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The book is based on a course on Special Relativity and acclaimed by students taught by Dragan who is a leader of a research group on Relativistic Quantum Information theory at the University of Warsaw and the National University of ...

Over the past decades, growing amount and diversity of methods have been proposed for image matching, particularly with the development of deep learning techniques over the recent years. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the … [94] Language models learned from data have been shown to contain human-like biases. Other approaches have been developed which don't fit neatly into this three-fold categorisation, and sometimes more than one is used by the same machine learning system. Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy. 08:10–08:20 . i It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification. Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. It is seen as a part of artificial intelligence.Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. (And a Dataset of 230,000 3D Facial Landmarks), Learning From Simulated and Unsupervised Images Through Adversarial Training, Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space, Video Frame Interpolation via Adaptive Convolution, Video Frame Interpolation via Adaptive Separable Convolution, GMS: Grid-based Motion Statistics for Fast, Ultra-Robust Feature Correspondence, Joint Detection and Identification Feature Learning for Person Search, Flow-Guided Feature Aggregation for Video Object Detection, Richer Convolutional Features for Edge Detection, Annotating Object Instances With a Polygon-RNN, RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation, Detecting Oriented Text in Natural Images by Linking Segments, Deep Lattice Networks and Partial Monotonic Functions, Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, RON: Reverse Connection With Objectness Prior Networks for Object Detection, Universal Style Transfer via Feature Transforms, Residual Attention Network for Image Classification, Accurate Single Stage Detector Using Recurrent Rolling Convolution, Feature Pyramid Networks for Object Detection, OctNet: Learning Deep 3D Representations at High Resolutions, Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution, Self-Critical Sequence Training for Image Captioning, Age Progression/Regression by Conditional Adversarial Autoencoder, Style Transfer from Non-Parallel Text by Cross-Alignment, Lifting From the Deep: Convolutional 3D Pose Estimation From a Single Image, DeepBach: a Steerable Model for Bach Chorales Generation, The Predictron: End-To-End Learning and Planning, Convolutional Sequence to Sequence Learning, OptNet: Differentiable Optimization as a Layer in Neural Networks, Prototypical Networks for Few-shot Learning, Deep Voice: Real-time Neural Text-to-Speech, Reinforcement Learning with Deep Energy-Based Policies, Learning Deep CNN Denoiser Prior for Image Restoration, GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, A Point Set Generation Network for 3D Object Reconstruction From a Single Image, Deeply Supervised Salient Object Detection With Short Connections, BlitzNet: A Real-Time Deep Network for Scene Understanding, Language Modeling with Gated Convolutional Networks, Unlabeled Samples Generated by GAN Improve the Person Re-Identification Baseline in Vitro, RMPE: Regional Multi-Person Pose Estimation, Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning, VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition, The Reversible Residual Network: Backpropagation Without Storing Activations, Recurrent Scale Approximation for Object Detection in CNN, Spatially Adaptive Computation Time for Residual Networks, Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis, 3D Bounding Box Estimation Using Deep Learning and Geometry, Multi-View 3D Object Detection Network for Autonomous Driving, Interpretable Explanations of Black Boxes by Meaningful Perturbation, Inverse Compositional Spatial Transformer Networks, FastMask: Segment Multi-Scale Object Candidates in One Shot, OnACID: Online Analysis of Calcium Imaging Data in Real Time, Semantic Scene Completion From a Single Depth Image, Learning Efficient Convolutional Networks Through Network Slimming, Learning Feature Pyramids for Human Pose Estimation, Be Your Own Prada: Fashion Synthesis With Structural Coherence, Scene Graph Generation by Iterative Message Passing, Fast Image Processing With Fully-Convolutional Networks, Learning Multiple Tasks with Multilinear Relationship Networks, Learning to Reason: End-To-End Module Networks for Visual Question Answering, Single Shot Text Detector With Regional Attention, Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment With Limited Resources, Deep Feature Interpolation for Image Content Changes, On Human Motion Prediction Using Recurrent Neural Networks, Image Super-Resolution via Deep Recursive Residual Network, Learning Cross-Modal Embeddings for Cooking Recipes and Food Images, Simple Does It: Weakly Supervised Instance and Semantic Segmentation, Low-Shot Visual Recognition by Shrinking and Hallucinating Features, Soft Proposal Networks for Weakly Supervised Object Localization, Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks, Gradient Episodic Memory for Continual Learning, DSAC - Differentiable RANSAC for Camera Localization, Attend to You: Personalized Image Captioning With Context Sequence Memory Networks, Language Modeling with Recurrent Highway Hypernetworks, Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning, Detecting Visual Relationships With Deep Relational Networks, Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach, Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model, Multi-Context Attention for Human Pose Estimation, Controlling Perceptual Factors in Neural Style Transfer, Adversarial Discriminative Domain Adaptation, Working hard to know your neighbor's margins: Local descriptor learning loss, SegFlow: Joint Learning for Video Object Segmentation and Optical Flow, Segmentation-Aware Convolutional Networks Using Local Attention Masks, Detail-Revealing Deep Video Super-Resolution, CREST: Convolutional Residual Learning for Visual Tracking, Discriminative Correlation Filter With Channel and Spatial Reliability, Semantic Image Synthesis via Adversarial Learning, Spatiotemporal Multiplier Networks for Video Action Recognition, PoseTrack: Joint Multi-Person Pose Estimation and Tracking, Hierarchical Attentive Recurrent Tracking, Good Semi-supervised Learning That Requires a Bad GAN, Deep Watershed Transform for Instance Segmentation, Learning by Association -- A Versatile Semi-Supervised Training Method for Neural Networks, Unrestricted Facial Geometry Reconstruction Using Image-To-Image Translation, MemNet: A Persistent Memory Network for Image Restoration, TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning, Compressed Sensing using Generative Models, Switching Convolutional Neural Network for Crowd Counting, WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation, Show, Adapt and Tell: Adversarial Training of Cross-Domain Image Captioner, Video Frame Synthesis Using Deep Voxel Flow, Multiple Instance Detection Network With Online Instance Classifier Refinement, Train longer, generalize better: closing the generalization gap in large batch training of neural networks, Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction, Unite the People: Closing the Loop Between 3D and 2D Human Representations, Learning Combinatorial Optimization Algorithms over Graphs, FeUdal Networks for Hierarchical Reinforcement Learning, ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression, Learning a Deep Embedding Model for Zero-Shot Learning, ECO: Efficient Convolution Operators for Tracking, SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning, Multi-View Supervision for Single-View Reconstruction via Differentiable Ray Consistency, Task-based End-to-end Model Learning in Stochastic Optimization, Learning to Compose Domain-Specific Transformations for Data Augmentation, HashNet: Deep Learning to Hash by Continuation, Deeply-Learned Part-Aligned Representations for Person Re-Identification, Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model, Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation, Octree Generating Networks: Efficient Convolutional Architectures for High-Resolution 3D Outputs, Semantic Autoencoder for Zero-Shot Learning, Decoupled Neural Interfaces using Synthetic Gradients, Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks, Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search, Optical Flow Estimation Using a Spatial Pyramid Network, AMC: Attention guided Multi-modal Correlation Learning for Image Search, Deep Video Deblurring for Hand-Held Cameras, Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data, Causal Effect Inference with Deep Latent-Variable Models, MMD GAN: Towards Deeper Understanding of Moment Matching Network, Representation Learning by Learning to Count, Unsupervised Video Summarization With Adversarial LSTM Networks, Coarse-To-Fine Volumetric Prediction for Single-Image 3D Human Pose, End-To-End Instance Segmentation With Recurrent Attention, DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data, Learning Shape Abstractions by Assembling Volumetric Primitives, Local Binary Convolutional Neural Networks, Raster-To-Vector: Revisiting Floorplan Transformation, Positive-Unlabeled Learning with Non-Negative Risk Estimator, Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis, Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade, Improved Stereo Matching With Constant Highway Networks and Reflective Confidence Learning, Query-Guided Regression Network With Context Policy for Phrase Grounding, Top-Down Visual Saliency Guided by Captions.

[34] The data is known as training data, and consists of a set of training examples. [76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly. 33 Full PDFs related to this paper.

Reinforcement Learning and Optimal Control Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. [42] It is a learning with no external rewards and no external teacher advice. [55], In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts of inactivity.

Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. TUK Campus Dataset, Stereo Waterdrop Removal with Row-Wise Dilated Attention, Temporally-Continuous Probabilistic Prediction Using Polynomial Trajectory Parameterization, Content Disentanglement for Semantically Consistent Synthetic-To-Real Domain Adaptation, Cross-Modal 3D Object Detection and Tracking for Auto-Driving, Contact Tracing: A Low Cost Reconstruction Framework for Surface Contact Interpolation, Real-Time Physically-Accurate Simulation of Robotic Snap Connection Process, Fundamental Challenges in Deep Learning for Stiff Contact Dynamics, Multi-Contact Locomotion Planning with Bilateral Contact Forces Considering Kinematics and Statics During Contact Transition, Computationally Efficient HQP-Based Whole-Body Control Exploiting the Operational-Space Formulation, Towards an Online Framework for Changing-Contact Robot Manipulation Tasks, Experimental Verification of Stability Theory for a Planar Rigid Body with Two Unilateral Frictional Contacts (I), Sensor Fusion-Based Anthropomorphic Control of Under-Actuated Bionic Hand in Dynamic Environment, Model-Based Trajectory Prediction and Hitting Velocity Control for a New Table Tennis Robot, Active Exploration and Mapping Via Iterative Covariance Regulation Over Continuous SE(3) Trajectories, Modeling and Control of PANTHERA Self-Reconfigurable Pavement Sweeping Robot under Actuator Constraints, Coloured Petri Nets for Monitoring Human Actions in Flexible Human-Robot Teams, Adaptive Passivity-Based Multi-Task Tracking Control for Robotic Manipulators, Amplification of Clamping Mechanism Using Internally-Balanced Magnetic Unit, Distributed Tube-Based Nonlinear MPC for Motion Control of Skid-Steer Robots with Terra-Mechanical Constraints, Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games, The Radar Ghost Dataset – an Evaluation of Ghost Objects in Automotive Radar Data, ChangeSim: Towards End-To-End Online Scene Change Detection in Industrial Indoor Environments, Indoor Future Person Localization from an Egocentric Wearable Camera, Grounding Linguistic Commands to Navigable Regions, TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset, Diverse Complexity Measures for Dataset Curation in Self-Driving, A Dataset for Provident Vehicle Detection at Night, Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception, A Photorealistic Terrain Simulation Pipeline for Unstructured Outdoor Environments, NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences, Topo-Boundary: A Benchmark Dataset on Topological Road-Boundary Detection Using Aerial Images for Autonomous Driving, ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Picking, A Large-Scale Dataset for Water Segmentation of SAR Satellite, ESPADA: Extended Synthetic and Photogrammetric Aerial-Image Dataset, Adversarial Training on Point Clouds for Sim-To-Real 3D Object Detection, CrossMap Transformer: A Crossmodal Masked Path TransformerUsing Double Back-Translation for Vision-And-Language Navigation, Case Relation Transformer: A Crossmodal Language Generation Model for Fetching Instructions, Target-Dependent UNITER: A Transformer-Based Multimodal Language Comprehension Model for Domestic Service Robots, Self-Critical Learning of Influencing Factors for Trajectory Prediction Using Gated Graph Convolutional Network, Trajectory Generation in New Environments from past Experiences, DistillPose: Lightweight Camera Localization Using Auxiliary Learning, Identifying Valid Robot Configurations Via a Deep Learning Approach, DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks, StyleLess Layer: Improving Robustness for Real-World Driving, Annotation Cost Reduction of Stream-Based Active Learning by Automated Weak Labeling Using a Robot Arm, Comprehension of Spatial Constraints by Neural Logic Learning from a Single RGB-D Scan, A CNN Based Vision-Proprioception Fusion Method for Robust UGV Terrain Classification, Visual-Tactile Cross-Modal Data Generation Using Residue-Fusion GAN with Feature-Matching and Perceptual Losses, Geometry Guided Network for Point Cloud Registration, Graph Guided Deformation for Point Cloud Completion, Uncertainty-Aware Self-Supervised Learning of Spatial Perception Tasks, ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation, Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization, MLPD: Multi-Label Pedestrian Detector in Multispectral Domain, Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras, EVReflex: Dense Time-To-Impact Prediction for Event-Based Obstacle Avoidance, PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds, A Registration-Aided Domain Adaptation Network for 3D Point Cloud Based Place Recognition, INeRF: Inverting Neural Radiance Fields for Pose Estimation, RaP-Net: A Region-Wise and Point-Wise Weighting Network to Extract Robust Features for Indoor Localization, Differentiable Factor Graph Optimization for Learning Smoothers, Attention Augmented ConvLSTM for Environment Prediction, Overcoming Obstructions Via Bandwidth-Limited Multi-Agent Spatial Handshaking, Scene Descriptor Expressing Ambiguity in Information Recovery Based on Incomplete Partial Observation, Bootstrapped Self-Supervised Training with Monocular Video for Semantic Segmentation and Depth Estimation, Automatic Learning System for Object Function Points from Random Shape Generation and Physical Validation, Fast Image-Anomaly Mitigation for Autonomous Mobile Robots, Visual Identification of Articulated Object Parts, Unsupervised Monocular Depth Learning with Integrated Intrinsics and Spatio-Temporal Constraints, ViNet: Pushing the Limits of Visual Modality for Audio-Visual Saliency Prediction, MDN-VO: Estimating Visual Odometry with Confidence, Unsupervised Deep Persistent Monocular Visual Odometry and Depth Estimation in Extreme Environments, Correlate-And-Excite: Real-Time Stereo Matching Via Guided Cost Volume Excitation, Improving Robot Localisation by Ignoring Visual Distraction, Semantic Segmentation-Assisted Scene Completion for LiDAR Point Clouds, Dynamic Domain Adaptation for Single-View 3D Reconstruction, You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module, VIPose: Real-Time Visual-Inertial 6D Object Pose Tracking, Using Visual Anomaly Detection for Task Execution Monitoring, Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes, Pose Estimation from RGB Images of Highly Symmetric Objects Using a Novel Multi-Pose Loss and Differential Rendering, Denoising 3D Human Poses from Low-Resolution Video Using Variational Autoencoder, KDFNet: Learning Keypoint Distance Field for 6D Object Pose Estimation, All Characteristics Preservation: Single Image Dehazing Based on Hierarchical Detail Reconstruction Wavelet Decomposition Network, PCTMA-Net: Point Cloud Transformer with Morphing Atlas-Based Point Generation Network for Dense Point Cloud Completion, Superline: A Robust Line Segment Feature for Visual SLAM, ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images, Model Adaptation through Hypothesis Transfer with Gradual Knowledge Distillation, VoluMon: Weakly Supervised Volumetric Monocular Estimation with Ellipsoid Representations, Cross-Modal Representation Learning for Lightweight and Accurate Facial Action Unit Detection, Stereo Matching by Self-Supervision of Multiscopic Vision, Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimation, Efficient Learning of Goal-Oriented Push-Grasping Synergy in Clutter, Iterative Coarse-To-Fine 6D-Pose Estimation Using Back-Propagation, Understanding Human Manipulation with the Environment: A Novel Taxonomy for Video Labelling, Excavation Learning for Rigid Objects in Clutter, Fast-Learning Grasping and Pre-Grasping Via Clutter Quantization and Q-Map Masking, Joint Space Control Via Deep Reinforcement Learning, Precise Object Placement with Pose Distance Estimations for Different Objects and Grippers, Learning to Detect Multi-Modal Grasps for Dexterous Grasping in Dense Clutter, Double-Dot Network for Antipodal Grasp Detection, Neural Motion Prediction for In-Flight Uneven Object Catching, Learning a Generative Transition Model for Uncertainty-Aware Robotic Manipulation, Occlusion-Aware Search for Object Retrieval in Clutter, Grasp Pose Detection from a Single RGB Image, DepthGrasp: Depth Completion of Transparent Objects Using Self-Attentive Adversarial Network with Spectral Residual for Grasping, Reactive Long Horizon Task Execution Via Visual Skill and Precondition Models, Efficient and Accurate Candidate Generation for Grasp Pose Detection in SE(3), DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration, Graph-Based Task-Specific Prediction Models for Interactions between Deformable and Rigid Objects, GhostPose*: Multi-View Pose Estimation of Transparent Objects for Robot Hand Grasping, Reinforcement Learning for Vision-Based Object Manipulation with Non-Parametric Policy and Action Primitives, Casting Manipulation of Unknown String by Robot Arm, Deformation Control of a Deformable Object Based on Visual and Tactile Feedback, A Soft Robotic Gripper with an Active Palm and Reconfigurable Fingers for Fully Dexterous In-Hand Manipulation, The Stewart Hand: A Highly Dexterous 6-Degrees-Of-Freedom Manipulator Based on the Stewart-Gough Platform (I), Real-Time Safety and Control of Robotic Manipulators with Torque Saturation in Operational Space, Robot Hand Based on a Spherical Parallel Mechanism for Within-Hand Rotations about a Fixed Point, Learning Compliant Grasping and Manipulation by Teleoperation with Adaptive Force Control, Optimal Scheduling and Non-Cooperative Distributed Model Predictive Control for Multiple Robotic Manipulators, OneVision: Centralized to Distributed Controller Synthesis with Delay Compensation, Robofleet: Open Source Communication and Management for Fleets of Autonomous Robots, Learning Connectivity for Data Distribution in Robot Teams, Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments, Deadlock Prediction and Recovery for Distributed Collision Avoidance with Buffered Voronoi Cells, Scalable Distributed Planning for Multi-Robot, Multi-Target Tracking, State Estimation and Model-Predictive Control for Multi-Robot Handling and Tracking of AGV Motions Using IGPS, Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks, Assembly Sequence Generation for New Objects Via Experience Learned from Similar Object, Combining Learning from Demonstration with Learning by Exploration to Facilitate Contact-Rich Tasks, Learn to Differ: Sim2Real Small Defection Segmentation Network, Control Strategy for Jam and Wedge-Free 3D Precision Insertion of Heavy Objects Suspended with a Multi-Cable Crane, Combining Unsupervised Muscle Co-Contraction Estimation with Bio-Feedback Allows Augmented Kinesthetic Teaching, 3D Reactive Control and Frontier-Based Exploration for Unstructured Environments, Adaptive Terrain Traversability Prediction Based on Multi-Source Transfer Gaussian Processes, Multiclass Terrain Classification Using Sound and Vibration from Mobile Robot Terrain Interaction, Perceptive Autonomous Stair Climbing for Quadrupedal Robots, Trajectory Selection for Power-Over-Tether Atmospheric Sensing UAS, CCRobot-IV: An Obstacle-Free Split-Type Quad-Ducted Propeller-Driven Bridge Stay Cable-Climbing Robot, An Industrial Robot for Firewater Piping Inspection and Mapping, A Mixed Reality Supervision and Telepresence Interface for Outdoor Field Robotics, A Soft Somesthetic Robotic Finger Based on Conductive Working Liquid and an Origami Structure, Extended Tactile Perception: Vibration Sensing through Tools and Grasped Objects, AuraSense: Robot Collision Avoidance by Full Surface Proximity Detection, A Low-Cost Modular System of Customizable, Versatile, and Flexible Tactile Sensor Arrays, Self-Contained Kinematic Calibration of a Novel Whole-Body Artificial Skin for Human-Robot Collaboration, A Multi-Chamber Smart Suction Cup for Adaptive Gripping and Haptic Exploration, A Multi-Axis FBG-Based Tactile Sensor for Gripping in Space, Active Visuo-Tactile Point Cloud Registration for Accurate Pose Estimation of Objects in an Unknown Workspace, High Dynamic Range 6-Axis Force Sensor Employing a Semiconductor-Metallic Foil Strain Gauge Combination, Tactile Scanning for Detecting Micro Bump by Strain-Sensitive Artificial Skin, A Force Recognition System for Distinguishing Click Responses of Various Objects, A Robust Controller for Stable 3D Pinching Using Tactile Sensing, Dynamic Modeling of Hand-Object Interactions Via Tactile Sensing, A Local Filtering Technique for Robot Skin Data, Energy Generating Electronic Skin with Intrinsic Tactile Sensing without Touch Sensors (I), Sensor Selection for Detecting Deviations from a Planned Itinerary, Autonomous Decision-Making with Incomplete Information and Safety Rules Based on Non-Monotonic Reasoning, Automata-Based Optimal Planning with Relaxed Specifications, Probabilistically Guaranteed Satisfaction of Temporal Logic Constraints During Reinforcement Learning, Learning from Demonstrations Using Signal Temporal Logic in Stochastic and Continuous Domains, Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots, A Topological Approach to Finding Coarsely Diverse Paths, Probabilistic Specification Learning for Planning with Safety Constraints, Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic, Safe Linear Temporal Logic Motion Planning in Dynamic Environments, Decentralized Classification with Assume-Guarantee Planning, Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference, Formalizing the Execution Context of Behavior Trees for Runtime Verification of Deliberative Policies, Probabilistic Trajectory Prediction with Structural Constraints, Formalizing Trajectories in Human-Robot Encounters Via Probabilistic STL Inference, Convex Approximation for LTL-Based Planning, Temporal Force Synergies in Human Grasping, Trajectory-Based Split Hindsight Reverse Curriculum Learning, Detecting Grasp Phases and Adaption of Object-Hand Interaction Forces of a Soft Humanoid Hand Based on Tactile Feedback, SpectGRASP: Robotic Grasping by Spectral Correlation, Assessing Grasp Quality Using Local Sensitivity Analysis, Geometry-Based Grasping Pipeline for Bi-Modal Pick and Place, Computing a Task-Dependent Grasp Metric Using Second-Order Cone Programs, Multi-Object Grasping -- Estimating the Number of Objects in a Robotic Grasp, PackerBot: Variable-Sized Product Packing with Heuristic Deep Reinforcement Learning, Geometric Characterization of the Planar Multi-Finger Equilibrium Grasps, Formulation and Validation of an Intuitive Quality Measure for Antipodal Grasp Pose Evaluation, Scooping Manipulation Via Motion Control with a Two-Fingered Gripper and Its Application to Bin Picking, DDGC: Generative Deep Dexterous Grasping in Clutter, Planning Grasps with Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes (I), A Three-Fingered Adaptive Gripper with Multiple Grasping Modes, Dexterous Textile Manipulation Using Electroadhesive Fingers, A Series Elastic, Compact Differential Mechanism: On the Development of Adaptive, Lightweight Robotic Grippers and Hands, Computational Design of Reconfigurable Underactuated Linkages for Adaptive Grippers, A Multi-Modal Robotic Gripper with a Reconfigurable Base: Improving Dexterous Manipulation without Compromising Grasping Efficiency, Grasping with Embedded Synergies through a Reconfigurable Electric Actuation Topology, An Under-Actuated Whippletree Mechanism Gripper Based on Multi-Objective Design Optimization with Auto-Tuned Weights, A Caging Inspired Gripper Using Flexible Fingers and a Movable Palm, The Role of Digit Arrangement in Soft Robotic In-Hand Manipulation, A Dexterous, Reconfigurable Robot Hand Combining Anthropomorphic and Interdigitated Configurations, A Computational Framework for Robot Hand Design Via Reinforcement Learning, Variable-Grasping-Mode Gripper with Different Finger Structures for Grasping Small-Sized Items, Force Control with Friction Compensation in a Pneumatic Gripper, Analysis of Fingertip Force Vector for Pinch-Lifting Gripper with Robust Adaptation to Environments (I), Design and Validation of a Smartphone-Based Haptic Feedback System for Gait Training, Robotic Guidance System for Visually Impaired Users Running Outdoors Using Haptic Feedback, Variable Stiffness Folding Joints for Haptic Feedback.

Learning to model and reconstruct humans in clothing is challenging due to articulation, non-rigid deformation, and varying clothing types and topologies. This approach tries to model the way the human brain processes light and sound into vision and hearing. Hands on Machine Learning with Scikit Learn Keras and TensorFlow 2nd Edition-Ashraf Ony. {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}} "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. With the help of easy-to-follow recipes, this book will take you through the advanced AI and machine learning approaches and algorithms that are required to build smart models for problem-solving.

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