Paper Group ANR 834
Robust approximate linear regression without correspondence. Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data. Learning Erdős-Rényi Random Graphs via Edge Detecting Queries. Multiscale Principle of Relevant Information for Hyperspectral Image Classification. REFINED (REpresen …
Robust approximate linear regression without correspondence
| Title | Robust approximate linear regression without correspondence |
| Authors | Amin Nejatbakhsh, Erdem Varol |
| Abstract | We propose methods for estimating correspondence between two point sets under the presence of outliers in both the source and target sets. The proposed algorithms expand upon the theory of the regression without correspondence problem to estimate transformation coefficients using unordered multisets of covariates and responses. Previous theoretical analysis of the problem has been done in a setting where the responses are a complete permutation of the regressed covariates. This paper expands the problem setting by analyzing the cases where only a subset of the responses is a permutation of the regressed covariates in addition to some covariates being outliers. We term this problem \textit{robust regression without correspondence} and provide several algorithms based on random sample consensus for exact and approximate recovery in a noiseless and noisy one-dimensional setting as well as an approximation algorithm for multiple dimensions. The theoretical guarantees of the algorithms are verified in simulated data. We demonstrate an important computational neuroscience application of the proposed framework by demonstrating its effectiveness in a \textit{Caenorhabditis elegans} neuron matching problem where the presence of outliers in both the source and target nematodes is a natural tendency. |
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| Published | 2019-06-01 |
| URL | https://arxiv.org/abs/1906.00273v3 |
| https://arxiv.org/pdf/1906.00273v3.pdf | |
| PWC | https://paperswithcode.com/paper/190600273 |
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Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data
| Title | Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data |
| Authors | Johan S. Obando-Ceron, Victor Romero Cano, Walter Mayor Toro |
| Abstract | This paper presents a framework for efficiently learning feature selection policies which use less features to reach a high classification precision on large unstructured data. It uses a Deep Convolutional Autoencoder (DCAE) for learning compact feature spaces, in combination with recently-proposed Reinforcement Learning (RL) algorithms as Double DQN and Retrace. |
| Tasks | Feature Selection |
| Published | 2019-12-20 |
| URL | https://arxiv.org/abs/1912.09595v1 |
| https://arxiv.org/pdf/1912.09595v1.pdf | |
| PWC | https://paperswithcode.com/paper/exploiting-the-potential-of-deep |
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Learning Erdős-Rényi Random Graphs via Edge Detecting Queries
| Title | Learning Erdős-Rényi Random Graphs via Edge Detecting Queries |
| Authors | Zihan Li, Matthias Fresacher, Jonathan Scarlett |
| Abstract | In this paper, we consider the problem of learning an unknown graph via queries on groups of nodes, with the result indicating whether or not at least one edge is present among those nodes. While learning arbitrary graphs with $n$ nodes and $k$ edges is known to be hard in the sense of requiring $\Omega( \min{ k^2 \log n, n^2})$ tests (even when a small probability of error is allowed), we show that learning an Erd\H{o}s-R'enyi random graph with an average of $\bar{k}$ edges is much easier; namely, one can attain asymptotically vanishing error probability with only $O(\bar{k}\log n)$ tests. We establish such bounds for a variety of algorithms inspired by the group testing problem, with explicit constant factors indicating a near-optimal number of tests, and in some cases asymptotic optimality including constant factors. In addition, we present an alternative design that permits a near-optimal sublinear decoding time of $O(\bar{k} \log^2 \bar{k} + \bar{k} \log n)$. |
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| Published | 2019-05-09 |
| URL | https://arxiv.org/abs/1905.03410v4 |
| https://arxiv.org/pdf/1905.03410v4.pdf | |
| PWC | https://paperswithcode.com/paper/190503410 |
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Multiscale Principle of Relevant Information for Hyperspectral Image Classification
| Title | Multiscale Principle of Relevant Information for Hyperspectral Image Classification |
| Authors | Yantao Wei, Shujian Yu, Jose C. Principe |
| Abstract | This paper proposes a novel architecture, termed multiscale principle of relevant information (MPRI), to learn discriminative spectral-spatial features for hyperspectral image (HSI) classification. MPRI inherits the merits of the principle of relevant information (PRI) to effectively extract multiscale information embedded in the given data, and also takes advantage of the multilayer structure to learn representations in a coarse-to-fine manner. Specifically, MPRI performs spectral-spatial pixel characterization (using PRI) and feature dimensionality reduction (using regularized linear discriminant analysis) iteratively and successively. Extensive experiments on four benchmark data sets demonstrate that MPRI outperforms existing state-of-the-art HSI classification methods (including deep learning based ones) qualitatively and quantitatively, especially in the scenario of limited training samples. |
| Tasks | Dimensionality Reduction, Hyperspectral Image Classification, Image Classification |
| Published | 2019-07-13 |
| URL | https://arxiv.org/abs/1907.06022v1 |
| https://arxiv.org/pdf/1907.06022v1.pdf | |
| PWC | https://paperswithcode.com/paper/multiscale-principle-of-relevant-information |
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REFINED (REpresentation of Features as Images with NEighborhood Dependencies): A novel feature representation for Convolutional Neural Networks
| Title | REFINED (REpresentation of Features as Images with NEighborhood Dependencies): A novel feature representation for Convolutional Neural Networks |
| Authors | Omid Bazgir, Ruibo Zhang, Saugato Rahman Dhruba, Raziur Rahman, Souparno Ghosh, Ranadip Pal |
| Abstract | Deep learning with Convolutional Neural Networks has shown great promise in various areas of image-based classification and enhancement but is often unsuitable for predictive modeling involving non-image based features or features without spatial correlations. We present a novel approach for representation of high dimensional feature vector in a compact image form, termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies), that is conducible for convolutional neural network based deep learning. We consider the correlations between features to generate a compact representation of the features in the form of a two-dimensional image using minimization of pairwise distances similar to multi-dimensional scaling. We hypothesize that this approach enables embedded feature selection and integrated with Convolutional Neural Network based Deep Learning can produce more accurate predictions as compared to Artificial Neural Networks, Random Forests and Support Vector Regression. We illustrate the superior predictive performance of the proposed representation, as compared to existing approaches, using synthetic datasets, cell line efficacy prediction based on drug chemical descriptors for NCI60 dataset and drug sensitivity prediction based on transcriptomic data and chemical descriptors using GDSC dataset. Results illustrated on both synthetic and biological datasets shows the higher prediction accuracy of the proposed framework as compared to existing methodologies while maintaining desirable properties in terms of bias and feature extraction. |
| Tasks | Feature Selection |
| Published | 2019-12-11 |
| URL | https://arxiv.org/abs/1912.05687v1 |
| https://arxiv.org/pdf/1912.05687v1.pdf | |
| PWC | https://paperswithcode.com/paper/refined-representation-of-features-as-images |
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Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images
| Title | Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images |
| Authors | Ilya Kavalerov, Weilin Li, Wojciech Czaja, Rama Chellappa |
| Abstract | Recent research has resulted in many new techniques that are able to capture the special properties of hyperspectral data for hyperspectral image analysis, with hyperspectral image classification as one of the most active tasks. Time-frequency methods decompose spectra into multi-spectral bands, while hierarchical methods like neural networks incorporate spatial information across scales and model multiple levels of dependencies between spectral features. The Fourier scattering transform is an amalgamation of time-frequency representations with neural network architectures, both of which have recently been proven to provide significant advances in spectral-spatial classification. We test the proposed three dimensional Fourier scattering method on four standard hyperspectral datasets, and present results that indicate that the Fourier scattering transform is highly effective at representing spectral data when compared with other state-of-the-art spectral-spatial classification methods. |
| Tasks | Classification Of Hyperspectral Images, Hyperspectral Image Classification, Image Classification |
| Published | 2019-06-17 |
| URL | https://arxiv.org/abs/1906.06804v1 |
| https://arxiv.org/pdf/1906.06804v1.pdf | |
| PWC | https://paperswithcode.com/paper/three-dimensional-fourier-scattering |
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TraceWalk: Semantic-based Process Graph Embedding for Consistency Checking
| Title | TraceWalk: Semantic-based Process Graph Embedding for Consistency Checking |
| Authors | Chen Qian, Lijie Wen, Akhil Kumar |
| Abstract | Process consistency checking (PCC), an interdiscipline of natural language processing (NLP) and business process management (BPM), aims to quantify the degree of (in)consistencies between graphical and textual descriptions of a process. However, previous studies heavily depend on a great deal of complex expert-defined knowledge such as alignment rules and assessment metrics, thus suffer from the problems of low accuracy and poor adaptability when applied in open-domain scenarios. To address the above issues, this paper makes the first attempt that uses deep learning to perform PCC. Specifically, we proposed TraceWalk, using semantic information of process graphs to learn latent node representations, and integrates it into a convolutional neural network (CNN) based model called TraceNet to predict consistencies. The theoretical proof formally provides the PCC’s lower limit and experimental results demonstrate that our approach performs more accurately than state-of-the-art baselines. |
| Tasks | Graph Embedding |
| Published | 2019-05-16 |
| URL | https://arxiv.org/abs/1905.06883v1 |
| https://arxiv.org/pdf/1905.06883v1.pdf | |
| PWC | https://paperswithcode.com/paper/tracewalk-semantic-based-process-graph |
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Segmentation-Aware Hyperspectral Image Classification
| Title | Segmentation-Aware Hyperspectral Image Classification |
| Authors | Berkan Demirel, Omer Ozdil, Yunus Emre Esin, Safak Ozturk |
| Abstract | In this paper, we propose an unified hyperspectral image classification method which takes three-dimensional hyperspectral data cube as an input and produces a classification map. In the proposed method, a deep neural network which uses spectral and spatial information together with residual connections, and pixel affinity network based segmentation-aware superpixels are used together. In the architecture, segmentation-aware superpixels run on the initial classification map of deep residual network, and apply majority voting on obtained results. Experimental results show that our propoped method yields state-of-the-art results in two benchmark datasets. Moreover, we also show that the segmentation-aware superpixels have great contribution to the success of hyperspectral image classification methods in cases where training data is insufficient. |
| Tasks | Hyperspectral Image Classification, Image Classification |
| Published | 2019-05-22 |
| URL | https://arxiv.org/abs/1905.09211v1 |
| https://arxiv.org/pdf/1905.09211v1.pdf | |
| PWC | https://paperswithcode.com/paper/segmentation-aware-hyperspectral-image |
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What Gets Echoed? Understanding the “Pointers” in Explanations of Persuasive Arguments
| Title | What Gets Echoed? Understanding the “Pointers” in Explanations of Persuasive Arguments |
| Authors | David Atkinson, Kumar Bhargav Srinivasan, Chenhao Tan |
| Abstract | Explanations are central to everyday life, and are a topic of growing interest in the AI community. To investigate the process of providing natural language explanations, we leverage the dynamics of the /r/ChangeMyView subreddit to build a dataset with 36K naturally occurring explanations of why an argument is persuasive. We propose a novel word-level prediction task to investigate how explanations selectively reuse, or echo, information from what is being explained (henceforth, explanandum). We develop features to capture the properties of a word in the explanandum, and show that our proposed features not only have relatively strong predictive power on the echoing of a word in an explanation, but also enhance neural methods of generating explanations. In particular, while the non-contextual properties of a word itself are more valuable for stopwords, the interaction between the constituent parts of an explanandum is crucial in predicting the echoing of content words. We also find intriguing patterns of a word being echoed. For example, although nouns are generally less likely to be echoed, subjects and objects can, depending on their source, be more likely to be echoed in the explanations. |
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| Published | 2019-11-01 |
| URL | https://arxiv.org/abs/1911.00523v1 |
| https://arxiv.org/pdf/1911.00523v1.pdf | |
| PWC | https://paperswithcode.com/paper/what-gets-echoed-understanding-the-pointers-1 |
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Towards 3D Human Shape Recovery Under Clothing
| Title | Towards 3D Human Shape Recovery Under Clothing |
| Authors | Xin Chen, Anqi Pang, Yu Zhu, Yuwei Li, Xi Luo, Ge Zhang, Peihao Wang, Yingliang Zhang, Shiying Li, Jingyi Yu |
| Abstract | We present a learning-based scheme for robustly and accurately estimating clothing fitness as well as the human shape on clothed 3D human scans. Our approach maps the clothed human geometry to a geometry image that we call clothed-GI. To align clothed-GI under different clothing, we extend the parametric human model and employ skeleton detection and warping for reliable alignment. For each pixel on the clothed-GI, we extract a feature vector including color/texture, position, normal, etc. and train a modified conditional GAN network for per-pixel fitness prediction using a comprehensive 3D clothing. Our technique significantly improves the accuracy of human shape prediction, especially under loose and fitted clothing. We further demonstrate using our results for human/clothing segmentation and virtual clothes fitting at a high visual realism. |
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| Published | 2019-04-04 |
| URL | http://arxiv.org/abs/1904.02601v2 |
| http://arxiv.org/pdf/1904.02601v2.pdf | |
| PWC | https://paperswithcode.com/paper/towards-3d-human-shape-recovery-under |
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Scalable Graph Embeddings via Sparse Transpose Proximities
| Title | Scalable Graph Embeddings via Sparse Transpose Proximities |
| Authors | Yuan Yin, Zhewei Wei |
| Abstract | Graph embedding learns low-dimensional representations for nodes in a graph and effectively preserves the graph structure. Recently, a significant amount of progress has been made toward this emerging research area. However, there are several fundamental problems that remain open. First, existing methods fail to preserve the out-degree distributions on directed graphs. Second, many existing methods employ random walk based proximities and thus suffer from conflicting optimization goals on undirected graphs. Finally, existing factorization methods are unable to achieve scalability and non-linearity simultaneously. This paper presents an in-depth study on graph embedding techniques on both directed and undirected graphs. We analyze the fundamental reasons that lead to the distortion of out-degree distributions and to the conflicting optimization goals. We propose {\em transpose proximity}, a unified approach that solves both problems. Based on the concept of transpose proximity, we design \strap, a factorization based graph embedding algorithm that achieves scalability and non-linearity simultaneously. \strap makes use of the {\em backward push} algorithm to efficiently compute the sparse {\em Personalized PageRank (PPR)} as its transpose proximities. By imposing the sparsity constraint, we are able to apply non-linear operations to the proximity matrix and perform efficient matrix factorization to derive the embedding vectors. Finally, we present an extensive experimental study that evaluates the effectiveness of various graph embedding algorithms, and we show that \strap outperforms the state-of-the-art methods in terms of effectiveness and scalability. |
| Tasks | Graph Embedding |
| Published | 2019-05-16 |
| URL | https://arxiv.org/abs/1905.07245v1 |
| https://arxiv.org/pdf/1905.07245v1.pdf | |
| PWC | https://paperswithcode.com/paper/scalable-graph-embeddings-via-sparse |
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CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural Networks
| Title | CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural Networks |
| Authors | Xinrui Cui, Dan Wang, Z. Jane Wang |
| Abstract | With the widespread applications of deep convolutional neural networks (DCNNs), it becomes increasingly important for DCNNs not only to make accurate predictions but also to explain how they make their decisions. In this work, we propose a CHannel-wise disentangled InterPretation (CHIP) model to give the visual interpretation to the predictions of DCNNs. The proposed model distills the class-discriminative importance of channels in networks by utilizing the sparse regularization. Here, we first introduce the network perturbation technique to learn the model. The proposed model is capable to not only distill the global perspective knowledge from networks but also present the class-discriminative visual interpretation for specific predictions of networks. It is noteworthy that the proposed model is able to interpret different layers of networks without re-training. By combining the distilled interpretation knowledge in different layers, we further propose the Refined CHIP visual interpretation that is both high-resolution and class-discriminative. Experimental results on the standard dataset demonstrate that the proposed model provides promising visual interpretation for the predictions of networks in image classification task compared with existing visual interpretation methods. Besides, the proposed method outperforms related approaches in the application of ILSVRC 2015 weakly-supervised localization task. |
| Tasks | Image Classification |
| Published | 2019-02-07 |
| URL | https://arxiv.org/abs/1902.02497v2 |
| https://arxiv.org/pdf/1902.02497v2.pdf | |
| PWC | https://paperswithcode.com/paper/chip-channel-wise-disentangled-interpretation |
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Effective Use of Transformer Networks for Entity Tracking
| Title | Effective Use of Transformer Networks for Entity Tracking |
| Authors | Aditya Gupta, Greg Durrett |
| Abstract | Tracking entities in procedural language requires understanding the transformations arising from actions on entities as well as those entities’ interactions. While self-attention-based pre-trained language encoders like GPT and BERT have been successfully applied across a range of natural language understanding tasks, their ability to handle the nuances of procedural texts is still untested. In this paper, we explore the use of pre-trained transformer networks for entity tracking tasks in procedural text. First, we test standard lightweight approaches for prediction with pre-trained transformers, and find that these approaches underperform even simple baselines. We show that much stronger results can be attained by restructuring the input to guide the transformer model to focus on a particular entity. Second, we assess the degree to which transformer networks capture the process dynamics, investigating such factors as merged entities and oblique entity references. On two different tasks, ingredient detection in recipes and QA over scientific processes, we achieve state-of-the-art results, but our models still largely attend to shallow context clues and do not form complex representations of intermediate entity or process state. |
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| Published | 2019-09-05 |
| URL | https://arxiv.org/abs/1909.02635v1 |
| https://arxiv.org/pdf/1909.02635v1.pdf | |
| PWC | https://paperswithcode.com/paper/effective-use-of-transformer-networks-for |
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Speech Emotion Recognition with Dual-Sequence LSTM Architecture
| Title | Speech Emotion Recognition with Dual-Sequence LSTM Architecture |
| Authors | Jianyou Wang, Michael Xue, Ryan Culhane, Enmao Diao, Jie Ding, Vahid Tarokh |
| Abstract | Speech Emotion Recognition (SER) has emerged as a critical component of the next generation human-machine interfacing technologies. In this work, we propose a new dual-level model that predicts emotions based on both MFCC features and mel-spectrograms produced from raw audio signals. Each utterance is preprocessed into MFCC features and two mel-spectrograms at different time-frequency resolutions. A standard LSTM processes the MFCC features, while a novel LSTM architecture, denoted as Dual-Sequence LSTM (DS-LSTM), processes the two mel-spectrograms simultaneously. The outputs are later averaged to produce a final classification of the utterance. Our proposed model achieves, on average, a weighted accuracy of 72.7% and an unweighted accuracy of 73.3%—a 6% improvement over current state-of-the-art unimodal models—and is comparable with multimodal models that leverage textual information as well as audio signals. |
| Tasks | Emotion Recognition, Speech Emotion Recognition |
| Published | 2019-10-20 |
| URL | https://arxiv.org/abs/1910.08874v4 |
| https://arxiv.org/pdf/1910.08874v4.pdf | |
| PWC | https://paperswithcode.com/paper/speech-emotion-recognition-with-dual-sequence |
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Memory Augmented Recursive Neural Networks
| Title | Memory Augmented Recursive Neural Networks |
| Authors | Forough Arabshahi, Zhichu Lu, Sameer Singh, Animashree Anandkumar |
| Abstract | Recursive neural networks are able to capture compositionality of tree-structured data such as mathematical equations, and hence, enable better generalization. However, generalization to harder problem instances (a.k.a extrapolation) is challenging. This is due to error propagation along the nodes of the recursive network and the inability to capture long-range dependencies effectively. To overcome this, we propose to augment each node in the recursive network with an external memory in the form of a differentiable stack. This improves both local and global representation of compositional data due to better expressive power and the ability to capture long-range correlations. We demonstrate strong empirical results on the task of symbolic equation verification, where the stack augmentation enables accurate extrapolation to significantly harder instances. |
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| Published | 2019-11-05 |
| URL | https://arxiv.org/abs/1911.01545v3 |
| https://arxiv.org/pdf/1911.01545v3.pdf | |
| PWC | https://paperswithcode.com/paper/memory-augmented-recursive-neural-networks |
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