{"list":"other","total":55,"rows":[{"id":"2303.08774","title":"GPT-4 Technical Report","created":"2023-03-15","cited_by_count":18396},{"id":"1707.06347","title":"Proximal Policy Optimization Algorithms","created":"2017-07-20","cited_by_count":15215},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"2304.08485","title":"Visual Instruction Tuning","created":"2023-04-17","cited_by_count":7869},{"id":"1801.04381","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","created":"2018-01-13","cited_by_count":4795},{"id":"2307.15818","title":"RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control","created":"2023-07-28","cited_by_count":3080},{"id":"1711.03938","title":"CARLA: An Open Urban Driving Simulator","created":"2017-11-10","cited_by_count":2449},{"id":"2311.10122","title":"Video-LLaVA: Learning United Visual Representation by Alignment Before Projection","created":"2023-11-16","cited_by_count":1402},{"id":"2003.13678","title":"Designing Network Design Spaces","created":"2020-03-30","cited_by_count":960},{"id":"2110.02178","title":"MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer","created":"2021-10-05","cited_by_count":642},{"id":"1710.02410","title":"End-to-end Driving via Conditional Imitation Learning","created":"2017-10-06","cited_by_count":485},{"id":"1811.08886","title":"HAQ: Hardware-Aware Automated Quantization with Mixed Precision","created":"2018-11-21","cited_by_count":443},{"id":"2207.04429","title":"LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action","created":"2022-07-10","cited_by_count":439},{"id":"2102.04906","title":"Dynamic Neural Networks: A Survey","created":"2021-02-09","cited_by_count":408},{"id":"2201.08117","title":"Learning robust perceptive locomotion for quadrupedal robots in the wild","created":"2022-01-20","cited_by_count":382},{"id":"1609.05143","title":"Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning","created":"2016-09-16","cited_by_count":366},{"id":"1904.08980","title":"Exploring the Limitations of Behavior Cloning for Autonomous Driving","created":"2019-04-18","cited_by_count":346},{"id":"1802.05668","title":"Model compression via distillation and quantization","created":"2018-02-15","cited_by_count":289},{"id":"2405.10369","title":"Reinforcement learning","created":"2024-05-16","cited_by_count":247},{"id":"1909.12326","title":"Model Pruning Enables Efficient Federated Learning on Edge Devices","created":"2019-09-26","cited_by_count":222},{"id":"1809.08835","title":"Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning","created":"2018-09-24","cited_by_count":175},{"id":"2309.05665","title":"Robot Parkour Learning","created":"2023-09-11","cited_by_count":149},{"id":"1611.05128","title":"Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning","created":"2016-11-16","cited_by_count":134},{"id":"2008.06402","title":"SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud","created":"2020-08-14","cited_by_count":112},{"id":"2103.05668","title":"Core Challenges of Social Robot Navigation: A Survey","created":"2021-03-09","cited_by_count":106},{"id":"2104.13906","title":"Reward (Mis)design for Autonomous Driving","created":"2021-04-28","cited_by_count":104},{"id":"1912.00177","title":"Urban Driving with Conditional Imitation Learning","created":"2019-11-30","cited_by_count":86},{"id":"2111.01674","title":"Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots","created":"2021-10-25","cited_by_count":76},{"id":"2106.05022","title":"Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions","created":"2021-06-09","cited_by_count":75},{"id":"1707.03167","title":"RegNet: Multimodal Sensor Registration Using Deep Neural Networks","created":"2017-07-11","cited_by_count":69},{"id":"2402.02769","title":"Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate","created":"2024-02-05","cited_by_count":47},{"id":"2211.11152","title":"You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model","created":"2022-11-21","cited_by_count":43},{"id":"2201.13096","title":"SPDY: Accurate Pruning with Speedup Guarantees","created":"2022-01-31","cited_by_count":36},{"id":"2405.20915","title":"Fast yet Safe: Early-Exiting with Risk Control","created":"2024-05-31","cited_by_count":30},{"id":"2405.05983","title":"Real-Time Pill Identification for the Visually Impaired Using Deep Learning","created":"2024-05-08","cited_by_count":21},{"id":"2306.10014","title":"Coaching a Teachable Student","created":"2023-06-16","cited_by_count":19},{"id":"2405.03026","title":"Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Task","created":"2024-05-05","cited_by_count":19},{"id":"2204.10320","title":"SelfD: Self-Learning Large-Scale Driving Policies From the Web","created":"2022-04-21","cited_by_count":18},{"id":"2309.16772","title":"XVO: Generalized Visual Odometry via Cross-Modal Self-Training","created":"2023-09-28","cited_by_count":18},{"id":"2105.04104","title":"AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference","created":"2021-05-10","cited_by_count":17},{"id":"2212.00972","title":"Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-world","created":"2022-12-02","cited_by_count":17},{"id":"2303.08010","title":"Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models","created":"2023-03-14","cited_by_count":16},{"id":"2003.02449","title":"Cluster Pruning: An Efficient Filter Pruning Method for Edge AI Vision Applications","created":"2020-03-05","cited_by_count":11},{"id":"2008.02033","title":"Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning","created":"2020-08-05","cited_by_count":11},{"id":"2104.02487","title":"Neural Process for Black-Box Model Optimization Under Bayesian Framework","created":"2021-04-03","cited_by_count":11},{"id":"2309.12295","title":"Learning to Drive Anywhere","created":"2023-09-21","cited_by_count":10},{"id":"2007.05616","title":"NaviGAN: A Generative Approach for Socially Compliant Navigation","created":"2020-07-10","cited_by_count":7},{"id":"2407.07364","title":"Real-time system optimal traffic routing under uncertainties -- Can physics models boost reinforcement learning?","created":"2024-07-10","cited_by_count":7},{"id":"2308.15368","title":"RED: A Systematic Real-Time Scheduling Approach for Robotic Environmental Dynamics","created":"2023-08-29","cited_by_count":6},{"id":"1907.13306","title":"Cost-Driven Offloading for DNN-based Applications over Cloud, Edge and End Devices","created":"2019-07-31","cited_by_count":5},{"id":"2105.12923","title":"Robust Navigation for Racing Drones based on Imitation Learning and Modularization","created":"2021-05-27","cited_by_count":5},{"id":"2101.10417","title":"Cloud, Fog or Edge: Where to Compute?","created":"2021-01-25","cited_by_count":4},{"id":"1806.08943","title":"Edge and Central Cloud Computing: A Perfect Pairing for High Energy Efficiency and Low-latency","created":"2018-06-23","cited_by_count":3},{"id":"2008.07083","title":"Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving","created":"2020-08-17","cited_by_count":3},{"id":"2103.16188","title":"Collaborative Cloud and Edge Mobile Computing in C-RAN Systems with Minimal End-to-End Latency","created":"2021-03-30","cited_by_count":3}],"next_cursor":null}