{"total":204,"rows":[{"id":"2311.03658","title":"The Linear Representation Hypothesis and the Geometry of Large Language Models","created":"2023-11-07","cited_by_count":716,"area":null},{"id":"2405.07987","title":"The Platonic Representation Hypothesis","created":"2024-05-13","cited_by_count":382,"area":null},{"id":"2106.04619","title":"Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style","created":"2021-06-08","cited_by_count":312,"area":null},{"id":"2107.10098","title":"Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA","created":"2021-07-21","cited_by_count":160,"area":null},{"id":"2202.05441","title":"Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs","created":"2022-02-11","cited_by_count":144,"area":null},{"id":"2209.11924","title":"Interventional Causal Representation Learning","created":"2022-09-24","cited_by_count":142,"area":null},{"id":"2202.03169","title":"CITRIS: Causal Identifiability from Temporal Intervened Sequences","created":"2022-02-07","cited_by_count":133,"area":null},{"id":"2204.00673","title":"Learnable latent embeddings for joint behavioral and neural analysis","created":"2022-04-01","cited_by_count":117,"area":null},{"id":"2106.11230","title":"Can contrastive learning avoid shortcut solutions?","created":"2021-06-21","cited_by_count":114,"area":null},{"id":"2306.00542","title":"Nonparametric Identifiability of Causal Representations from Unknown Interventions","created":"2023-06-01","cited_by_count":106,"area":null},{"id":"2202.14037","title":"Understanding Contrastive Learning Requires Incorporating Inductive Biases","created":"2022-02-28","cited_by_count":96,"area":null},{"id":"2306.02235","title":"Learning Linear Causal Representations from Interventions under General Nonlinear Mixing","created":"2023-06-04","cited_by_count":92,"area":null},{"id":"2301.06262","title":"Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges","created":"2023-01-16","cited_by_count":87,"area":null},{"id":"2211.16467","title":"Linear Causal Disentanglement via Interventions","created":"2022-11-29","cited_by_count":84,"area":null},{"id":"2307.06250","title":"Identifiability Guarantees for Causal Disentanglement from Soft Interventions","created":"2023-07-12","cited_by_count":84,"area":null},{"id":"2403.03867","title":"On the Origins of Linear Representations in Large Language Models","created":"2024-03-06","cited_by_count":79,"area":null},{"id":"2106.08320","title":"Self-Supervised Learning with Kernel Dependence Maximization","created":"2021-06-15","cited_by_count":77,"area":null},{"id":"2311.04056","title":"Multi-View Causal Representation Learning with Partial Observability","created":"2023-11-07","cited_by_count":74,"area":null},{"id":"2304.09355","title":"To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review","created":"2023-04-19","cited_by_count":72,"area":null},{"id":"2111.03042","title":"Unsupervised Learning of Compositional Energy Concepts","created":"2021-11-04","cited_by_count":71,"area":null},{"id":"2206.10044","title":"Identifiability of deep generative models without auxiliary information","created":"2022-06-20","cited_by_count":70,"area":null},{"id":"2206.01101","title":"Weakly Supervised Representation Learning with Sparse Perturbations","created":"2022-06-02","cited_by_count":65,"area":null},{"id":"2307.05596","title":"Compositional Generalization from First Principles","created":"2023-07-10","cited_by_count":64,"area":null},{"id":"2303.09166","title":"Identifiability Results for Multimodal Contrastive Learning","created":"2023-03-16","cited_by_count":61,"area":null},{"id":"2302.02672","title":"Identifiability of latent-variable and structural-equation models: from linear to nonlinear","created":"2023-02-06","cited_by_count":58,"area":null},{"id":"2206.06169","title":"Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems","created":"2022-06-13","cited_by_count":51,"area":null},{"id":"2303.16535","title":"Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning","created":"2023-03-29","cited_by_count":48,"area":null},{"id":"2108.12043","title":"Learning Disentangled Representations in the Imaging Domain","created":"2021-08-26","cited_by_count":46,"area":null},{"id":"2305.14229","title":"Provably Learning Object-Centric Representations","created":"2023-05-23","cited_by_count":45,"area":null},{"id":"2201.10000","title":"Neural Manifold Clustering and Embedding","created":"2022-01-24","cited_by_count":44,"area":null},{"id":"2302.10283","title":"Self-supervised learning of Split Invariant Equivariant representations","created":"2023-02-14","cited_by_count":44,"area":null},{"id":"2102.12982","title":"A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives","created":"2021-02-25","cited_by_count":41,"area":null},{"id":"2204.07596","title":"Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning","created":"2022-04-15","cited_by_count":40,"area":null},{"id":"2407.00143","title":"InfoNCE: Identifying the Gap Between Theory and Practice","created":"2024-06-28","cited_by_count":40,"area":null},{"id":"2205.01789","title":"Do More Negative Samples Necessarily Hurt in Contrastive Learning?","created":"2022-05-03","cited_by_count":39,"area":null},{"id":"2106.07115","title":"Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective","created":"2021-06-14","cited_by_count":37,"area":null},{"id":"2211.16327","title":"On the Power of Foundation Models","created":"2022-11-29","cited_by_count":37,"area":null},{"id":"2302.07920","title":"InfoNCE Loss Provably Learns Cluster-Preserving Representations","created":"2023-02-15","cited_by_count":37,"area":null},{"id":"2306.11078","title":"Beyond Normal: On the Evaluation of Mutual Information Estimators","created":"2023-06-19","cited_by_count":37,"area":null},{"id":"2503.21073","title":"Shared Global and Local Geometry of Language Model Embeddings","created":"2025-03-27","cited_by_count":36,"area":null},{"id":"2306.02899","title":"Learning nonparametric latent causal graphs with unknown interventions","created":"2023-06-05","cited_by_count":34,"area":null},{"id":"2203.08547","title":"Non-isotropy Regularization for Proxy-based Deep Metric Learning","created":"2022-03-16","cited_by_count":33,"area":null},{"id":"2310.15709","title":"Causal Representation Learning Made Identifiable by Grouping of Observational Variables","created":"2023-10-24","cited_by_count":32,"area":null},{"id":"2402.15017","title":"Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning","created":"2024-02-22","cited_by_count":31,"area":null},{"id":"2409.02772","title":"Unifying Causal Representation Learning with the Invariance Principle","created":"2024-09-04","cited_by_count":31,"area":null},{"id":"2410.21869","title":"Cross-Entropy Is All You Need To Invert the Data Generating Process","created":"2024-10-29","cited_by_count":31,"area":null},{"id":"2106.15610","title":"An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild","created":"2021-06-29","cited_by_count":30,"area":null},{"id":"2110.15796","title":"Properties from Mechanisms: An Equivariance Perspective on Identifiable Representation Learning","created":"2021-10-29","cited_by_count":30,"area":null},{"id":"2302.02865","title":"Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs","created":"2023-02-06","cited_by_count":29,"area":null},{"id":"2402.09236","title":"Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models","created":"2024-02-14","cited_by_count":29,"area":null},{"id":"2604.21691","title":"There Will Be a Scientific Theory of Deep Learning","created":"2026-04-23","cited_by_count":29,"area":null},{"id":"2205.14814","title":"Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding","created":"2022-05-30","cited_by_count":28,"area":null},{"id":"2206.02416","title":"Embrace the Gap: VAEs Perform Independent Mechanism Analysis","created":"2022-06-06","cited_by_count":28,"area":null},{"id":"2303.00106","title":"The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning","created":"2023-02-28","cited_by_count":27,"area":null},{"id":"2207.02598","title":"Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning","created":"2022-07-06","cited_by_count":26,"area":null},{"id":"2111.03220","title":"Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices","created":"2021-11-05","cited_by_count":25,"area":null},{"id":"2303.00633","title":"An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization","created":"2023-03-01","cited_by_count":25,"area":null},{"id":"2203.15702","title":"Contrasting the landscape of contrastive and non-contrastive learning","created":"2022-03-29","cited_by_count":24,"area":null},{"id":"2405.13888","title":"Marrying Causal Representation Learning with Dynamical Systems for Science","created":"2024-05-22","cited_by_count":23,"area":null},{"id":"2301.05169","title":"Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning","created":"2023-01-12","cited_by_count":22,"area":null},{"id":"2310.11011","title":"From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling","created":"2023-10-17","cited_by_count":22,"area":null},{"id":"2305.17326","title":"Matrix Information Theory for Self-Supervised Learning","created":"2023-05-27","cited_by_count":21,"area":null},{"id":"2404.07900","title":"High-Dimension Human Value Representation in Large Language Models","created":"2024-04-11","cited_by_count":20,"area":null},{"id":"2411.07784","title":"Interaction Asymmetry: A General Principle for Learning Composable Abstractions","created":"2024-11-12","cited_by_count":20,"area":null},{"id":"2207.03784","title":"A Non-isotropic Probabilistic Take on Proxy-based Deep Metric Learning","created":"2022-07-08","cited_by_count":19,"area":null},{"id":"2210.00638","title":"What shapes the loss landscape of self-supervised learning?","created":"2022-10-02","cited_by_count":19,"area":null},{"id":"2306.04160","title":"Rethinking Weak Supervision in Helping Contrastive Learning","created":"2023-06-07","cited_by_count":18,"area":null},{"id":"2311.08815","title":"Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations","created":"2023-11-15","cited_by_count":17,"area":null},{"id":"2406.14302","title":"Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning","created":"2024-06-20","cited_by_count":16,"area":null},{"id":"2503.01824","title":"From superposition to sparse codes: interpretable representations in neural networks","created":"2025-03-03","cited_by_count":16,"area":null},{"id":"2602.24012","title":"InfoNCE Induces Gaussian Distribution","created":"2026-02-27","cited_by_count":16,"area":null},{"id":"2210.05972","title":"Unsupervised Learning of Equivariant Structure from Sequences","created":"2022-10-12","cited_by_count":15,"area":null},{"id":"2312.00379","title":"Optimal Sample Complexity of Contrastive Learning","created":"2023-12-01","cited_by_count":15,"area":null},{"id":"2203.11284","title":"A Contrastive Objective for Learning Disentangled Representations","created":"2022-03-21","cited_by_count":14,"area":null},{"id":"2204.04606","title":"Towards efficient representation identification in supervised learning","created":"2022-04-10","cited_by_count":14,"area":null},{"id":"2308.01578","title":"Unsupervised Representation Learning for Time Series: A Review","created":"2023-08-03","cited_by_count":14,"area":null},{"id":"2202.04933","title":"Energy-Based Contrastive Learning of Visual Representations","created":"2022-02-10","cited_by_count":13,"area":null},{"id":"2307.10907","title":"The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning","created":"2023-07-20","cited_by_count":13,"area":null},{"id":"2311.04774","title":"Towards a Unified Framework of Contrastive Learning for Disentangled Representations","created":"2023-11-08","cited_by_count":13,"area":null},{"id":"2411.06518","title":"Causal Representation Learning from Multimodal Biomedical Observations","created":"2024-11-10","cited_by_count":13,"area":null},{"id":"2206.04756","title":"An Empirical Study on Disentanglement of Negative-free Contrastive Learning","created":"2022-06-09","cited_by_count":12,"area":null},{"id":"2406.13371","title":"Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment","created":"2024-06-19","cited_by_count":12,"area":null},{"id":"2107.02495","title":"InfoNCE is variational inference in a recognition parameterised model","created":"2021-07-06","cited_by_count":10,"area":null},{"id":"2302.09795","title":"Simple Disentanglement of Style and Content in Visual Representations","created":"2023-02-20","cited_by_count":10,"area":null},{"id":"2311.04837","title":"Identifying Semantic Component for Robust Molecular Property Prediction","created":"2023-11-08","cited_by_count":10,"area":null},{"id":"2402.06223","title":"Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning","created":"2024-02-09","cited_by_count":10,"area":null},{"id":"2410.03006","title":"Formation of Representations in Neural Networks","created":"2024-10-03","cited_by_count":10,"area":null},{"id":"2504.10143","title":"On the Value of Cross-Modal Misalignment in Multimodal Representation Learning","created":"2025-04-14","cited_by_count":10,"area":null},{"id":"2304.01762","title":"Incorporating Unlabelled Data into Bayesian Neural Networks","created":"2023-04-04","cited_by_count":9,"area":null},{"id":"2311.18048","title":"An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis","created":"2023-11-29","cited_by_count":9,"area":null},{"id":"2406.07141","title":"Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention","created":"2024-06-11","cited_by_count":9,"area":null},{"id":"2410.14673","title":"Self-supervised contrastive learning performs non-linear system identification","created":"2024-10-18","cited_by_count":9,"area":null},{"id":"2502.09252","title":"On the Importance of Embedding Norms in Self-Supervised Learning","created":"2025-02-13","cited_by_count":9,"area":null},{"id":"2504.13101","title":"Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research","created":"2025-04-17","cited_by_count":9,"area":null},{"id":"2210.05248","title":"Self-supervised debiasing using low rank regularization","created":"2022-10-11","cited_by_count":8,"area":null},{"id":"2312.05771","title":"Hacking Task Confounder in Meta-Learning","created":"2023-12-10","cited_by_count":8,"area":null},{"id":"2406.10743","title":"Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations?","created":"2024-06-15","cited_by_count":8,"area":null},{"id":"2206.06593","title":"On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis","created":"2022-06-14","cited_by_count":7,"area":null},{"id":"2208.02810","title":"Analyzing Data-Centric Properties for Graph Contrastive Learning","created":"2022-08-04","cited_by_count":7,"area":null},{"id":"2403.14973","title":"Pose-Aware Self-Supervised Learning with Viewpoint Trajectory Regularization","created":"2024-03-22","cited_by_count":7,"area":null}],"next_cursor":"100"}