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Check out the latest superpixel benchmark — Superpixel Benchmark (2016) — and let me know your opinion! @david_stutz

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READINGS

  • M. Johnson, K. Hofmann, T. Hutton, D. Bignell. The Malmo Platform for Artificial Intelligence Experimentation. International Joint Conference on Artificial Intelligence, 2015.   Reading Notes
  • S. J. Russell, P. Norvig. Artifical Intelligence: A Modern Approach. Pearson Education, 2nd Edition, 2003.   Reading Notes
  • A. Koenig, B. Moo. C++ Made Easier: The Rule of Three. 2001.   Reading Notes
  • S. B. Lippman, J. Lajoie, B. E. Moo. C++ Primer (4th Edition). Addison-Wesley Professional, 2005.   Reading Notes
  • G. Kutyniok. Theory and Applications of Compressed Sensing. Computing Research Repository, abs/1203.3815, 2012.   Reading Notes
  • R. Socher, B. Huval, B. P. Bath, C. D. Manning, A. Y. Ng. Convolutional-Recursive Deep Learning for 3D Object Classification. NIPS, 2012.   Reading Notes
  • M. Engelcke, D. Rao, D. Zeng Wang, C. H. Tong, I. Posner. Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. CoRR, 2016.   Reading Notes
  • Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, O. Ronneberger. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. MICCAI, 2016.   Reading Notes
  • J. Huang, S. You. Point cloud labeling using 3d convolutional neural network. ICPR, 2016.   Reading Notes
  • A. Brock, T. Lim, J. M. Ritchie, N. Weston. Generative and Discriminative Voxel Modeling with Convolutional Neural Networks. CoRR, 2016.   Reading Notes
  • S. Song, J. Xiao. Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images. CVPR, 2016.   Reading Notes
  • Nima Sedaghat Alvar, Mohammadreza Zolfaghari, Thomas Brox. Orientation-boosted Voxel Nets for 3D Object Recognition. CoRR, abs/1604.03351, 2016.   Reading Notes
  • Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, Joshua B. Tenenbaum. Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. CoRR, 2016.   Reading Notes
  • Hang Su, Subhransu Maji, Evangelos Kalogerakis, Erik G. Learned-Miller. Multi-view Convolutional Neural Networks for 3D Shape Recognition. CoRR, 2015.   Reading Notes
  • Daniel Maturana, Sebastian Scherer. 3D Convolutional Neural Networks for Landing Zone Detection from LiDAR. ICRA, 2015.   Reading Notes
  • Ben Graham. Sparse 3D convolutional neural networks. BMVC, 2015.   Reading Notes
  • Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, J. Xiao. 3D ShapeNets: A Deep Representation for Volumetric Shape Modeling. CVPR, 2015.   Reading Notes
  • Yangyan Li, Sören Pirk, Hao Su, Charles Ruizhongtai Qi, Leonidas J. Guibas. FPNN: Field Probing Neural Networks for 3D Data. CoRR, abs/1605.06240.   Reading Notes
  • Charles Ruizhongtai Qi. Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, Leonidas Guibas. Volumetric and Multi-View CNNs for Object Classification on 3D Data. CVPR, 2016.   Reading Notes
  • Q. Dou, H. Chen, L. Yu, L. Zhao, J. Qin, D. Wang, V. C. T. Mok, L. Shi, P.-A. Heng. Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks. IEEE Trans. Med. Imaging, 2016.   Reading Notes
  • H. Chen, Q. Dou, L. Yu, P.-A. Heng. VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation. CoRR, 2016.   Reading Notes
  • B. Li, T. Zhang, T. Xia. Vehicle Detection from 3D Lidar Using Fully Convolutional Network. Robotics: Science and Systems, 2016.   Reading Notes
  • Daniel Maturana, Sebastian Scherer. VoxNet: A 3D Convolutional Neural Network for real-time object recognition. IROS, 2015.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 9, MIT Press, 2016.   Reading Notes
  • P. Márquez-Neila, P. Kohli, C. Rother, L. Baumela. Non-parametric Higher-Order Random Fields for Image Segmentation. ECCV, 2014.   Reading Notes
  • P. Arbeláez J. Pont-Tuset, J. T. Barron, F. Marqués, J. Malik. Multiscale Combinatorial Grouping.Conference on Computer Vision and Pattern Recognition, 2014.   Reading Notes
  • P. Arbelaez. Boundary Extraction in Natural Images Using Ultrametric Contour Maps. Conference on Computer Vision and Pattern Recognition, 2006.   Reading Notes
  • J. Mairal, F. Bach, J. Ponce. Sparse Modeling for Image and Vision Processing. Foundations and Trends in Computer Graphics and Vision, colume 8, 2014.   Reading Notes
  • M. Menze, A. Geiger. Object scene flow for autonomous vehicles. Conference on Computer Vision and Pattern Recognition, 2015.   Reading Notes
  • L. Sevilla-Lara, D. Sun, V. Jampani, M. J. Black. Optical Flow with Semantic Segmentation and Localized Layers. Computing Research Repository, abs/1603.03911, 2016.   Reading Notes
  • M. Kiefel, V. Jampani, P. V. Gehler. Permutohedral Lattice CNNs. Computing Research Repository, abs/1412.6618, 2014.   Reading Notes
  • David G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91–110, November 2004.   Reading Notes
  • O. Chum, J. Philbin, J. Sivic, M. Isard, A. Zisserman. Total recall: Automatic query expansion with a generative feature model for object retrieval. In Computer Vision, International Conference on, pages 1–8, Rio de Janeiro, Brazil, October 2007.   Reading Notes
  • C. Harris, M. Stephens. A combined corner and edge detector. In Alvey Vision Conference, pages 1–6, Manchester, United Kingdom, September 1988.   Reading Notes
  • S. Vedula, S. Baker, P. Rander, R. T. Collins, T. Kanade. Three-Dimensional Scene Flow. Transactions on Pattern Analysis and Machine Intelligence, 2005.   Reading Notes
  • A. Geiger, P. Lenz, R. Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark. Conference on Computer Vision and Pattern Recognition, 2012.   Reading Notes
  • J. Shen. Gamma-Convergence Approximation to Piecewise Constant Mumford-Shah Segmentation. International Conference on Advanced Concepts for Intelligent Vision Systems, 2005.   Reading Notes
  • P. L. Combettes and J.-C. Pesquet.Proximal Splitting Methods in Signal Processing. Fixed-Point Algorithms for Inverse Problems in Science and Engineering, Springer, 2011.   Reading Notes
  • Y. Chen, T. Pock, R. Ranftl, H. Bischof. Revisiting Loss-Specific Training of Filter-Based MRFs for Image Restoration. German Conference on Pattern Recognition, 2015.   Reading Notes
  • P. Ochs, Y. Chen,T. Brox, T. Pock. iPiano: Inertial Proximal Algorithm for Non-Convex Optimization. Computing Research Repository, abs/1404.4805, 2014.   Reading Notes
  • A. Babenko, A. Slesarev, A. Chigorin, V. S. Lempitsky. Neural codes for image retrieval. In Computer Vision, European Conference on, volume 8689 of Lecture Notes in Computer Science, pages 584–599, Zurich, Switzerland, September 2014. Springer.   Reading Notes
  • H. Jégou, M. Douze, C. Schmid. Product quantization for nearest neighbor search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(1):117–128, 2011.   Reading Notes
  • A. Gordo, J. A. Rodríguez-Serrano, F. Perronnin, E. Valveny. Leveraging category-level labels for instance-level image retrieval. In Computer Vision and Pattern Recognition, Conference on, pages 3045–3052, Providence, Rhode Island, June 2012.   Reading Notes
  • H. Jégou, A. Zisserman. Triangulation embedding and democratic aggregation for image search. In Computer Vision and Pattern Recognition, Conference on, pages 3310–3317, Columbus, June 2014.   Reading Notes
  • J. Philbin, M. Isard, J. Sivic, and A. Zisserman. Descriptor learning for efficient retrieval. In Computer Vision, European Conference on, volume 6313 of Lecture Notes in Computer Science, pages 677–691, Heraklion, Greece, September 2010. Springer.   Reading Notes
  • H. Jégou, M. Douze, C. C. Schmid, P. Pérez. Aggregating local descriptors into a compact image representation. In Computer Vision and Pattern Recognition, Conference on, pages 3304–3311, San Fransisco, California, June 2010.   Reading Notes
  • F. Perronnin, C. R. Dance. Fisher kernels on visual vocabularies for image categorization. In Computer Vision and Pattern Recognition, Conference on, pages 1–8, Minneapolis, Minnesota, June 2007.   Reading Notes
  • T. Ge, Q. Ke, J, Sun. Sparse-coded features for image retrieval. In British Machine Vision Conference, Bristol, United Kingdom, September 2013.   Reading Notes
  • J. Sivic, A. Zisserman. Video google: A text retrieval approach to object matching in videos. In Computer Vision, International Conference on, pages 1470–1477, Nice, France, October 2003.   Reading Notes
  • M. Grundmann, V. Kwatra, M. Han, I. A. Essa. Efficient hierarchical graph-based video segmentation. Conference on Computer Vision and Pattern, San Francisco, 2010.   Reading Notes
  • Y. Zhang, R. Hartley, J. Mashford, S. Burn. Superpixels via pseudoboolean optimization. International Conference on Computer Vision, 2011.   Reading Notes
  • D. Tang, H. Fu, X. Cao. Topology preserved regular superpixel International Conference on Multimedia and Expo, 2012.   Reading Notes
  • K. Tang, R. Sukthankar, J. Yagnik, L. Fei-Fei. Discriminative Segment Annotation in Weakly Labeled Video. Conference on Computer Vision and Pattern Recognition, 2013.   Reading Notes
  • A. Babenko, A. Slesarev, A. Chigorin, and V. S. Lempitsky. Neural codes for image retrieval. In Conference on Computer Vision, pages 584–599, 2014.   Reading Notes
  • Y. Ganin, V. S. Lempitsky. N^4-Fields: Neural Network Nearest Neighbor Fields for Image Transforms. Computing Research Repository, 2014.   Reading Notes
  • P. Dollár, C. Zitnick. Structured Forests for Fast Edge Detection. International Conference on Computer Vision, 2013.   Reading Notes
  • C. Xu, J. J. Corso. Evaluation of Super-voxel Methods for Early Video Processing. Conference on Computer Vision and Pattern Recognition, 2012.   Reading Notes
  • C. Conrad, M. Mertz, R. Mester. Contour-relaxed superpixels. Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013.   Reading Notes
  • S. Beigpour, M. Serra, J. van de Weijer, R. Benavente, M. Vanrell, O. Penacchio, D. Samaras. Intrinsic Image Evaluation on Synthetic Complex Scenes. International Conference on Image Processing, 2013.   Reading Notes
  • M. Y. Lui, O. Tuzel, S. Ramalingam, R. Chellappa. Entropy rate superpixel segmentation. Conference on Computer Vision and Pattern Recognition, 2011.   Reading Notes
  • O. Veksler, Y. Boykov, P. Mehrani. Superpixels and supervoxels in an energy optimization framework. European Conference on Computer Vision, pages 211–224, 2010.   Reading Notes
  • R. Grosse, M. K. Johnson, E. H. Adelson, W. T. Freeman. Ground truth dataset and baseline evaluations for intrinsic image algorithms. International Conference on Computer Vision, 2009.   Reading Notes
  • A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, K. Siddiqi. TurboPixels: Fast superpixels using geometric flows. Transactions on Pattern Analysis and Machine Intelligence, 31(12):2290–2297, 2009.   Reading Notes
  • A. Vedaldi, S. Soatto. Quick Shift and Kernel Methods for Mode Seeking. European Conference on Computer Vision, 2008.   Reading Notes
  • P. F. Felzenswalb and D. P. Huttenlocher. Efficient graph-based image segmentation. International Journal of Computer Vision, volume 59, number 2, 2004.   Reading Notes
  • N. C. Oza. Online Bagging and Boosting. International Conference on Systems, Man and Cybernetics, 2005.   Reading Notes
  • A. A. Amini, T. E. Weymouth, R. C. Jain. Using Dynamic Programming for Solving Variational Problems in Vision. Transactions on Pattern Analysis and Machine Intelligence, 2002.   Reading Notes
  • J. Shi, J. Malik. Normalized Cuts and Image Segmentation. Transactions on Pattern Analysis and Machine Intelligence, 2008.   Reading Notes
  • S. Gupta, P. Arbelaez, J. Malik. Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images. Conference on Computer Vision and Pattern Recognition, 2013.   Reading Notes
  • S. R. Bulò, P. Kontschieder. Neural Decision Forests for Semantic Image Labelling. Conference on Computer Vision and Pattern Recognition, 2014.   Reading Notes
  • R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Süsstrunk. SLIC Superpixels Compared to State-Of-The-Art Superpixel Methods. Transactions on Pattern Analysis and Machine Intelligence, pages 2274 – 2282, 2012.   Reading Notes
  • M. Van den Bergh, X. Boix, G. Roig, B. de Capitani, L. van Gool. SEEDS – Superpixels Extracted via Energy-Driven Sampling. European Conference on Computer Vision, pages 13- 26, 2012.   Reading Notes
  • M. J. Zaki, W. Meira Jr. Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, 2014.   Reading Notes
  • J. A. Lee, M. Verleysen. Nonlinear dimensionality reduction. Springer , New York; London, 2007.   Reading Notes
  • J. Gama. Knowledge Discovery from Data Streams. Chapman & Hall/CRC, 2010.   Reading Notes
  • C. C. Aggarwal. Data Mining: The Textbook. Springer International Publishing, 2015.   Reading Notes
  • D. Arthur, S. Vassilvitskii. k-means++: The Advantages of Careful Seeding. Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, 2007.   Reading Notes
  • C. Elkan. Using the Triangle Inequality to Accelerate k-Means. International Conference on Machine Learning, 2003.   Reading Notes
  • R. Socher, B. Huval, B. P. Bath, C. D. Manning, A. Y. Ng. Convolutional-Recursive Deep Learning for 3D Object Classification. NIPS, 2012.   Reading Notes
  • M. Engelcke, D. Rao, D. Zeng Wang, C. H. Tong, I. Posner. Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. CoRR, 2016.   Reading Notes
  • Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, O. Ronneberger. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. MICCAI, 2016.   Reading Notes
  • J. Huang, S. You. Point cloud labeling using 3d convolutional neural network. ICPR, 2016.   Reading Notes
  • A. Brock, T. Lim, J. M. Ritchie, N. Weston. Generative and Discriminative Voxel Modeling with Convolutional Neural Networks. CoRR, 2016.   Reading Notes
  • S. Song, J. Xiao. Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images. CVPR, 2016.   Reading Notes
  • Nima Sedaghat Alvar, Mohammadreza Zolfaghari, Thomas Brox. Orientation-boosted Voxel Nets for 3D Object Recognition. CoRR, abs/1604.03351, 2016.   Reading Notes
  • Diederik P. Kingma, Max Welling. Auto-Encoding Variational Bayes- CoRR, abs/1312.6114.   Reading Notes
  • Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Ole Winther. Autoencoding beyond pixels using a learned similarity metric. ICML, 2016.   Reading Notes
  • Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, Joshua B. Tenenbaum. Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. CoRR, 2016.   Reading Notes
  • Hang Su, Subhransu Maji, Evangelos Kalogerakis, Erik G. Learned-Miller. Multi-view Convolutional Neural Networks for 3D Shape Recognition. CoRR, 2015.   Reading Notes
  • Ben Graham. Sparse 3D convolutional neural networks. BMVC, 2015.   Reading Notes
  • Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, J. Xiao. 3D ShapeNets: A Deep Representation for Volumetric Shape Modeling. CVPR, 2015.   Reading Notes
  • Yangyan Li, Sören Pirk, Hao Su, Charles Ruizhongtai Qi, Leonidas J. Guibas. FPNN: Field Probing Neural Networks for 3D Data. CoRR, abs/1605.06240.   Reading Notes
  • Charles Ruizhongtai Qi. Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, Leonidas Guibas. Volumetric and Multi-View CNNs for Object Classification on 3D Data. CVPR, 2016.   Reading Notes
  • Q. Dou, H. Chen, L. Yu, L. Zhao, J. Qin, D. Wang, V. C. T. Mok, L. Shi, P.-A. Heng. Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks. IEEE Trans. Med. Imaging, 2016.   Reading Notes
  • H. Chen, Q. Dou, L. Yu, P.-A. Heng. VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation. CoRR, 2016.   Reading Notes
  • B. Li, T. Zhang, T. Xia. Vehicle Detection from 3D Lidar Using Fully Convolutional Network. Robotics: Science and Systems, 2016.   Reading Notes
  • Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu,David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio. Generative Adversarial Networks. CoRR, abs/1406.2661.   Reading Notes
  • Daniel Maturana, Sebastian Scherer. VoxNet: A 3D Convolutional Neural Network for real-time object recognition. IROS, 2015.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 20, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 19, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 18, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 17, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 16, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 15, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 11, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 10, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 9, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 8, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 7, MIT Press, 2016.   Reading Notes
  • P. Márquez-Neila, P. Kohli, C. Rother, L. Baumela. Non-parametric Higher-Order Random Fields for Image Segmentation. ECCV, 2014.   Reading Notes
  • M. Menze, A. Geiger. Object scene flow for autonomous vehicles. Conference on Computer Vision and Pattern Recognition, 2015.   Reading Notes
  • L. Sevilla-Lara, D. Sun, V. Jampani, M. J. Black. Optical Flow with Semantic Segmentation and Localized Layers. Computing Research Repository, abs/1603.03911, 2016.   Reading Notes
  • M. Kiefel, V. Jampani, P. V. Gehler. Permutohedral Lattice CNNs. Computing Research Repository, abs/1412.6618, 2014.   Reading Notes
  • A. Babenko, A. Slesarev, A. Chigorin, V. S. Lempitsky. Neural codes for image retrieval. In Computer Vision, European Conference on, volume 8689 of Lecture Notes in Computer Science, pages 584–599, Zurich, Switzerland, September 2014. Springer.   Reading Notes
  • K. Leyton-Brown, Y. Shoham. Essentials of Game Theory: A Concise, Multidisciplinary Introduction. Morgan & Claypool Publishers, 2008.   Reading Notes
  • Diederik P. Kingma, Max Welling. Auto-Encoding Variational Bayes- CoRR, abs/1312.6114.   Reading Notes
  • Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Ole Winther. Autoencoding beyond pixels using a learned similarity metric. ICML, 2016.   Reading Notes
  • Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu,David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio. Generative Adversarial Networks. CoRR, abs/1406.2661.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 20, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 19, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 18, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 17, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 16, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 15, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 11, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 10, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 9, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 8, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 7, MIT Press, 2016.   Reading Notes
  • J. Mairal, F. Bach, J. Ponce. Sparse Modeling for Image and Vision Processing. Foundations and Trends in Computer Graphics and Vision, colume 8, 2014.   Reading Notes
  • M. Kiefel, V. Jampani, P. V. Gehler. Permutohedral Lattice CNNs. Computing Research Repository, abs/1412.6618, 2014.   Reading Notes
  • S. J. Russell, P. Norvig. Artifical Intelligence: A Modern Approach. Pearson Education, 2nd Edition, 2003.   Reading Notes
  • J. Demsar. Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research, volume 7, 2006.   Reading Notes
  • M. J. Zaki, W. Meira Jr. Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, 2014.   Reading Notes
  • S Boyd, L. Vandenberghe. Convex Optimization. Cambridge University Press, New York, 2004.   Reading Notes
  • J. A. Lee, M. Verleysen. Nonlinear dimensionality reduction. Springer , New York; London, 2007.   Reading Notes
  • J. Gama. Knowledge Discovery from Data Streams. Chapman & Hall/CRC, 2010.   Reading Notes
  • C. C. Aggarwal. Data Mining: The Textbook. Springer International Publishing, 2015.   Reading Notes
  • D. Arthur, S. Vassilvitskii. k-means++: The Advantages of Careful Seeding. Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, 2007.   Reading Notes
  • Y. Ganin, V. S. Lempitsky. N^4-Fields: Neural Network Nearest Neighbor Fields for Image Transforms. Computing Research Repository, 2014.   Reading Notes
  • C. Elkan. Using the Triangle Inequality to Accelerate k-Means. International Conference on Machine Learning, 2003.   Reading Notes
  • G. Louppe, L. Wehenkel, A. Sutera, P. Geurts. Understanding Variable Importances in Forests of Randomized Trees. Advances in Neural Information Processing Systems, 2013.   Reading Notes
  • C. E. Rasmussen, C. K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.   Reading Notes
  • A. Criminisi, J. Shotton. Density Forests. In Decision Forests for Computer Vision and Medical Image Analysis, Springer London, 2013.   Reading Notes
  • A. Saffari, C. Leistner, J. Santner, M. Godec, H. Bischof. On-line Random Forests. International Conference on Computer Vision Workshops, 2009.   Reading Notes
  • C. J. C. Burges. Some Notes on Applied Mathematics for Machine Learning. Advanced Lectures on Machine Learning, 2004.   Reading Notes
  • A. Blum. On-Line Algorithms in Machine Learning. Proceedings of the Workshop on On-Line Algorithms, 1996.   Reading Notes
  • S. Thrun, W. Burgard, D. Fox. Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). MIT Press, Cambridge, 2005.   Reading Notes
  • N. C. Oza. Online Bagging and Boosting. International Conference on Systems, Man and Cybernetics, 2005.   Reading Notes
  • S. Gupta, P. Arbelaez, J. Malik. Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images. Conference on Computer Vision and Pattern Recognition, 2013.   Reading Notes
  • S. R. Bulò, P. Kontschieder. Neural Decision Forests for Semantic Image Labelling. Conference on Computer Vision and Pattern Recognition, 2014.   Reading Notes
  • G. Kutyniok. Theory and Applications of Compressed Sensing. Computing Research Repository, abs/1203.3815, 2012.   Reading Notes
  • J. Shen. Gamma-Convergence Approximation to Piecewise Constant Mumford-Shah Segmentation. International Conference on Advanced Concepts for Intelligent Vision Systems, 2005.   Reading Notes
  • P. L. Combettes and J.-C. Pesquet.Proximal Splitting Methods in Signal Processing. Fixed-Point Algorithms for Inverse Problems in Science and Engineering, Springer, 2011.   Reading Notes
  • P. Ochs, Y. Chen,T. Brox, T. Pock. iPiano: Inertial Proximal Algorithm for Non-Convex Optimization. Computing Research Repository, abs/1404.4805, 2014.   Reading Notes
  • S Boyd, L. Vandenberghe. Convex Optimization. Cambridge University Press, New York, 2004.   Reading Notes
  • C. E. Rasmussen, C. K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.   Reading Notes
  • C. J. C. Burges. Some Notes on Applied Mathematics for Machine Learning. Advanced Lectures on Machine Learning, 2004.   Reading Notes
  • Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, O. Ronneberger. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. MICCAI, 2016.   Reading Notes
  • Q. Dou, H. Chen, L. Yu, L. Zhao, J. Qin, D. Wang, V. C. T. Mok, L. Shi, P.-A. Heng. Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks. IEEE Trans. Med. Imaging, 2016.   Reading Notes
  • H. Chen, Q. Dou, L. Yu, P.-A. Heng. VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation. CoRR, 2016.   Reading Notes
  • R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Süsstrunk. SLIC Superpixels Compared to State-Of-The-Art Superpixel Methods. Transactions on Pattern Analysis and Machine Intelligence, pages 2274 – 2282, 2012.   Reading Notes
  • M. F. Porter. An Algorithm for Suffix Stripping. Readings in Information Retrieval, 1997.   Reading Notes
  • P. L. Combettes and J.-C. Pesquet.Proximal Splitting Methods in Signal Processing. Fixed-Point Algorithms for Inverse Problems in Science and Engineering, Springer, 2011.   Reading Notes
  • P. Ochs, Y. Chen,T. Brox, T. Pock. iPiano: Inertial Proximal Algorithm for Non-Convex Optimization. Computing Research Repository, abs/1404.4805, 2014.   Reading Notes
  • Daniel Maturana, Sebastian Scherer. 3D Convolutional Neural Networks for Landing Zone Detection from LiDAR. ICRA, 2015.   Reading Notes
  • B. Li, T. Zhang, T. Xia. Vehicle Detection from 3D Lidar Using Fully Convolutional Network. Robotics: Science and Systems, 2016.   Reading Notes
  • A. Geiger, P. Lenz, R. Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark. Conference on Computer Vision and Pattern Recognition, 2012.   Reading Notes
  • S. Thrun, W. Burgard, D. Fox. Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). MIT Press, Cambridge, 2005.   Reading Notes
  • D. Easley, J.Kleinberg. Networks, Crowds and Markets: Reasoning About a Highly Connected World. Cambridge University Press, 2010.   Reading Notes
  • M. Fowler. Inversion of Control Containers and the Dependency Injection Pattern. 2004.   Reading Notes
  • J. Yip. It's Not Just Standing Up: Patterns for Daily Standup Meetings. 2006.   Reading Notes