/1812.02850v3 |
| http://arxiv.org/pdf/1812.02850v3.pdf | |
| PWC | https://paperswithcode.com/paper/toybox-better-atari-environments-for-testing |
| Repo | |
| Framework | |
Optimizing Query Evaluations using Reinforcement Learning for Web Search
| Title | Optimizing Query Evaluations using Reinforcement Learning for Web Search |
| Authors | Corby Rosset, Damien Jose, Gargi Ghosh, Bhaskar Mitra, Saurabh Tiwary |
| Abstract | In web search, typically a candidate generation step selects a small set of documents—from collections containing as many as billions of web pages—that are subsequently ranked and pruned before being presented to the user. In Bing, the candidate generation involves scanning the index using statically designed match plans that prescribe sequences of different match criteria and stopping conditions. In this work, we pose match planning as a reinforcement learning task and observe up to 20% reduction in index blocks accessed, with small or no degradation in the quality of the candidate sets. |
| Tasks | |
| Published | 2018-04-12 |
| URL | https://arxiv.org/abs/1804.04410v2 |
| https://arxiv.org/pdf/1804.04410v2.pdf | |
| PWC | https://paperswithcode.com/paper/optimizing-query-evaluations-using |
| Repo | |
| Framework | |
Adversarial Text Generation via Feature-Mover’s Distance
| Title | Adversarial Text Generation via Feature-Mover’s Distance |
| Authors | Liqun Chen, Shuyang Dai, Chenyang Tao, Dinghan Shen, Zhe Gan, Haichao Zhang, Yizhe Zhang, Lawrence Carin |
| Abstract | Generative adversarial networks (GANs) have achieved significant success in generating real-valued data. However, the discrete nature of text hinders the application of GAN to text-generation tasks. Instead of using the standard GAN objective, we propose to improve text-generation GAN via a novel approach inspired by optimal transport. Specifically, we consider matching the latent feature distributions of real and synthetic sentences using a novel metric, termed the feature-mover’s distance (FMD). This formulation leads to a highly discriminative critic and easy-to-optimize objective, overcoming the mode-collapsing and brittle-training problems in existing methods. Extensive experiments are conducted on a variety of tasks to evaluate the proposed model empirically, including unconditional text generation, style transfer from non-parallel text, and unsupervised cipher cracking. The proposed model yields superior performance, demonstrating wide applicability and effectiveness. |
| Tasks | Adversarial Text, Style Transfer, Text Generation |
| Published | 2018-09-17 |
| URL | http://arxiv.org/abs/1809.06297v1 |
| http://arxiv.org/pdf/1809.06297v1.pdf | |
| PWC | https://paperswithcode.com/paper/adversarial-text-generation-via-feature |
| Repo | |
| Framework | |
Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information
| Title | Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information |
| Authors | Seonhoon Kim, Inho Kang, Nojun Kwak |
| Abstract | Sentence matching is widely used in various natural language tasks such as natural language inference, paraphrase identification, and question answering. For these tasks, understanding logical and semantic relationship between two sentences is required but it is yet challenging. Although attention mechanism is useful to capture the semantic relationship and to properly align the elements of two sentences, previous methods of attention mechanism simply use a summation operation which does not retain original features enough. Inspired by DenseNet, a densely connected convolutional network, we propose a densely-connected co-attentive recurrent neural network, each layer of which uses concatenated information of attentive features as well as hidden features of all the preceding recurrent layers. It enables preserving the original and the co-attentive feature information from the bottommost word embedding layer to the uppermost recurrent layer. To alleviate the problem of an ever-increasing size of feature vectors due to dense concatenation operations, we also propose to use an autoencoder after dense concatenation. We evaluate our proposed architecture on highly competitive benchmark datasets related to sentence matching. Experimental results show that our architecture, which retains recurrent and attentive features, achieves state-of-the-art performances for most of the tasks. |
| Tasks | Natural Language Inference, Paraphrase Identification, Question Answering |
| Published | 2018-05-29 |
| URL | http://arxiv.org/abs/1805.11360v2 |
| http://arxiv.org/pdf/1805.11360v2.pdf | |
| PWC | https://paperswithcode.com/paper/semantic-sentence-matching-with-densely |
| Repo | |
| Framework | |
Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming
| Title | Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming |
| Authors | Bart van Merriënboer, Dan Moldovan, Alexander B Wiltschko |
| Abstract | The need to efficiently calculate first- and higher-order derivatives of increasingly complex models expressed in Python has stressed or exceeded the capabilities of available tools. In this work, we explore techniques from the field of automatic differentiation (AD) that can give researchers expressive power, performance and strong usability. These include source-code transformation (SCT), flexible gradient surgery, efficient in-place array operations, higher-order derivatives as well as mixing of forward and reverse mode AD. We implement and demonstrate these ideas in the Tangent software library for Python, the first AD framework for a dynamic language that uses SCT. |
| Tasks | |
| Published | 2018-09-25 |
| URL | http://arxiv.org/abs/1809.09569v2 |
| http://arxiv.org/pdf/1809.09569v2.pdf | |
| PWC | https://paperswithcode.com/paper/tangent-automatic-differentiation-using |
| Repo | |
| Framework | |
The Importance of Constraint Smoothness for Parameter Estimation in Computational Cognitive Modeling
| Title | The Importance of Constraint Smoothness for Parameter Estimation in Computational Cognitive Modeling |
| Authors | Abraham Nunes, Alexander Rudiuk |
| Abstract | Psychiatric neuroscience is increasingly aware of the need to define psychopathology in terms of abnormal neural computation. The central tool in this endeavour is the fitting of computational models to behavioural data. The most prominent example of this procedure is fitting reinforcement learning (RL) models to decision-making data collected from mentally ill and healthy subject populations. These models are generative models of the decision-making data themselves, and the parameters we seek to infer can be psychologically and neurobiologically meaningful. Currently, the gold standard approach to this inference procedure involves Monte-Carlo sampling, which is robust but computationally intensive—rendering additional procedures, such as cross-validation, impractical. Searching for point estimates of model parameters using optimization procedures remains a popular and interesting option. On a novel testbed simulating parameter estimation from a common RL task, we investigated the effects of smooth vs. boundary constraints on parameter estimation using interior point and deterministic direct search algorithms for optimization. Ultimately, we show that the use of boundary constraints can lead to substantial truncation effects. Our results discourage the use of boundary constraints for these applications. |
| Tasks | Decision Making |
| Published | 2018-03-24 |
| URL | http://arxiv.org/abs/1803.09018v1 |
| http://arxiv.org/pdf/1803.09018v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-importance-of-constraint-smoothness-for |
| Repo | |
| Framework | |
Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy
| Title | Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy |
| Authors | Neema Davis, Gaurav Raina, Krishna Jagannathan |
| Abstract | A key problem in location-based modeling and forecasting lies in identifying suitable spatial and temporal resolutions. In particular, judicious spatial partitioning can play a significant role in enhancing the performance of location-based forecasting models. In this work, we investigate two widely used tessellation strategies for partitioning city space, in the context of real-time taxi demand forecasting. Our study compares (i) Geohash tessellation, and (ii) Voronoi tessellation, using two distinct taxi demand datasets, over multiple time scales. For the purpose of comparison, we employ classical time-series tools to model the spatio-temporal demand. Our study finds that the performance of each tessellation strategy is highly dependent on the city geography, spatial distribution of the data, and the time of the day, and that neither strategy is found to perform optimally across the forecast horizon. We propose a hybrid tessellation algorithm that picks the best tessellation strategy at each instant, based on their performance in the recent past. Our hybrid algorithm is a non-stationary variant of the well-known HEDGE algorithm for choosing the best advice from multiple experts. We show that the hybrid tessellation strategy performs consistently better than either of the two strategies across the data sets considered, at multiple time scales, and with different performance metrics. We achieve an average accuracy of above 80% per km^2 for both data sets considered at 60 minute aggregation levels. |
| Tasks | Time Series |
| Published | 2018-05-17 |
| URL | http://arxiv.org/abs/1805.06619v2 |
| http://arxiv.org/pdf/1805.06619v2.pdf | |
| PWC | https://paperswithcode.com/paper/taxi-demand-forecasting-a-hedge-based |
| Repo | |
| Framework | |
Movie Question Answering: Remembering the Textual Cues for Layered Visual Contents
| Title | Movie Question Answering: Remembering the Textual Cues for Layered Visual Contents |
| Authors | Bo Wang, Youjiang Xu, Yahong Han, Richang Hong |
| Abstract | Movies provide us with a mass of visual content as well as attracting stories. Existing methods have illustrated that understanding movie stories through only visual content is still a hard problem. In this paper, for answering questions about movies, we put forward a Layered Memory Network (LMN) that represents frame-level and clip-level movie content by the Static Word Memory module and the Dynamic Subtitle Memory module, respectively. Particularly, we firstly extract words and sentences from the training movie subtitles. Then the hierarchically formed movie representations, which are learned from LMN, not only encode the correspondence between words and visual content inside frames, but also encode the temporal alignment between sentences and frames inside movie clips. We also extend our LMN model into three variant frameworks to illustrate the good extendable capabilities. We conduct extensive experiments on the MovieQA dataset. With only visual content as inputs, LMN with frame-level representation obtains a large performance improvement. When incorporating subtitles into LMN to form the clip-level representation, we achieve the state-of-the-art performance on the online evaluation task of ‘Video+Subtitles’. The good performance successfully demonstrates that the proposed framework of LMN is effective and the hierarchically formed movie representations have good potential for the applications of movie question answering. |
| Tasks | Question Answering, Video Question Answering |
| Published | 2018-04-25 |
| URL | http://arxiv.org/abs/1804.09412v1 |
| http://arxiv.org/pdf/1804.09412v1.pdf | |
| PWC | https://paperswithcode.com/paper/movie-question-answering-remembering-the |
| Repo | |
| Framework | |
Finding Appropriate Traffic Regulations via Graph Convolutional Networks
| Title | Finding Appropriate Traffic Regulations via Graph Convolutional Networks |
| Authors | Tomoharu Iwata, Takuma Otsuka, Hitoshi Shimizu, Hiroshi Sawada, Futoshi Naya, Naonori Ueda |
| Abstract | Appropriate traffic regulations, e.g. planned road closure, are important in congested events. Crowd simulators have been used to find appropriate regulations by simulating multiple scenarios with different regulations. However, this approach requires multiple simulation runs, which are time-consuming. In this paper, we propose a method to learn a function that outputs regulation effects given the current traffic situation as inputs. If the function is learned using the training data of many simulation runs in advance, we can obtain an appropriate regulation efficiently by bypassing simulations for the current situation. We use the graph convolutional networks for modeling the function, which enable us to find regulations even for unseen areas. With the proposed method, we construct a graph for each area, where a node represents a road, and an edge represents the road connection. By running crowd simulations with various regulations on various areas, we generate traffic situations and regulation effects. The graph convolutional networks are trained to output the regulation effects given the graph with the traffic situation information as inputs. With experiments using real-world road networks and a crowd simulator, we demonstrate that the proposed method can find a road to close that reduces the average time needed to reach the destination. |
| Tasks | |
| Published | 2018-10-23 |
| URL | http://arxiv.org/abs/1810.09712v1 |
| http://arxiv.org/pdf/1810.09712v1.pdf | |
| PWC | https://paperswithcode.com/paper/finding-appropriate-traffic-regulations-via |
| Repo | |
| Framework | |
Which Knowledge Graph Is Best for Me?
| Title | Which Knowledge Graph Is Best for Me? |
| Authors | Michael Färber, Achim Rettinger |
| Abstract | In recent years, DBpedia, Freebase, OpenCyc, Wikidata, and YAGO have been published as noteworthy large, cross-domain, and freely available knowledge graphs. Although extensively in use, these knowledge graphs are hard to compare against each other in a given setting. Thus, it is a challenge for researchers and developers to pick the best knowledge graph for their individual needs. In our recent survey, we devised and applied data quality criteria to the above-mentioned knowledge graphs. Furthermore, we proposed a framework for finding the most suitable knowledge graph for a given setting. With this paper we intend to ease the access to our in-depth survey by presenting simplified rules that map individual data quality requirements to specific knowledge graphs. However, this paper does not intend to replace our previously introduced decision-support framework. For an informed decision on which KG is best for you we still refer to our in-depth survey. |
| Tasks | Knowledge Graphs |
| Published | 2018-09-28 |
| URL | http://arxiv.org/abs/1809.11099v1 |
| http://arxiv.org/pdf/1809.11099v1.pdf | |
| PWC | https://paperswithcode.com/paper/which-knowledge-graph-is-best-for-me |
| Repo | |
| Framework | |
