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Ole Winther | Bernhard Schölkopf | Andrea Dittadi | Samuele Papa | Michele De Vita | Francesco Locatello | B. Schölkopf | Francesco Locatello | O. Winther | Samuele Papa | Andrea Dittadi
[1] Elizabeth S. Spelke,et al. Principles of Object Perception , 1990, Cogn. Sci..
[2] Brian Cantwell Smith,et al. On the origin of objects , 1997, Trends in Cognitive Sciences.
[3] Harold W. Kuhn,et al. The Hungarian method for the assignment problem , 1955, 50 Years of Integer Programming.
[4] Charless C. Fowlkes,et al. Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] Edward Vul,et al. Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference , 2011, Science.
[6] Nicolas Le Roux,et al. Learning a Generative Model of Images by Factoring Appearance and Shape , 2011, Neural Computation.
[7] Bernhard Schölkopf,et al. On causal and anticausal learning , 2012, ICML.
[8] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[10] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[11] Alex Graves,et al. Recurrent Models of Visual Attention , 2014, NIPS.
[12] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.
[13] M. Cugmas,et al. On comparing partitions , 2015 .
[14] Leon A. Gatys,et al. A Neural Algorithm of Artistic Style , 2015, ArXiv.
[15] Geoffrey E. Hinton,et al. Attend, Infer, Repeat: Fast Scene Understanding with Generative Models , 2016, NIPS.
[16] Harri Valpola,et al. Tagger: Deep Unsupervised Perceptual Grouping , 2016, NIPS.
[17] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[18] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[19] Li Fei-Fei,et al. CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Jürgen Schmidhuber,et al. Neural Expectation Maximization , 2017, NIPS.
[21] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[22] Andriy Mnih,et al. Disentangling by Factorising , 2018, ICML.
[23] Christopher P. Burgess,et al. The Multi-Entity Variational Autoencoder , 2018 .
[24] Jürgen Schmidhuber,et al. Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions , 2018, ICLR.
[25] Roger B. Grosse,et al. Isolating Sources of Disentanglement in Variational Autoencoders , 2018, NeurIPS.
[26] Yee Whye Teh,et al. Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects , 2018, NeurIPS.
[27] Michael C. Mozer,et al. Learning Deep Disentangled Embeddings with the F-Statistic Loss , 2018, NeurIPS.
[28] Christopher K. I. Williams,et al. A Framework for the Quantitative Evaluation of Disentangled Representations , 2018, ICLR.
[29] E. J. Green,et al. A Theory of Perceptual Objects , 2018, Philosophy and Phenomenological Research.
[30] Koray Kavukcuoglu,et al. Neural scene representation and rendering , 2018, Science.
[31] Andrea Vedaldi,et al. ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking , 2018, ECCV.
[32] Gunnar Rätsch,et al. Competitive Training of Mixtures of Independent Deep Generative Models , 2018 .
[33] Ludovic Denoyer,et al. Unsupervised Object Segmentation by Redrawing , 2019, NeurIPS.
[34] Wojciech M. Czarnecki,et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning , 2019, Nature.
[35] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[36] Sjoerd van Steenkiste,et al. Are Disentangled Representations Helpful for Abstract Visual Reasoning? , 2019, NeurIPS.
[37] Ole Winther,et al. LAVAE: Disentangling Location and Appearance , 2019, ArXiv.
[38] Kristian Kersting,et al. Faster Attend-Infer-Repeat with Tractable Probabilistic Models , 2019, ICML.
[39] Klaus Greff,et al. Multi-Object Representation Learning with Iterative Variational Inference , 2019, ICML.
[40] Bernhard Schölkopf,et al. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations , 2018, ICML.
[41] Stefan Bauer,et al. On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset , 2019, NeurIPS.
[42] Dana H. Brooks,et al. Structured Disentangled Representations , 2018, AISTATS.
[43] Jakub W. Pachocki,et al. Dota 2 with Large Scale Deep Reinforcement Learning , 2019, ArXiv.
[44] Stefan Bauer,et al. On the Fairness of Disentangled Representations , 2019, NeurIPS.
[45] Joelle Pineau,et al. Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks , 2019, AAAI.
[46] Alexander Lerchner,et al. Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs , 2019, ArXiv.
[47] Bin Li,et al. Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation , 2019, ICML.
[48] K. Kersting,et al. Structured Object-Aware Physics Prediction for Video Modeling and Planning , 2019, ICLR.
[49] A. P. Sarath Chandar,et al. Slot Contrastive Networks: A Contrastive Approach for Representing Objects , 2020, ArXiv.
[50] Elise van der Pol,et al. Contrastive Learning of Structured World Models , 2019, ICLR.
[51] Ingmar Posner,et al. Reconstruction Bottlenecks in Object-Centric Generative Models , 2020, ArXiv.
[52] Austin R. Benson,et al. Better Set Representations For Relational Reasoning , 2020, NeurIPS.
[53] Thomas Kipf,et al. Object-Centric Learning with Slot Attention , 2020, NeurIPS.
[54] Sungjin Ahn,et al. SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition , 2020, ICLR.
[55] Peter V. Gehler,et al. Towards causal generative scene models via competition of experts , 2020, ArXiv.
[56] Sjoerd van Steenkiste,et al. Investigating object compositionality in Generative Adversarial Networks , 2018, Neural Networks.
[57] Sungjin Ahn,et al. Improving Generative Imagination in Object-Centric World Models , 2020, ICML.
[58] Chuang Gan,et al. CLEVRER: CoLlision Events for Video REpresentation and Reasoning , 2020, ICLR.
[59] Ingmar Posner,et al. GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations , 2019, ICLR.
[60] Chang Chen,et al. Learning to Infer 3D Object Models from Images , 2020, ArXiv.
[61] Jure Leskovec,et al. Learning to Simulate Complex Physics with Graph Networks , 2020, ICML.
[62] Joelle Pineau,et al. Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking , 2019, AAAI.
[63] Alexey Dosovitskiy,et al. Learning Object-Centric Video Models by Contrasting Sets , 2020, ArXiv.
[64] Klaus Greff,et al. On the Binding Problem in Artificial Neural Networks , 2020, ArXiv.
[65] Stefano Soatto,et al. Learning to Manipulate Individual Objects in an Image , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[66] Alexander S. Ecker,et al. Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences , 2020, ArXiv.
[67] SCALOR: Generative World Models with Scalable Object Representations , 2019, ICLR.
[68] Francesco Locatello,et al. A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation , 2020, J. Mach. Learn. Res..
[69] B. Schölkopf,et al. On Disentangled Representations Learned from Correlated Data , 2020, ICML.
[70] Michael Tschannen,et al. Representation learning from videos in-the-wild: An object-centric approach , 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).
[71] B. Schölkopf,et al. On the Transfer of Disentangled Representations in Realistic Settings , 2020, ICLR.
[72] Ingmar Posner,et al. GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement , 2021, NeurIPS.
[73] Sergey Levine,et al. Recurrent Independent Mechanisms , 2019, ICLR.
[74] Yoshua Bengio,et al. Towards Causal Representation Learning , 2021, ArXiv.
[75] Fei Deng,et al. Generative Scene Graph Networks , 2021, ICLR.
[76] John Essington,et al. How we learn: why brains learn better than any machine … for now , 2021, Educational Review.
[77] Gaurav Malhotra,et al. The role of Disentanglement in Generalisation , 2021, ICLR.