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Found 20 papers, 7 papers with code
Full-Duplex Beyond Self-Interference: The Unlimited Sensing Way
The success of full-stack full-duplex communication systems depends on how effectively one can achieve digital self-interference cancellation (SIC).
Max-Min Fair Energy-Efficient Beam Design for Quantized ISAC LEO Satellite Systems: A Rate-Splitting Approach
Currently, two fundamental challenges, namely, inter-beam interference among users and power limitation at the LEO satellites, limit the full potential of the joint design of sensing and communication.
Model-Based Control with Sparse Neural Dynamics
In this paper, we propose a new framework for integrated model learning and predictive control that is amenable to efficient optimization algorithms.
Learning to Design and Use Tools for Robotic Manipulation
A designer policy is conditioned on task information and outputs a tool design that helps solve the task.
Generalizable Metric Network for Cross-domain Person Re-identification
Additionally, we introduce the Dropout-based Perturbation (DP) module to enhance the generalization capability of the metric network by enriching the sample-pair diversity.
Integrated Sensing and Communications Enabled Low Earth Orbit Satellite Systems
Extreme crowding of electromagnetic spectrum in recent years has led to the challenges in designing sensing and communications systems.
Joint Transmit and Receive Beamforming Design in Full-Duplex Integrated Sensing and Communications
Integrated sensing and communication (ISAC) has been envisioned as a solution to realize the sensing capability required for emerging applications in wireless networks, while efficiently utilizing the available spectral, hardware and energy resources.
Robot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control
Multiple predictive path points are dynamically generated by a deep Markov model optimized using RL approach for robot to track.
It Takes Two: Learning to Plan for Human-Robot Cooperative Carrying
Cooperative table-carrying is a complex task due to the continuous nature of the action and state-spaces, multimodality of strategies, and the need for instantaneous adaptation to other agents.
BEHAVIOR in Habitat 2.0: Simulator-Independent Logical Task Description for Benchmarking Embodied AI Agents
Robots excel in performing repetitive and precision-sensitive tasks in controlled environments such as warehouses and factories, but have not been yet extended to embodied AI agents providing assistance in household tasks.
Inferring Articulated Rigid Body Dynamics from RGBD Video
Being able to reproduce physical phenomena ranging from light interaction to contact mechanics, simulators are becoming increasingly useful in more and more application domains where real-world interaction or labeled data are difficult to obtain.
Unified Style Transfer
Currently, it is hard to compare and evaluate different style transfer algorithms due to chaotic definitions of style and the absence of agreed objective validation methods in the study of style transfer.
RainNet: A Large-Scale Imagery Dataset for Spatial Precipitation Downscaling
Spatial precipitation downscaling is one of the most important meteorological problems.
Image Translation via Fine-grained Knowledge Transfer
Extensive experiments well demonstrate the effectiveness and feasibility of our framework in different image-translation tasks.
RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling
To alleviate these obstacles, we present the first large-scale spatial precipitation downscaling dataset named RainNet, which contains more than $62, 400$ pairs of high-quality low/high-resolution precipitation maps for over $17$ years, ready to help the evolution of deep learning models in precipitation downscaling.
CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute Editing
In contrast to great success of memory-consuming face editing methods at a low resolution, to manipulate high-resolution (HR) facial images, i. e., typically larger than 7682 pixels, with very limited memory is still challenging.
Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression
It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples.
Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)
Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples.
Unsupervised Pool-Based Active Learning for Linear Regression
So, it is desirable to be able to select the optimal samples to label, so that a good machine learning model can be trained from a minimum amount of labeled data.
Physics-based Simulation of Continuous-Wave LIDAR for Localization, Calibration and Tracking
Light Detection and Ranging (LIDAR) sensors play an important role in the perception stack of autonomous robots, supplying mapping and localization pipelines with depth measurements of the environment.
