{"list":"other","total":114,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1312.6114","title":"Auto-Encoding Variational Bayes","created":"2013-12-20","cited_by_count":4053},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"1611.01144","title":"Categorical Reparameterization with Gumbel-Softmax","created":"2016-11-03","cited_by_count":2739},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1907.02893","title":"Invariant Risk Minimization","created":"2019-07-05","cited_by_count":1854},{"id":"1601.00670","title":"Variational Inference: A Review for Statisticians","created":"2016-01-04","cited_by_count":1472},{"id":"1806.01261","title":"Relational inductive biases, deep learning, and graph networks","created":"2018-06-04","cited_by_count":1360},{"id":"1611.00712","title":"The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables","created":"2016-11-02","cited_by_count":1148},{"id":"1811.12359","title":"Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations","created":"2018-11-29","cited_by_count":941},{"id":"1803.01422","title":"DAGs with NO TEARS: Continuous Optimization for Structure Learning","created":"2018-03-04","cited_by_count":752},{"id":"1606.04934","title":"Improving Variational Inference with Inverse Autoregressive Flow","created":"2016-06-15","cited_by_count":640},{"id":"1910.01442","title":"CLEVRER: CoLlision Events for Video REpresentation and Reasoning","created":"2019-10-03","cited_by_count":484},{"id":"1911.02508","title":"Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods","created":"2019-11-06","cited_by_count":443},{"id":"1705.08821","title":"Causal Effect Inference with Deep Latent-Variable Models","created":"2017-05-24","cited_by_count":410},{"id":"1901.11390","title":"MONet: Unsupervised Scene Decomposition and Representation","created":"2019-01-22","cited_by_count":392},{"id":"1904.12584","title":"The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision","created":"2019-04-26","cited_by_count":372},{"id":"1904.10098","title":"DAG-GNN: DAG Structure Learning with Graph Neural Networks","created":"2019-04-22","cited_by_count":331},{"id":"1401.0118","title":"Black Box Variational Inference","created":"2013-12-31","cited_by_count":319},{"id":"1502.03509","title":"MADE: Masked Autoencoder for Distribution Estimation","created":"2015-02-12","cited_by_count":307},{"id":"1905.11979","title":"Causal Confusion in Imitation Learning","created":"2019-05-28","cited_by_count":288},{"id":"2011.15091","title":"Inductive Biases for Deep Learning of Higher-Level Cognition","created":"2020-11-30","cited_by_count":275},{"id":"1812.05239","title":"Improving fairness in machine learning systems: What do industry practitioners need?","created":"2018-12-13","cited_by_count":262},{"id":"2012.05876","title":"Neurosymbolic AI: The 3rd Wave","created":"2020-12-10","cited_by_count":255},{"id":"1911.10500","title":"Causality for Machine Learning","created":"2019-11-24","cited_by_count":243},{"id":"1711.00848","title":"Variational Inference of Disentangled Latent Concepts from Unlabeled Observations","created":"2017-11-02","cited_by_count":241},{"id":"2012.05208","title":"On the Binding Problem in Artificial Neural Networks","created":"2020-12-09","cited_by_count":233},{"id":"1906.02226","title":"Gradient-Based Neural DAG Learning","created":"2019-06-05","cited_by_count":227},{"id":"1907.13052","title":"GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations","created":"2019-07-30","cited_by_count":227},{"id":"1909.10893","title":"Recurrent Independent Mechanisms","created":"2019-09-24","cited_by_count":219},{"id":"1909.13189","title":"Learning Sparse Nonparametric DAGs","created":"2019-09-29","cited_by_count":207},{"id":"2002.02770","title":"A Survey on Causal Inference","created":"2020-02-05","cited_by_count":202},{"id":"2002.00498","title":"DYNOTEARS: Structure Learning from Time-Series Data","created":"2020-02-02","cited_by_count":200},{"id":"1912.08142","title":"Causality matters in medical imaging","created":"2019-12-17","cited_by_count":193},{"id":"2006.10201","title":"On the Role of Sparsity and DAG Constraints for Learning Linear DAGs","created":"2020-06-17","cited_by_count":191},{"id":"1302.6815","title":"Learning Bayesian Networks: The Combination of Knowledge and Statistical Data","created":"2013-02-27","cited_by_count":187},{"id":"1310.1533","title":"CAM: Causal additive models, high-dimensional order search and penalized regression","created":"2013-10-06","cited_by_count":178},{"id":"1302.4983","title":"Causal Inference in the Presence of Latent Variables and Selection Bias","created":"2013-02-20","cited_by_count":166},{"id":"1903.01672","title":"Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes","created":"2019-03-05","cited_by_count":166},{"id":"1901.10912","title":"A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms","created":"2019-01-30","cited_by_count":158},{"id":"1906.04477","title":"Causal Discovery with Reinforcement Learning","created":"2019-06-11","cited_by_count":145},{"id":"2010.02863","title":"Directional Graph Networks","created":"2020-10-06","cited_by_count":133},{"id":"1910.01075","title":"Learning Neural Causal Models from Unknown Interventions","created":"2019-10-02","cited_by_count":113},{"id":"1803.10459","title":"Graphite: Iterative Generative Modeling of Graphs","created":"2018-03-28","cited_by_count":112},{"id":"2010.04296","title":"CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning","created":"2020-10-08","cited_by_count":109},{"id":"1709.08568","title":"The Consciousness Prior","created":"2017-09-25","cited_by_count":107},{"id":"1805.06826","title":"The Blessings of Multiple Causes","created":"2018-05-17","cited_by_count":104},{"id":"1906.03292","title":"On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset","created":"2019-06-07","cited_by_count":100},{"id":"2006.10833","title":"Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data","created":"2020-06-18","cited_by_count":100},{"id":"2011.02268","title":"Causal Autoregressive Flows","created":"2020-11-04","cited_by_count":99},{"id":"2011.04216","title":"DoWhy: An End-to-End Library for Causal Inference","created":"2020-11-09","cited_by_count":95},{"id":"1910.08527","title":"Masked Gradient-Based Causal Structure Learning","created":"2019-10-18","cited_by_count":89},{"id":"1706.09141","title":"Causal Structure Learning","created":"2017-06-28","cited_by_count":84},{"id":"2007.00631","title":"Causal Discovery in Physical Systems from Videos","created":"2020-07-01","cited_by_count":77},{"id":"1802.06310","title":"Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions","created":"2018-02-17","cited_by_count":76},{"id":"2010.09133","title":"DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks","created":"2020-10-18","cited_by_count":66},{"id":"1302.6808","title":"Learning Gaussian Networks","created":"2013-02-27","cited_by_count":62},{"id":"2010.12152","title":"Generative Neurosymbolic Machines","created":"2020-10-23","cited_by_count":57},{"id":"2010.06978","title":"Differentiable Causal Discovery Under Unmeasured Confounding","created":"2020-10-14","cited_by_count":56},{"id":"1911.07420","title":"A Graph Autoencoder Approach to Causal Structure Learning","created":"2019-11-18","cited_by_count":55},{"id":"2010.03110","title":"Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning","created":"2020-10-07","cited_by_count":53},{"id":"2006.12373","title":"Learning Physical Graph Representations from Visual Scenes","created":"2020-06-22","cited_by_count":50},{"id":"1903.02278","title":"Causal Discovery Toolbox: Uncover causal relationships in Python","created":"2019-03-06","cited_by_count":49},{"id":"1403.2150","title":"Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets","created":"2014-03-10","cited_by_count":48},{"id":"1803.04929","title":"Structural Agnostic Modeling: Adversarial Learning of Causal Graphs","created":"2018-03-13","cited_by_count":48},{"id":"1507.02608","title":"High-dimensional consistency in score-based and hybrid structure learning","created":"2015-07-09","cited_by_count":46},{"id":"1502.02454","title":"A fast PC algorithm for high dimensional causal discovery with multi-core PCs","created":"2015-02-09","cited_by_count":45},{"id":"1709.06138","title":"Model-Powered Conditional Independence Test","created":"2017-09-18","cited_by_count":43},{"id":"1408.2063","title":"From Ordinary Differential Equations to Structural Causal Models: the deterministic case","created":"2014-08-09","cited_by_count":41},{"id":"2009.13180","title":"CASTLE: Regularization via Auxiliary Causal Graph Discovery","created":"2020-09-28","cited_by_count":41},{"id":"1911.00081","title":"Scaling structural learning with NO-BEARS to infer causal transcriptome networks","created":"2019-10-31","cited_by_count":39},{"id":"1506.02494","title":"backShift: Learning causal cyclic graphs from unknown shift interventions","created":"2015-06-08","cited_by_count":37},{"id":"1907.04068","title":"Conditional Independence Testing using Generative Adversarial Networks","created":"2019-07-09","cited_by_count":36},{"id":"1807.03024","title":"Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders","created":"2018-07-09","cited_by_count":35},{"id":"1302.3599","title":"A Discovery Algorithm for Directed Cyclic Graphs","created":"2013-02-13","cited_by_count":34},{"id":"2001.06208","title":"Causal models for dynamical systems","created":"2020-01-17","cited_by_count":34},{"id":"1412.3773","title":"Distinguishing cause from effect using observational data: methods and benchmarks","created":"2014-12-11","cited_by_count":33},{"id":"2007.01272","title":"RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces","created":"2020-07-02","cited_by_count":32},{"id":"1910.01751","title":"Causal Induction from Visual Observations for Goal Directed Tasks","created":"2019-10-03","cited_by_count":31},{"id":"1906.10437","title":"Learning Causal State Representations of Partially Observable Environments","created":"2019-06-25","cited_by_count":30},{"id":"1401.0852","title":"Concave Penalized Estimation of Sparse Gaussian Bayesian Networks","created":"2014-01-04","cited_by_count":28},{"id":"2006.16789","title":"Causality Learning: A New Perspective for Interpretable Machine Learning","created":"2020-06-27","cited_by_count":28},{"id":"2001.05343","title":"Causal Discovery from Incomplete Data: A Deep Learning Approach","created":"2020-01-15","cited_by_count":26},{"id":"2002.02836","title":"Causally Correct Partial Models for Reinforcement Learning","created":"2020-02-07","cited_by_count":24},{"id":"1804.04622","title":"Causal Inference via Kernel Deviance Measures","created":"2018-04-12","cited_by_count":22},{"id":"2006.02615","title":"Double Generative Adversarial Networks for Conditional Independence Testing","created":"2020-06-03","cited_by_count":22},{"id":"1406.7648","title":"Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package","created":"2014-06-30","cited_by_count":19},{"id":"1806.07161","title":"Identifying Causal Effects with the R Package causaleffect","created":"2018-06-19","cited_by_count":19},{"id":"1309.6836","title":"Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure","created":"2013-09-26","cited_by_count":18},{"id":"2002.09573","title":"Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values","created":"2020-02-21","cited_by_count":18},{"id":"1805.02087","title":"A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias","created":"2018-05-05","cited_by_count":16},{"id":"1902.01073","title":"Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach","created":"2019-02-04","cited_by_count":16},{"id":"2001.01894","title":"Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method","created":"2020-01-07","cited_by_count":16},{"id":"1306.1043","title":"Structural Intervention Distance (SID) for Evaluating Causal Graphs","created":"2013-06-05","cited_by_count":14},{"id":"1701.05654","title":"Bayesian Network Learning via Topological Order","created":"2017-01-20","cited_by_count":14},{"id":"2008.09301","title":"Amortized learning of neural causal representations","created":"2020-08-21","cited_by_count":14},{"id":"2002.11576","title":"NestedVAE: Isolating Common Factors via Weak Supervision","created":"2020-02-26","cited_by_count":13},{"id":"2008.11376","title":"Causal Adversarial Network for Learning Conditional and Interventional Distributions","created":"2020-08-26","cited_by_count":13},{"id":"2012.03540","title":"Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications","created":"2020-12-07","cited_by_count":11}],"next_cursor":"100"}