{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/self-driving-cars/papers/5","list_of":"/task/self-driving-cars","task":"Self-Driving Cars","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":5,"pages_in_order":6,"rows_per_page":100,"rows":[401,500],"of":514,"counts":{"archive_papers_tagged":514,"with_a_code_link":187,"where_syntology_ran_a_sample":32,"not_listed_spam_title":0,"listed":514,"listed_where_code_ran":32,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":30,"every_run_a_failure_of_syntologys_instrument":2,"listed_with_a_run_with_no_instrument_failure":30,"listed_every_run_a_failure_of_syntologys_instrument":2,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/self-driving-cars","prev":"/task/self-driving-cars/papers/4","next":"/task/self-driving-cars/papers/6","papers":[{"url":null,"slug":"application-of-neuroevolution-in-autonomous","title":"Application of Neuroevolution in Autonomous Cars","date":"2020-06-26","arxiv_id":"2006.15175","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoncp-automated-pipelines-for-accurate","title":"AutoCP: Automated Pipelines for Accurate Prediction Intervals","date":"2020-06-24","arxiv_id":"2006.14099","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-explain-or-not-to-explain-a-study-on-the","title":"To Explain or Not to Explain: A Study on the Necessity of Explanations for Autonomous Vehicles","date":"2020-06-21","arxiv_id":"2006.11684","repositories_listed":0,"syntology":null},{"url":null,"slug":"formal-verification-of-end-to-end-learning-in","title":"Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges","date":"2020-06-15","arxiv_id":"2006.09181","repositories_listed":0,"syntology":null},{"url":null,"slug":"pixel-invisibility-detecting-objects","title":"Pixel Invisibility: Detecting Objects Invisible in Color Images","date":"2020-06-15","arxiv_id":"2006.08383","repositories_listed":0,"syntology":null},{"url":null,"slug":"feudal-steering-hierarchical-learning-for","title":"Feudal Steering: Hierarchical Learning for Steering Angle Prediction","date":"2020-06-11","arxiv_id":"2006.06869","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-semantic-mapping-for-urban","title":"Probabilistic Semantic Mapping for Urban Autonomous Driving Applications","date":"2020-06-08","arxiv_id":"2006.04894","repositories_listed":0,"syntology":null},{"url":null,"slug":"light-in-the-loop-using-a-photonics-co","title":"Light-in-the-loop: using a photonics co-processor for scalable training of neural networks","date":"2020-06-02","arxiv_id":"2006.01475","repositories_listed":0,"syntology":null},{"url":null,"slug":"review-on-3d-lidar-localization-for","title":"A Survey on 3D LiDAR Localization for Autonomous Vehicles","date":"2020-06-01","arxiv_id":"2006.00648","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attacks-and-defense-on-textual","title":"Adversarial Attacks and Defense on Texts: A Survey","date":"2020-05-28","arxiv_id":"2005.14108","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-depth-estimators-vulnerabilities","title":"Monocular Depth Estimators: Vulnerabilities and Attacks","date":"2020-05-28","arxiv_id":"2005.14302","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-adversarial-networks-applied-to","title":"Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?","date":"2020-05-27","arxiv_id":"2005.13510","repositories_listed":0,"syntology":null},{"url":null,"slug":"monitoring-and-diagnosability-of-perception","title":"Monitoring and Diagnosability of Perception Systems","date":"2020-05-24","arxiv_id":"2005.11816","repositories_listed":0,"syntology":null},{"url":null,"slug":"satellite-navigation-for-the-age-of-autonomy","title":"Satellite Navigation for the Age of Autonomy","date":"2020-05-19","arxiv_id":"2005.09144","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-monitoring-for-neural-network-based","title":"Online Monitoring for Neural Network Based Monocular Pedestrian Pose Estimation","date":"2020-05-11","arxiv_id":"2005.05451","repositories_listed":0,"syntology":null},{"url":null,"slug":"attentional-bottleneck-towards-an","title":"Attentional Bottleneck: Towards an Interpretable Deep Driving Network","date":"2020-05-08","arxiv_id":"2005.04298","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-goal-driven-agents-and-robots-a","title":"Explainable Goal-Driven Agents and Robots -- A Comprehensive Review","date":"2020-04-21","arxiv_id":"2004.09705","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffic-lane-detection-using-fcn","title":"Traffic Lane Detection using FCN","date":"2020-04-19","arxiv_id":"2004.08977","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-machine-learning-for-intelligent","title":"Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface","date":"2020-04-13","arxiv_id":"2004.05843","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-adversarial-examples-in-learning","title":"Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression","date":"2020-03-21","arxiv_id":"2003.10804","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-depth-estimation-with-gated","title":"Uncertainty depth estimation with gated images for 3D reconstruction","date":"2020-03-11","arxiv_id":"2003.05122","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-based-ood-detector-principles-on-graph","title":"Image-based OoD-Detector Principles on Graph-based Input Data in Human Action Recognition","date":"2020-03-03","arxiv_id":"2003.01719","repositories_listed":0,"syntology":null},{"url":null,"slug":"who-is-driving-around-me-unique-vehicle","title":"\"Who is Driving around Me?\" Unique Vehicle Instance Classification using Deep Neural Features","date":"2020-02-29","arxiv_id":"2003.08771","repositories_listed":0,"syntology":null},{"url":null,"slug":"advms-a-multi-source-multi-cost-defense","title":"AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks","date":"2020-02-19","arxiv_id":"2002.08439","repositories_listed":0,"syntology":null},{"url":"/paper/frsign-a-large-scale-traffic-light-dataset","slug":"frsign-a-large-scale-traffic-light-dataset","title":"FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains","date":"2020-02-05","arxiv_id":"2002.05665","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-graph-based-trajectory-predictor-with","title":"A Novel Graph based Trajectory Predictor with Pseudo Oracle","date":"2020-02-02","arxiv_id":"2002.00391","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-out-of-distribution-detection-in","title":"Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems","date":"2020-01-28","arxiv_id":"2001.10494","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-finite-state-control-of-pomdps","title":"Stochastic Finite State Control of POMDPs with LTL Specifications","date":"2020-01-21","arxiv_id":"2001.07679","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-abstraction-model-for-semantic","title":"An Abstraction Model for Semantic Segmentation Algorithms","date":"2019-12-27","arxiv_id":"1912.11995","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-millimeter-wave-imaging-for","title":"High Resolution Millimeter Wave Imaging For Self-Driving Cars","date":"2019-12-19","arxiv_id":"1912.09579","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-testing-and-falsification-with","title":"Automatic Testing With Reusable Adversarial Agents","date":"2019-10-30","arxiv_id":"1910.13645","repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-wise-decomposition-for-accelerating","title":"Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks","date":"2019-10-21","arxiv_id":"1910.09455","repositories_listed":0,"syntology":null},{"url":null,"slug":"teaching-vehicles-to-anticipate-a-systematic","title":"Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets","date":"2019-10-17","arxiv_id":"1910.07772","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-driving-from-natural-language","title":"Conditional Driving from Natural Language Instructions","date":"2019-10-16","arxiv_id":"1910.07615","repositories_listed":0,"syntology":null},{"url":null,"slug":"gladas-gesture-learning-for-advanced-driver","title":"GLADAS: Gesture Learning for Advanced Driver Assistance Systems","date":"2019-10-02","arxiv_id":"1910.04695","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-protection-rejection-of","title":"Learning with Protection: Rejection of Suspicious Samples under Adversarial Environment","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-assume-guarantee-profiles-for","title":"Towards Assume-Guarantee Profiles for Autonomous Vehicles","date":"2019-09-12","arxiv_id":"1909.04850","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-road-user-importance-estimation","title":"Context Aware Road-user Importance Estimation (iCARE)","date":"2019-08-30","arxiv_id":"1909.05152","repositories_listed":0,"syntology":null},{"url":null,"slug":"starnet-targeted-computation-for-object","title":"StarNet: Targeted Computation for Object Detection in Point Clouds","date":"2019-08-29","arxiv_id":"1908.11069","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-far-should-self-driving-cars-see-effect","title":"How far should self-driving cars see? Effect of observation range on vehicle self-localization","date":"2019-08-19","arxiv_id":"1908.06588","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-sparse-semantic-hd-maps-for-self","title":"Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization","date":"2019-08-08","arxiv_id":"1908.03274","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-learn-or-not-to-learn-visual-localization","title":"To Learn or Not to Learn: Visual Localization from Essential Matrices","date":"2019-08-04","arxiv_id":"1908.01293","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensyth-a-pruning-approach-to-synthesis-of","title":"EnSyth: A Pruning Approach to Synthesis of Deep Learning Ensembles","date":"2019-07-22","arxiv_id":"1907.09286","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-sensor-modeling-for-lidar-point","title":"End-to-end sensor modeling for LiDAR Point Cloud","date":"2019-07-17","arxiv_id":"1907.07748","repositories_listed":0,"syntology":null},{"url":null,"slug":"metamorphic-detection-of-adversarial-examples","title":"Metamorphic Detection of Adversarial Examples in Deep Learning Models With Affine Transformations","date":"2019-07-10","arxiv_id":"1907.04774","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-3d-point-cloud-representations","title":"Large-scale 3D point cloud representations via graph inception networks with applications to autonomous driving","date":"2019-06-26","arxiv_id":"1906.11359","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-in-the-automotive-industry-1","title":"Deep Learning in the Automotive Industry: Recent Advances and Application Examples","date":"2019-06-20","arxiv_id":"1906.08834","repositories_listed":0,"syntology":null},{"url":null,"slug":"lidar-based-detection-and-classification-of","title":"Lidar based Detection and Classification of Pedestrians and Vehicles Using Machine Learning Methods","date":"2019-06-12","arxiv_id":"1906.11899","repositories_listed":0,"syntology":null},{"url":null,"slug":"novelty-detection-via-network-saliency-in","title":"Novelty Detection via Network Saliency in Visual-based Deep Learning","date":"2019-06-09","arxiv_id":"1906.03685","repositories_listed":0,"syntology":null},{"url":null,"slug":"key-ingredients-of-self-driving-cars","title":"Key Ingredients of Self-Driving Cars","date":"2019-06-07","arxiv_id":"1906.02939","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600932","title":"Y-GAN: A Generative Adversarial Network for Depthmap Estimation from Multi-camera Stereo Images","date":"2019-06-03","arxiv_id":"1906.00932","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-does-it-mean-to-learn-in-deep-networks","title":"What Does It Mean to Learn in Deep Networks? And, How Does One Detect Adversarial Attacks?","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"asymptotically-unambitious-artificial-general","title":"Asymptotically Unambitious Artificial General Intelligence","date":"2019-05-29","arxiv_id":"1905.12186","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-pedestrian-vehicle-interactions","title":"Understanding Pedestrian-Vehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis","date":"2019-05-14","arxiv_id":"1905.05350","repositories_listed":0,"syntology":null},{"url":null,"slug":"190503517","title":"Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain","date":"2019-05-09","arxiv_id":"1905.03517","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-a-fast-object-detector-for-lidar","title":"Training a Fast Object Detector for LiDAR Range Images Using Labeled Data from Sensors with Higher Resolution","date":"2019-05-08","arxiv_id":"1905.03066","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximate-lstms-for-time-constrained","title":"Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars","date":"2019-05-02","arxiv_id":"1905.00689","repositories_listed":0,"syntology":null},{"url":null,"slug":"teaching-ai-ethics-law-and-policy","title":"Teaching AI, Ethics, Law and Policy","date":"2019-04-29","arxiv_id":"1904.12470","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-self-driving-cars-secure-evasion-attacks","title":"Are Self-Driving Cars Secure? Evasion Attacks against Deep Neural Networks for Steering Angle Prediction","date":"2019-04-15","arxiv_id":"1904.07370","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-examples-improve-generalization-for","title":"Synthetic Examples Improve Generalization for Rare Classes","date":"2019-04-11","arxiv_id":"1904.05916","repositories_listed":0,"syntology":null},{"url":null,"slug":"tampernn-efficient-tampering-detection-of","title":"TamperNN: Efficient Tampering Detection of Deployed Neural Nets","date":"2019-03-01","arxiv_id":"1903.00317","repositories_listed":0,"syntology":null},{"url":null,"slug":"liability-ethics-and-culture-aware-behavior","title":"Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks","date":"2019-02-25","arxiv_id":"1902.09355","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-self-supervised-high-level-sensor","title":"Towards Self-Supervised High Level Sensor Fusion","date":"2019-02-12","arxiv_id":"1902.04272","repositories_listed":0,"syntology":null},{"url":null,"slug":"autonomous-cars-vision-based-steering-wheel","title":"Autonomous Cars: Vision based Steering Wheel Angle Estimation","date":"2019-01-30","arxiv_id":"1901.10747","repositories_listed":0,"syntology":null},{"url":"/paper/context-prediction-for-unsupervised-deep","slug":"context-prediction-for-unsupervised-deep","title":"Self-Supervised Deep Learning on Point Clouds by Reconstructing Space","date":"2019-01-24","arxiv_id":"1901.08396","repositories_listed":0,"syntology":null},{"url":null,"slug":"safety-and-trustworthiness-of-deep-neural","title":"A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability","date":"2018-12-18","arxiv_id":"1812.08342","repositories_listed":0,"syntology":null},{"url":null,"slug":"strength-in-numbers-trading-off-robustness","title":"Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles","date":"2018-11-22","arxiv_id":"1811.09300","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-uncertainty-quantification-in-end","title":"Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control","date":"2018-11-16","arxiv_id":"1811.06817","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-of-depth-and-camera","title":"Self-Supervised Learning of Depth and Camera Motion from 360° Videos","date":"2018-11-13","arxiv_id":"1811.05304","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stream-convolutional-networks-for-end-to","title":"Two-stream convolutional networks for end-to-end learning of self-driving cars","date":"2018-11-13","arxiv_id":"1811.05785","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-dense-stereo-matching-for-digital","title":"Learning Dense Stereo Matching for Digital Surface Models from Satellite Imagery","date":"2018-11-08","arxiv_id":"1811.03535","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-tractable-probabilistic-models-for-moral","title":"Learning Tractable Probabilistic Models for Moral Responsibility and Blame","date":"2018-10-08","arxiv_id":"1810.03736","repositories_listed":0,"syntology":null},{"url":"/paper/triply-supervised-decoder-networks-for-joint","slug":"triply-supervised-decoder-networks-for-joint","title":"Triply Supervised Decoder Networks for Joint Detection and Segmentation","date":"2018-09-25","arxiv_id":"1809.09299","repositories_listed":0,"syntology":null},{"url":"/paper/combined-image-and-world-space-tracking-in","slug":"combined-image-and-world-space-tracking-in","title":"Combined Image- and World-Space Tracking in Traffic Scenes","date":"2018-09-19","arxiv_id":"1809.07357","repositories_listed":0,"syntology":null},{"url":null,"slug":"driving-experience-transfer-method-for-end-to","title":"Driving Experience Transfer Method for End-to-End Control of Self-Driving Cars","date":"2018-09-06","arxiv_id":"1809.01822","repositories_listed":0,"syntology":null},{"url":null,"slug":"obstacle-detection-quality-as-a-problem","title":"Obstacle Detection Quality as a Problem-Oriented Approach to Stereo Vision Algorithms Estimation in Road Situation Analysis","date":"2018-09-06","arxiv_id":"1809.02228","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-neural-networks-for-image","title":"Evaluation of Neural Networks for Image Recognition Applications: Designing a 0-1 MILP Model of a CNN to create adversarials","date":"2018-09-01","arxiv_id":"1809.00216","repositories_listed":0,"syntology":null},{"url":null,"slug":"juncnet-a-deep-neural-network-for-road","title":"JuncNet: A Deep Neural Network for Road Junction Disambiguation for Autonomous Vehicles","date":"2018-08-31","arxiv_id":"1809.01011","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-scene-compression-for-visual","title":"Hybrid Scene Compression for Visual Localization","date":"2018-07-19","arxiv_id":"1807.07512","repositories_listed":0,"syntology":null},{"url":"/paper/semantic-instance-meets-salient-object-study","slug":"semantic-instance-meets-salient-object-study","title":"Semantic Instance Meets Salient Object: Study on Video Semantic Salient Instance Segmentation","date":"2018-07-04","arxiv_id":"1807.01452","repositories_listed":0,"syntology":null},{"url":"/paper/ego-lane-analysis-system-elas-dataset-and","slug":"ego-lane-analysis-system-elas-dataset-and","title":"Ego-Lane Analysis System (ELAS): Dataset and Algorithms","date":"2018-06-15","arxiv_id":"1806.05984","repositories_listed":0,"syntology":null},{"url":null,"slug":"pointflownet-learning-representations-for","title":"PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds","date":"2018-06-06","arxiv_id":"1806.02170","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-evaluation-of-deep-learning-1","title":"Performance Evaluation of Deep Learning Networks for Semantic Segmentation of Traffic Stereo-Pair Images","date":"2018-06-05","arxiv_id":"1806.01896","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-attacks-on-agents-for-long-term","title":"Sequential Attacks on Agents for Long-Term Adversarial Goals","date":"2018-05-31","arxiv_id":"1805.12487","repositories_listed":0,"syntology":null},{"url":null,"slug":"trusted-neural-networks-for-safety","title":"Trusted Neural Networks for Safety-Constrained Autonomous Control","date":"2018-05-18","arxiv_id":"1805.07075","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-autonomous-reinforcement-learning","title":"Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization","date":"2018-05-12","arxiv_id":"1805.04748","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-adversarial-deep-learning","title":"Semantic Adversarial Deep Learning","date":"2018-04-19","arxiv_id":"1804.07045","repositories_listed":0,"syntology":null},{"url":null,"slug":"vehicle-communication-strategies-for","title":"Vehicle Communication Strategies for Simulated Highway Driving","date":"2018-04-19","arxiv_id":"1804.07178","repositories_listed":0,"syntology":null},{"url":null,"slug":"drive-video-analysis-for-the-detection-of","title":"Drive Video Analysis for the Detection of Traffic Near-Miss Incidents","date":"2018-04-07","arxiv_id":"1804.02555","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-self-driving-cars-on-twitter","title":"Analyzing Self-Driving Cars on Twitter","date":"2018-04-05","arxiv_id":"1804.04058","repositories_listed":0,"syntology":null},{"url":null,"slug":"event-based-vision-meets-deep-learning-on","title":"Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars","date":"2018-04-04","arxiv_id":"1804.01310","repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-naturalistic-driving-encounters-using","title":"Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning","date":"2018-02-28","arxiv_id":"1802.10214","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-image-evidence-analysis-of-cnn","title":"Efficient Image Evidence Analysis of CNN Classification Results","date":"2018-01-05","arxiv_id":"1801.01693","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-intelligence-and-statistics","title":"Artificial Intelligence and Statistics","date":"2017-12-08","arxiv_id":"1712.03779","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-grand-theft-auto-v-for-training","title":"Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep Learning in Self Driving Cars","date":"2017-12-04","arxiv_id":"1712.01397","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-guided-black-box-safety-testing-of","title":"Feature-Guided Black-Box Safety Testing of Deep Neural Networks","date":"2017-10-21","arxiv_id":"1710.07859","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-the-resource-requirements-of","title":"Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices","date":"2017-09-27","arxiv_id":"1709.09503","repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudo-labels-for-supervised-learning-on","title":"Pseudo-labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection under Ego-motion","date":"2017-09-27","arxiv_id":"1709.09323","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-machine-learning-for-networking","title":"Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges","date":"2017-09-19","arxiv_id":"1709.06599","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-evasion-attacks-to-deep-neural","title":"Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification","date":"2017-09-17","arxiv_id":"1709.05583","repositories_listed":0,"syntology":null}],"record_sha256":"05d41d295314de92e82a1968c7a52aac4ccdd7e71cda8266a9722abb0a3b9357","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}