{"list":"other","total":48,"rows":[{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"id":"2207.12598","title":"Classifier-Free Diffusion Guidance","created":"2022-07-26","cited_by_count":5847},{"id":"2206.00364","title":"Elucidating the Design Space of Diffusion-Based Generative Models","created":"2022-06-01","cited_by_count":3045},{"id":"2205.09991","title":"Planning with Diffusion for Flexible Behavior Synthesis","created":"2022-05-20","cited_by_count":1061},{"id":"2305.13301","title":"Training Diffusion Models with Reinforcement Learning","created":"2023-05-22","cited_by_count":982},{"id":"2002.00444","title":"Deep Reinforcement Learning for Autonomous Driving: A Survey","created":"2020-02-02","cited_by_count":721},{"id":"2208.06193","title":"Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning","created":"2022-08-12","cited_by_count":562},{"id":"1705.05065","title":"AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles","created":"2017-05-15","cited_by_count":523},{"id":"2211.15657","title":"Is Conditional Generative Modeling all you need for Decision-Making?","created":"2022-11-28","cited_by_count":516},{"id":"2302.07121","title":"Universal Guidance for Diffusion Models","created":"2023-02-14","cited_by_count":437},{"id":"2303.09295","title":"DIRE for Diffusion-Generated Image Detection","created":"2023-03-16","cited_by_count":390},{"id":"1702.02284","title":"Adversarial Attacks on Neural Network Policies","created":"2017-02-08","cited_by_count":320},{"id":"2405.12399","title":"Diffusion for World Modeling: Visual Details Matter in Atari","created":"2024-05-20","cited_by_count":300},{"id":"2003.08938","title":"Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations","created":"2020-03-19","cited_by_count":238},{"id":"1905.10615","title":"Adversarial Policies: Attacking Deep Reinforcement Learning","created":"2019-05-25","cited_by_count":214},{"id":"1801.02612","title":"Spatially Transformed Adversarial Examples","created":"2018-01-08","cited_by_count":195},{"id":"2305.20081","title":"Efficient Diffusion Policies for Offline Reinforcement Learning","created":"2023-05-31","cited_by_count":157},{"id":"1805.07894","title":"Constructing Unrestricted Adversarial Examples with Generative Models","created":"2018-05-21","cited_by_count":135},{"id":"2101.08452","title":"Robust Reinforcement Learning on State Observations with Learned Optimal Adversary","created":"2021-01-21","cited_by_count":135},{"id":"2305.18459","title":"Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning","created":"2023-05-29","cited_by_count":115},{"id":"2305.08192","title":"Diffusion Models for Imperceptible and Transferable Adversarial Attack","created":"2023-05-14","cited_by_count":94},{"id":"1902.07906","title":"Wasserstein Adversarial Examples via Projected Sinkhorn Iterations","created":"2019-02-21","cited_by_count":89},{"id":"2003.12613","title":"Adaptive Reward-Poisoning Attacks against Reinforcement Learning","created":"2020-03-27","cited_by_count":86},{"id":"1712.02779","title":"Exploring the Landscape of Spatial Robustness","created":"2017-12-07","cited_by_count":81},{"id":"2305.16494","title":"Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability","created":"2023-05-25","cited_by_count":66},{"id":"2001.09684","title":"Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning","created":"2020-01-27","cited_by_count":61},{"id":"2106.05087","title":"Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL","created":"2021-06-09","cited_by_count":55},{"id":"1809.03470","title":"ViZDoom Competitions: Playing Doom from Pixels","created":"2018-09-10","cited_by_count":51},{"id":"2210.01787","title":"Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective","created":"2022-10-04","cited_by_count":51},{"id":"1906.10571","title":"Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals","created":"2019-06-24","cited_by_count":50},{"id":"2307.12499","title":"AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models","created":"2023-07-24","cited_by_count":46},{"id":"2210.05927","title":"Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning","created":"2022-10-12","cited_by_count":43},{"id":"2404.10335","title":"Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Models","created":"2024-04-16","cited_by_count":38},{"id":"2307.16212","title":"Robust Multi-Agent Reinforcement Learning with State Uncertainty","created":"2023-07-30","cited_by_count":36},{"id":"2305.13625","title":"DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection","created":"2023-05-23","cited_by_count":35},{"id":"2006.05032","title":"Stealing Deep Reinforcement Learning Models for Fun and Profit","created":"2020-06-09","cited_by_count":24},{"id":"2301.07487","title":"Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness","created":"2023-01-17","cited_by_count":19},{"id":"2310.02635","title":"Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own","created":"2023-10-04","cited_by_count":19},{"id":"2310.11311","title":"Elucidating The Design Space of Classifier-Guided Diffusion Generation","created":"2023-10-17","cited_by_count":18},{"id":"2207.10170","title":"Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks","created":"2022-07-20","cited_by_count":12},{"id":"2206.07188","title":"Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising","created":"2022-06-14","cited_by_count":8},{"id":"2306.05873","title":"Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions","created":"2023-06-09","cited_by_count":7},{"id":"2312.08751","title":"Improve Robustness of Reinforcement Learning against Observation Perturbations via $l_\\infty$ Lipschitz Policy Networks","created":"2023-12-14","cited_by_count":7},{"id":"2109.07171","title":"Balancing detectability and performance of attacks on the control channel of Markov Decision Processes","created":"2021-09-15","cited_by_count":4},{"id":"2406.19807","title":"Deceptive Diffusion: Generating Synthetic Adversarial Examples","created":"2024-06-28","cited_by_count":2}],"next_cursor":null}