{"list":"other","total":74,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1505.07818","title":"Domain-Adversarial Training of Neural Networks","created":"2015-05-28","cited_by_count":3044},{"id":"1611.01144","title":"Categorical Reparameterization with Gumbel-Softmax","created":"2016-11-03","cited_by_count":2739},{"id":"1409.7495","title":"Unsupervised Domain Adaptation by Backpropagation","created":"2014-09-26","cited_by_count":1917},{"id":"1812.01754","title":"Moment Matching for Multi-Source Domain Adaptation","created":"2018-12-04","cited_by_count":1331},{"id":"1502.02791","title":"Learning Transferable Features with Deep Adaptation Networks","created":"2015-02-10","cited_by_count":1329},{"id":"1706.07522","title":"Deep Hashing Network for Unsupervised Domain Adaptation","created":"2017-06-22","cited_by_count":1188},{"id":"1710.03077","title":"Deeper, Broader and Artier Domain Generalization","created":"2017-10-09","cited_by_count":986},{"id":"1811.12359","title":"Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations","created":"2018-11-29","cited_by_count":941},{"id":"1712.02560","title":"Maximum Classifier Discrepancy for Unsupervised Domain Adaptation","created":"2017-12-07","cited_by_count":683},{"id":"1605.06636","title":"Deep Transfer Learning with Joint Adaptation Networks","created":"2016-05-21","cited_by_count":612},{"id":"1412.3474","title":"Deep Domain Confusion: Maximizing for Domain Invariance","created":"2014-12-10","cited_by_count":600},{"id":"1907.04809","title":"Variational Autoencoders and Nonlinear ICA: A Unifying Framework","created":"2019-07-10","cited_by_count":503},{"id":"1802.03916","title":"Detecting and Correcting for Label Shift with Black Box Predictors","created":"2018-02-12","cited_by_count":366},{"id":"1812.02849","title":"A Survey of Unsupervised Deep Domain Adaptation","created":"2018-12-06","cited_by_count":361},{"id":"1608.06019","title":"Domain Separation Networks","created":"2016-08-22","cited_by_count":320},{"id":"2010.03978","title":"A Brief Review of Domain Adaptation","created":"2020-10-07","cited_by_count":288},{"id":"1805.08651","title":"Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning","created":"2018-05-22","cited_by_count":282},{"id":"1605.06336","title":"Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA","created":"2016-05-20","cited_by_count":273},{"id":"1802.08735","title":"A DIRT-T Approach to Unsupervised Domain Adaptation","created":"2018-02-23","cited_by_count":215},{"id":"2102.11107","title":"Towards Causal Representation Learning","created":"2021-02-22","cited_by_count":209},{"id":"1811.08585","title":"Progressive Feature Alignment for Unsupervised Domain Adaptation","created":"2018-11-21","cited_by_count":183},{"id":"1903.01672","title":"Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes","created":"2019-03-05","cited_by_count":166},{"id":"2107.10098","title":"Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA","created":"2021-07-21","cited_by_count":160},{"id":"2209.11924","title":"Interventional Causal Representation Learning","created":"2022-09-24","cited_by_count":142},{"id":"2203.16437","title":"Weakly supervised causal representation learning","created":"2022-03-30","cited_by_count":140},{"id":"2202.03169","title":"CITRIS: Causal Identifiability from Temporal Intervened Sequences","created":"2022-02-07","cited_by_count":133},{"id":"1707.06422","title":"Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions","created":"2017-07-20","cited_by_count":126},{"id":"2003.04475","title":"Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift","created":"2020-03-10","cited_by_count":125},{"id":"2003.07554","title":"A Unified View of Label Shift Estimation","created":"2020-03-17","cited_by_count":119},{"id":"1803.00830","title":"Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift","created":"2018-03-02","cited_by_count":109},{"id":"2306.00542","title":"Nonparametric Identifiability of Causal Representations from Unknown Interventions","created":"2023-06-01","cited_by_count":106},{"id":"2110.05428","title":"Learning Temporally Causal Latent Processes from General Temporal Data","created":"2021-10-11","cited_by_count":98},{"id":"2106.05200","title":"Independent mechanism analysis, a new concept?","created":"2021-06-09","cited_by_count":97},{"id":"2206.07751","title":"On the Identifiability of Nonlinear ICA: Sparsity and Beyond","created":"2022-06-15","cited_by_count":85},{"id":"2211.16467","title":"Linear Causal Disentanglement via Interventions","created":"2022-11-29","cited_by_count":84},{"id":"2307.06250","title":"Identifiability Guarantees for Causal Disentanglement from Soft Interventions","created":"2023-07-12","cited_by_count":84},{"id":"2109.03795","title":"Desiderata for Representation Learning: A Causal Perspective","created":"2021-09-08","cited_by_count":76},{"id":"2311.04056","title":"Multi-View Causal Representation Learning with Partial Observability","created":"2023-11-07","cited_by_count":74},{"id":"2010.02637","title":"Weakly Supervised Disentangled Generative Causal Representation Learning","created":"2020-10-06","cited_by_count":68},{"id":"2402.05052","title":"Causal Representation Learning from Multiple Distributions: A General Setting","created":"2024-02-07","cited_by_count":68},{"id":"2210.13647","title":"Temporally Disentangled Representation Learning","created":"2022-10-24","cited_by_count":61},{"id":"2305.17225","title":"Causal Component Analysis","created":"2023-05-26","cited_by_count":56},{"id":"2007.01261","title":"Curriculum Manager for Source Selection in Multi-Source Domain Adaptation","created":"2020-07-02","cited_by_count":55},{"id":"2012.11807","title":"Learning Disentangled Semantic Representation for Domain Adaptation","created":"2020-12-22","cited_by_count":55},{"id":"2106.15563","title":"Learning latent causal graphs via mixture oracles","created":"2021-06-29","cited_by_count":55},{"id":"2007.08801","title":"Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation","created":"2020-07-17","cited_by_count":53},{"id":"2208.06406","title":"Function Classes for Identifiable Nonlinear Independent Component Analysis","created":"2022-08-12","cited_by_count":53},{"id":"2206.06169","title":"Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems","created":"2022-06-13","cited_by_count":51},{"id":"2210.01798","title":"Latent Hierarchical Causal Structure Discovery with Rank Constraints","created":"2022-10-01","cited_by_count":51},{"id":"2301.08230","title":"Score-based Causal Representation Learning with Interventions","created":"2023-01-19","cited_by_count":49},{"id":"2303.16535","title":"Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning","created":"2023-03-29","cited_by_count":48},{"id":"2002.03497","title":"Few-shot Domain Adaptation by Causal Mechanism Transfer","created":"2020-02-10","cited_by_count":47},{"id":"1805.01386","title":"Boosting Domain Adaptation by Discovering Latent Domains","created":"2018-05-03","cited_by_count":46},{"id":"1808.09347","title":"Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation","created":"2018-08-28","cited_by_count":45},{"id":"2401.04890","title":"Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies","created":"2024-01-10","cited_by_count":41},{"id":"2310.04723","title":"Subspace Identification for Multi-Source Domain Adaptation","created":"2023-10-07","cited_by_count":37},{"id":"2002.03278","title":"Domain Adaptation as a Problem of Inference on Graphical Models","created":"2020-02-09","cited_by_count":36},{"id":"2403.08335","title":"A Sparsity Principle for Partially Observable Causal Representation Learning","created":"2024-03-13","cited_by_count":35},{"id":"2306.02899","title":"Learning nonparametric latent causal graphs with unknown interventions","created":"2023-06-05","cited_by_count":34},{"id":"2312.11001","title":"A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables","created":"2023-12-18","cited_by_count":33},{"id":"2010.15764","title":"Domain adaptation under structural causal models","created":"2020-10-29","cited_by_count":28},{"id":"2402.00849","title":"Score-based Causal Representation Learning: Linear and General Transformations","created":"2024-02-01","cited_by_count":27},{"id":"2105.04051","title":"Aggregating From Multiple Target-Shifted Sources","created":"2021-05-09","cited_by_count":24},{"id":"2107.14447","title":"T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation","created":"2021-07-30","cited_by_count":24},{"id":"2201.05666","title":"Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions","created":"2022-01-14","cited_by_count":19},{"id":"2311.02695","title":"Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions","created":"2023-11-05","cited_by_count":18},{"id":"2104.01568","title":"Information-theoretic regularization for Multi-source Domain Adaptation","created":"2021-04-04","cited_by_count":15},{"id":"2311.12267","title":"Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity","created":"2023-11-21","cited_by_count":15},{"id":"2406.05937","title":"Linear Causal Representation Learning from Unknown Multi-node Interventions","created":"2024-06-09","cited_by_count":14},{"id":"2207.13179","title":"Unsupervised Learning under Latent Label Shift","created":"2022-07-26","cited_by_count":9},{"id":"2305.11379","title":"Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks","created":"2023-05-19","cited_by_count":7},{"id":"2307.05704","title":"A Causal Ordering Prior for Unsupervised Representation Learning","created":"2023-07-11","cited_by_count":3},{"id":"2303.05734","title":"Generative Model Based Noise Robust Training for Unsupervised Domain Adaptation","created":"2023-03-10","cited_by_count":1}],"next_cursor":null}