Publications associated with this project.
[1] Achille, A., and Soatto, S. (2018). Emergence of invariance and disentanglement in deep representations. The Journal of Machine Learning Research, 19(1), 1947-1980.
[2] Achille, A., and Soatto, S. (2018). Information dropout: Learning optimal representations through noisy computation. IEEE transactions on pattern analysis and machine intelligence, 40(12), 2897-2905.
[3] A. Achille, G. Paolini, S. Soatto. "Where is the information in a deep neural network?." arXiv preprint arXiv:1905.12213 (2019).
[4] Anand, K., Bianconi, G., and Severini, S. (2011). Shannon and von Neumann entropy of random networks with heterogeneous expected degree. Physical Review E, 83(3), 036109.
[5] Shervin R. Arashloo, Josef Kittler: Robust one-class kernel spectral regression. IEEE Trans Neural Networks and Learning Systems 2020 (to appear).
[6] Y Balaji, R Chellappa, S Feizi. Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation. arXiv preprint arXiv:2010.05862
[7] Bardenet, R., Doucet, A., and Holmes, C. (2017). On Markov chain Monte Carlo methods for tall data. The Journal of Machine Learning Research, 18(1), 1515-1557.
[8] A. Barp, S. Takao, M. Betancourt, A. Arnaudon, M. Girolami: A Unifying Description of Measure-Preserving Diffusions on Manifolds using a Novel Bracket Geometry. Submitted to NeurIPS 2020.
[9] A. Barp, M. Betancourt, M. Girolami: The Bracket Geometry of Measure-Preserving Flows, and the Geometric Conspiracy of HMC. In preparation (2020a).
[10] A. Barp, M. Betancourt, M. Girolami: The Bracket Geometry of Statistics. Uncovering the Foundations of Langevin MCMC, Measure-Preserving Systems, and Stein Operators. In preparation (2020b).
[11] A. Barp, M. Betancourt, R. Lelievre, T. McLachlan, T. Kennedy, M. Girolami: Monte Carlo Sampling and Optimisation on Manifolds via Hamiltonian Dynamics. In preparation (2020).
[12] Alessandro Barp, Francois-Xavier Briol, Andrew Duncan, Mark Girolami, and Lester Mackey. “Minimum stein discrepancy estimators”. In Advances in Neural Information Processing Systems, pages 12964–12976, 2019.
[13] Bernton, E., Heng, J., Doucet, A. and Jacob, P.E. (2019). Schrödinger Bridge Samplers. arXiv preprint arXiv:1912.13170.
[14] Beskos, A., Girolami, M., Lan, S., Farrell, P. E., and Stuart, A. M. (2017). Geometric MCMC for infinite-dimensional inverse problems. Journal of Computational Physics, 335, 327-351.
[15] F Biggs, B Guedj: Differentiable PAC-Bayes Objectives with PartiallyAggregated Neural Networks, arXiv:2006.12228 (2020).
[16] Bober-Irizar, M., Husain, S., Ong, E. J., and Bober, M. (2017). Cultivating DNN diversity for large scale video labelling. arXiv preprint arXiv:1707.04272.
[17] Alexandre Bouchard-Côté, Sebastian J Vollmer, and Arnaud Doucet. “The bouncy particle sampler: A nonreversible rejection-free Markov chain Monte Carlo method”. Journal of the American Statistical Association, 113(522):855–867, 2018.
[18] F-X. Briol, A. Barp, A. B. Duncan, and M. Girolami. “Statistical inference for generative models with maximum mean discrepancy”. arXiv:1906.05944, 2019.
[19] S. Bulathwela, S. Kreitmayer and M. Perez-Ortiz, "What's in it for me?: Augmenting recommended learning resources with navigable annotations", 2020 Conference on Intelligent User Interfaces.
[20] S. Bulathwela, M. Perez-Ortiz, A. Lipani, E. Yilmaz and J. Shawe-Taylor, "Predicting entanglement in videolectures", under review at the 2020 Educational Data Mining Conference.
[21] S. Bulathwela, M. Perez-Ortiz, R. Mehrotra, D. Orlic, C. de la Higuera, J. Shawe-Taylor and E. Yilmaz, "SUM'20: State-based User Modelling", workshop organised at the 2020 Conference on Web Search and Data Mining.
[22] S. Bulathwela, M. Perez-Ortiz, R. Mehrotra, D. Orlic, C. de la Higuera, J. Shawe-Taylor and E. Yilmaz, "Report on the WSDM 2020 Workshop on State-based User Modelling (SUM'20)", SIGIR reports, 2020.
[23] S. Bulathwela, M. Perez-Ortiz, E. Yilmaz and J. Shawe-Taylor, "TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources", 2020 AAAI Conference on Artificial Intelligence.
[24] S. Bulathwela, M. Perez-Ortiz, E. Yilmaz and J. Shawe-Taylor, "Towards an Integrative Educational Recommender for Lifelong Learners", 2020 AAAI Conference on Artificial Intelligence.
[25] Cabello, A., Severini, S., and Winter, A. (2014). Graph-theoretic approach to quantum correlations. Physical review letters, 112(4), 040401.
[26] Aditya Chattopadhyay, Benjamin David Haeffele, Donald Geman, René Vidal: Quantifying Task Complexity Through Generalized Information Measures. ICLR 2021 (submitted).
[27] W. Y. Chen, A. Barp, F-X. Briol, J. Gorham, M. Girolami, L. Mackey, and C. J. Oates. “Stein point Markov chain Monte Carlo”. In International Conference on Machine Learning, PMLR 97, pages 1011–1021, 2019.
[28] Cheng, X., Chatterji, N. S., Bartlett, P. L., and Jordan, M. I. (2018, July). Underdamped Langevin MCMC: A non-asymptotic analysis. In Conference on Learning Theory (pp. 300-323). PMLR.
[29] Chrétien, S.; and Guedj, B., Revisiting clustering as matrix factorisation on the Stiefel manifold. 2019. Submitted.
[30] Cicek, Safa, Soatto, S. “Unsupervised domain adaptation via regularized conditional alignment”, CVPR 2019, pp. 1416-1425.
[31] Cicek, S, Nakhaei, Alireza, Fujimura, Kikuo, Soatto, S. “MARL-PPS: Multi-agent reinforcement learning with periodic parameter sharing”, AAMAS’19, Proceedings of the 18th International Conference on Autonomous Agents and MultiAgents Systems, 2019, pp. 1883-1885.
[32] Cicek, S, Soatto, S. “Input and weight space smoothing for semi-supervised learning”, CVPR2019, 10 pages.
[33] C. Ciliberto, M. Herbster, A. D. Ialongo, M. Pontil, A. Rocchetto, S. Severini, L. Wossnig, "Quantum machine learning: a classical perspective", Proc. R. Soc. A 474.2209 (2018), arXiv:1707.08561 [quant-ph]
[34] Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, and Guy Rom, (2019) Online k-means Clustering, https://arxiv.org/abs/1909.06861
[35] Rob Cornish, Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, and Arnaud Doucet. “Scalable Metropolis-Hastings for exact Bayesian inference with large datasets”. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 1351–1360, Long Beach, California, USA, 09–15 Jun 2019. PMLR.
[36] Dairyko, M., Hogben, L., Lin, J. C. H., Lockhart, J., Roberson, D., Severini, S., and Young, M. (2017). Note on von Neumann and Rényi entropies of a graph. Linear Algebra and its Applications, 521, 240-253.
[37] Pierre Del Moral, Arnaud Doucet, and Ajay Jasra. “Sequential Monte Carlo samplers. Journal of the Royal Statistical Society”. Series B (Statistical Methodology), 68(3):411–436, 2006.
[38] George Deligiannidis, Daniel Paulin, Alexandre Bouchard-Côté, and Arnaud Doucet. “Randomized Hamiltonian Monte Carlo as scaling limit of the bouncy particle sampler and dimension-free convergence rates”. arXiv preprint arXiv:1808.04299, 2018.
[39] Deligiannidis, G., Doucet, A. and Rubenthaler, S. (2020). Ensemble Rejection Sampling. arXiv preprint arXiv:2001.09188
[40] Shay Deutsch, Iacopo Masi, Stefano Soatto. “Finding structure in point cloud data with the robust isoperimetric loss”, SSVM 2019, pp. 25-37
[41] Dong, J., and Soatto, S. (2015). Domain-size pooling in local descriptors: DSP-SIFT. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5097-5106).
[42] Simon S. Du, Wei Hu, Sham M. Kakade, Jason D. Lee, Qi Lei. “Few-Shot Learning via Learning the Representation, Provably”. arXiv preprint arXiv:2002.09434 (2020).
[43] Zhen-Hua Feng, Josef Kittler, Muhammad Awais, Patrik Huber, Xiaojun Wu: Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks. CVPR 2018
[44] Z-H.Feng, X-J.Wu and J.Kittler: Mining hard augmented examples for robust facial landmark localisation, IEEE Signal Proc. Letters 26(3): 450-454, 2019
[45] Zhen-Hua Feng, Josef Kittler, Muhammad Awais, Xiaojun Wu:. “Rectified Wing Loss for Efficient and Robust Facial Landmark Localisation with Convolutional Neural Networks”. International Journal of Computer Vision pp. 1-20, 2020.
[46] Ge, R., Lee, J. D., and Ma, T. (2016). Matrix completion has no spurious local minimum. In Advances in Neural Information Processing Systems (pp. 2973-2981).
[47] Guilherme França, Michael I Jordan, René Vidal. “On Dissipative Symplectic Integration with Applications to Gradient-Based Optimization”. arXiv preprint arXiv:2004.06840 (2020)
[48] Guilherme França, Daniel P Robinson, René Vidal. “Gradient flows and accelerated proximal splitting methods”. arXiv preprint arXiv:1908.00865.
[49] Guilherme França, Jeremias Sulam, Daniel P Robinson, René Vidal. “Conformal symplectic and relativistic optimization”. arXiv preprint arXiv:1903.04100.
[50] Guilherme França, Daniel P Robinson, René Vidal. “Relax, and accelerate: A continuous perspective on admm”. arXiv preprint arXiv:1808.04048.
[51] Guilherme França, Daniel P Robinson, René Vidal. “ADMM and Accelerated ADMM as Continuous Dynamical Systems”. International Conference on Machine Learning 2018.
[52] Guilherme França, Daniel P Robinson, René Vidal. “A Dynamical Systems Perspective on Nonsmooth Constrained Optimization”. arXiv preprint arXiv:1808.04048.
[53] Ryan Giordano, Tamara Broderick, Michael I. Jordan: Covariances, robustness, and variational Bayes. Journal of Machine Learning Research, 19, 1−49. (2019)
[54] A Golatkar, A Achille, S Soatto: Eternal sunshine of the spotless net: Selective forgetting in deep networks, CVPR 2020, pp. 9304-9312
[55] A Golatkar, A Achille, S Soatto: Forgetting Outside the Box:Scrubbing Deep Networks of InformationAccessible from Input-Output Observations, arXiv:2003.02960 (2020).
[56] Gagnon, P. and Doucet, A. (2019) Non-reversible Jump Algorithms for Bayesian Nested Model Selection. arXiv preprint arXiv:1911.01340.
[57] Guedj, B., A Primer on PAC-Bayesian Learning. 2019. Published in the proceedings of the second congress of the French Mathematical Society
[58] Benjamin Guedj, and Bhargav Srinivasa Desikan (2019) Kernel-Based Ensemble Learning in Python, https://arxiv.org/abs/1912.08311
[59] Benjamin Guedj, and Louis Pujol (2019) Still no free lunches: the price to pay for tighter PAC- Bayes bounds, https://arxiv.org/abs/1910.04460
[60] Guedj, B.; and Rengot, J., Non-linear aggregation of filters to improve image denoising. 2020. Accepted at Computing Conference.
[61] Guedj, B.; and Robbiano, S., PAC-Bayesian high dimensional bipartite ranking, Journal of Statistical Planning and Inference, 196: 70 - 86. 2018.
[62] Haeffele, B. D., and Vidal, R. (2017). Global optimality in neural network training. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7331-7339).
[63] Haeffele, B. D., and Vidal, R. (2019). Structured low-rank matrix factorization: Global optimality, algorithms, and applications. IEEE transactions on pattern analysis and machine intelligence, 42(6), 1468-1482.
[64] He, Tong, Huang, Haibin, Yi, Li, Zhou, Yuqian, Wu, Chihao, Wang, Jue, Soatto, Stefano. “GeoNet: Deep Geodesic Networks for Point Cloud Analysis”, CVPR 2019, pp. 6888-6897.
[65] He, Tong, Soatto, S. “Mono3D++, Monocular 3D vehicle detection with two-scale 3D hypotheses and task priors”, The 33rd AAAI Conference on Artificial Intelligence (AAAI-19), Vol. 33, No. 1, pp. 8409-8416. https://doi.org/10.1609/aaai.v33i01.33018409
[66] Jeremy Heng, Adrian N Bishop, George Deligiannidis, and Arnaud Doucet. “Controlled sequential Monte Carlo”. Annals of Statistics, to appear, 2020.
[67] Jeremy Heng, Arnaud Doucet, and Yvo Pokern. “Gibbs flow for approximate transport with applications to Bayesian computation”. arXiv preprint arXiv:1909.08787, 2019.
[68] Zengxi Huang, Zhen-Hua Feng, Fei Yan, Josef Kittler, Xiao-Jun Wu: Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset. AMDO 2018: 63-72
[69] Husain, S. S., and Bober, M. (2016). Improving large-scale image retrieval through robust aggregation of local descriptors. IEEE transactions on pattern analysis and machine intelligence, 39(9), 1783-1796.
[70] S. S. Hussain, E-J Ong, M. Bober: ACTNET: end-to-end learning of feature activations and multi-stream aggregation for effective instance image retrieval. CoRR abs/1907.05794 (2019b)
[71] S. S. Hussain and M. Bober, REMAP: Multi-layer entropy-guided pooling of dense CNN features for image retrieval, IEEE Trans. Image Proc., May 2019, 28(10), 5201-5213.
[72] Naoya Inoue, Pontus Stenetorp and Kentaro Inui, R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reason, to appear in Proceedings of the ACL 2020.
[73] Jahangiri, E., Yoruk, E., Vidal, R., Younes, L., and Geman, D. (2017). Information pursuit: A bayesian framework for sequential scene parsing. arXiv preprint arXiv:1701.02343.
[74] Varun Kanade, Andrea Rocchetto, Simone Severini, "Learning DNFs under product distributions via μ-biased quantum Fourier sampling." CoRR abs/1802.05690 (2018)
[75] I. Kavalerov, W. Czaja, R. Chellappa: A Multi-Class Hinge Loss for Conditional GANs, Winter Conference on Computer Vision, 2021.
[76] I. Kavalerov, W. Czaja, R. Chellappa: Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images, arxiv.org/abs/1906.06804 (2020).
[77] Josef Kittler, Cemre Zor: Delta divergence: A novel decision cognizant measure of classifier incongruence. IEEE Transactions on Cybernetics, 49(6), 2331-2343, 2019Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, and J. Regier. (2020). “Decision-making with auto-encoding variational Bayes”. arXiv preprint arXiv:2002.07217 (2020).
[78] Josef Kittler, Cemre Zor: Delta divergence: A novel decision cognizant measure of classifier incongruence. IEEE Transactions on Cybernetics, 49(6), 2331-2343, 2019
[79] J. Kittler, C. Zor, I Kaloskampis, Y. Hicks and W-W. Wang, Error sensitivity analysis of Delta divergence, a novel measure for classifier incongruence detection, Pattern Recognition, 77:30- 44, 2018.
[80] M. Kristan et al., The sixth visual object tracking challenge results, ECCV2018 Workshops 1: 3-53, 2018
[81] Lee, J. D., Simchowitz, M., Jordan, M. I., and Recht, B. (2016, June). Gradient descent only converges to minimizers. In Conference on learning theory (pp. 1246-1257).
[82] Letarte, G.; Germain, P.; Guedj, B.; and Laviolette, F., Dichotomize and Generalize: PAC- Bayesian Binary Activated Deep Neural Networks. Advances in Neural Information Processing Systems 32:, NeurIPS 2019, pages 6869-6879, 2019.
[83] Hui Li, Xioaojun Wu and Josef Kittler, MDLatLRR: A novel decomposition method for infrared and visible image fusion, IEEE Trans Image Proc. 29:4733-4746, 2020
[84] D. Liu, M. Bober and J Kittler: Visual Semantic Information Pursuit: A Survey, IEEE Trans. PAMI (to appear 2020).
[85] Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett and Michael I. Jordan. “Is there an analog of Nesterov acceleration for MCMC?” arXiv preprint arXiv:1902.00996 (2019).
[86] Maddison, Chris J and Paulin, Daniel and Teh, Yee Whye and Doucet, Arnaud. Dual space preconditioning for gradient descent, arXiv preprint arXiv:1902.02257, 2019.
[87] Chris J Maddison, John Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, and Yee Teh. “Filtering variational objectives”. In Advances in Neural Information Processing Systems, pages 6576–6586, 2017.
[88] Marchand, M., Su, H., Morvant, E., Rousu, J., and Shawe-Taylor, J. S. (2014). Multilabel structured output learning with random spanning trees of max-margin markov networks. In Advances in Neural Information Processing Systems (pp. 873-881).
[89] L Middleton, G Deligiannidis, A Doucet, and PE Jacob. “Unbiased smoothing using particle independent Metropolis–Hastings”. In AISTATS, 2019. 5
[90] Lawrence Middleton, George Deligiannidis, Arnaud Doucet, and Pierre E Jacob. “Unbiased Markov chain Monte Carlo for intractable target distributions”. Electronic Journal of Statistics, to appear, 2020.
[91] Mhammedi, Z.; GruÃànwald, P.; and Guedj, B., PAC-Bayes Un-Expected Bernstein Inequality. Advances in Neural Information Processing Systems 32: NeurIPS 2019, pages 12180-12191.
[92] Kento Nozawa, Pascal Germain, and Benjamin Guedj (2019) PAC-Bayesian Contrastive Unsupervised Representation Learning, https://arxiv.org/abs/1910.04464
[93] Eng-Jon Ong, Sameed Husain, Mikel Bober-Irizar, Miroslaw Bober: Deep Architectures and Ensembles for Semantic Video Classification. IEEE Trans Circ. Syst. Video Techn. 29(12), 3568-3582, 2019.
[94] Ambar Pal, Connor Lane, René Vidal, Benjamin Haeffele. On the Regularization Properties of Structured Dropout. IEEE Conference on Computer Vision and Pattern Recognition, 2020.
[95] Parrado-Hernández, E., Ambroladze, A., Shawe-Taylor, J., and Sun, S. (2012). PAC-Bayes bounds with data dependent priors. The Journal of Machine Learning Research, 13(1), 3507-3531.
[96] Ponti, M., Kittler, J., Riva, M., de Campos, T., and Zor, C. (2017). A decision cognizant Kullback–Leibler divergence. Pattern Recognition, 61, 470-478.
[97] Shervin Rahimzadeh Arashloo, Josef Kittler. “Robust one-class kernel spectral regression”. IEEE Trans Neural Networks and Learning Systems 2020 (to appear).
[98] Oates, C. J., Girolami, M., and Chopin, N. (2017). Control functionals for Monte Carlo integration. Series B Statistical methodology.
[99] Omar Rivasplata, Emilio Parrado-Hernandez, John Shawe-Taylor, Shiliang Sun, and Csaba Szepesvari. Pac-bayes bounds for stable algorithms with instance-dependent priors. In Proceedings of the Neural Information Processing Systems Conference, 2018.
[100] O. Rivasplata, V. Tankasali, C. Szepesvari, M. Perez-Ortiz and J. Shawe-Taylor, "PAC-Bayes with Backprop: Tighter risk certificates for neural networks", under review at the 2020 International Conference on Machine Learning, 2020.
[101] A. Rocchetto, E. Grant, S. Strelchuk, G. Carleo, S. Severini, "Learning hard quantum distributions with variational autoencoders", Quantum Information 4, 28 (2018), arXiv:170.00725 [quant-ph]
[102] Y Romano, M Sesia, EJ Candès. “Classification with Valid and Adaptive Coverage”. arXiv preprint arXiv:2006.02544 (2020).
[103] C.J. Simon-Gabriel, A. Barp, L. Mackey: Metrizing Weak Convergence with Maximum Mean Discrepancies. Submitted to NeurIPS 2020.
[104] Simon-Gabriel, C., Mackey, L., Barp, A. (2020). Targeted Convergence Characteristics of Maximum Mean Discrepancies and Kernel Stein Discrepancies. In preparation
[105] G. Singh and J. Shawe-Taylor, "Faster Convergence and Generalization in Deep Neural Networks", 2018, arXiv:1807.11414 [cs.LG]
[106] Soatto, S., and Chiuso, A. (2016). International Conference on Learning Representations.
[107] South, L. F., Karvonen, T., Nemeth, C., Girolami, M., and Oates, C. (2020). Semi-Exact Control Functionals From Sard's Method. arXiv preprint arXiv:2002.00033.
[108] Su, W., Boyd, S., and Candes, E. J. (2016). A differential equation for modeling Nesterov's accelerated gradient method: theory and insights. The Journal of Machine Learning Research, 17(1), 5312-5354.
[109] Shiliang Sun, Mengran Yu, John Shawe-Taylor, Stability based PAC-Bayes Analysis for Multi- view Learning Algorithms, submitted.
[110] S. Syed, A. Bouchard-Côté, G. Deligiannidis and Doucet, A. (2019) Non-reversible Parallel Tempering: A Scalable Highly Parallel MCMC Scheme. arXiv preprint arXiv:1905.02939.
[111] Vono M., Paulin, D. and Doucet, A. (2019). Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting. arXiv preprint arXiv:1905.11937.
[112] Jun Wan, Sergio Escalera, Francisco J. Perales López, Josef Kittler: Articulated motion and deformable objects. Pattern Recognition 79: 55-64 (2018)
[113] George Wynne, Francois-Xavier Briol, and Mark Girolami. “Convergence guarantees for gaussian process approximations under several observation models”. arXiv preprint arXiv:2001.10818, 2020.
[114] Tianyang Xu, Zhenhua Feng, Xiaojun Wu and Josef Kittler, An accelerated correlation filter tracker, Pattern Recogn. 102:107172(2020)
[115] Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu, Josef Kittler: Learning Adaptive Discriminative Correlation Filters via Temporal Consistency preserving Spatial Feature Selection for Robust Visual Tracking. IEEE Trans Image Processing, 28(11), 5596-5609, 2019
[116] Tianyang Xu, Zhenhua Feng, Xiaojun Wu and Josef Kittler: Complementary discriminative correlation filter based on collaborative representation for visual object tracking. IEEE Trans. Circuits and Systems for Video Technology, 2020 (accepted)
[117] Wibisono, A., Wilson, A. C., and Jordan, M. I. (2016). A variational perspective on accelerated methods in optimization. proceedings of the National Academy of Sciences, 113(47), E7351-E7358.
[118] Wong, Alex, Soatto, S. “Bilateral cyclic constraints and adaptive regularization for Unsupervised monocular depth prediction”, CVPR 2019, pp. 5644-5653
[119] Fei Yan, Krystian Mikolajczyk, Josef Kittler: Person Re-Identification with Vision and Language. Proceedings ICPR 2018.
[120] Yanchao Yang, Dong Lao, Ganesh Sundaramoorthi, Stefano Soatto. “Phase consistent ecological domain adaptation”, CVPR 2020, pp. 9011-9020
[121] Yanchao Yang, Stefano Soatto. “FDA: Fourier domain adaptation for semantic segmentation”, CVPR 2020, pp. 4085-4095
[122] Yanchao Yang, Antonio Loquercio, Davide Scaramuzza, Stefano Soatto. “Unsupervised moving object detection via contextual information separation”, CVPR 2019, pp. 879-888.
[123] Yanchao Yang, Alex Wong, Stefano Soatto. “Dense depth posterior (DDP) from single image and sparse range”, CVPR 2019, pp. 3353-3362.
[124] Feng Zhenhua, Kittler Josef, Awais Muhammad, Wu Xiao-Jun: Rectified Wing Loss for Efficient and Robust Facial Landmark Localisation with Convolutional Neural Networks. (2020) International Journal of Computer Vision pp. 1-20.
[125] C Zor, M Awais, J Kittler, M Bober, S Husain, Q Kong, C Kroos: Divergence Based Weighting for Information Channels in Deep Convolutional Neural Networks for Bird Audio Detection. ICASSP 2019: 3052-3056