Search Results for author:
Found 31 papers, 4 papers with code
Physical Property Understanding from Language-Embedded Feature Fields
Can computers perceive the physical properties of objects solely through vision?
Distributed Policy Gradient for Linear Quadratic Networked Control with Limited Communication Range
This paper proposes a scalable distributed policy gradient method and proves its convergence to near-optimal solution in multi-agent linear quadratic networked systems.
Exploiting Multipath Information for Integrated Localization and Sensing via PHD Filtering
Accurate localization and perception are pivotal for enhancing the safety and reliability of vehicles.
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense
We propose an active defense strategy, where agents automatically reduce the impact of potentially harmful messages on the final decision.
Diffusion Posterior Sampling for Nonlinear CT Reconstruction
This technique is attractive since it permits a one-time, unsupervised training of a CT prior; which can then be incorporated with an arbitrary data model.
Towards Quantum Federated Learning
This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL.
A Deep Learning Approach for Generating Soft Range Information from RF Data
Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements.
A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform
Localization systems based on ultra-wide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of non-line-of-sight (NLOS) errors.
Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification
In particular, we present a Bayesian model for the generative process of the received waveform composed by latent variables for both range-related features and environment semantics.
Deep GEM-Based Network for Weakly Supervised UWB Ranging Error Mitigation
Ultra-wideband (UWB)-based techniques, while becoming mainstream approaches for high-accurate positioning, tend to be challenged by ranging bias in harsh environments.
Generalized Expectation Maximization Framework for Blind Image Super Resolution
Learning-based methods for blind single image super resolution (SISR) conduct the restoration by a learned mapping between high-resolution (HR) images and their low-resolution (LR) counterparts degraded with arbitrary blur kernels.
Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables
Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions.
Injecting Image Details into CLIP's Feature Space
Although CLIP-like Visual Language Models provide a functional joint feature space for image and text, due to the limitation of the CILP-like model's image input size (e. g., 224), subtle details are lost in the feature representation if we input high-resolution images (e. g., 2240).
Distributed Scheduling at Non-Signalized Intersections with Mixed Cooperative and Non-Cooperative Vehicles
Intersection management with mixed cooperative and non-cooperative vehicles is crucial in next-generation transportation systems.
CoCAtt: A Cognitive-Conditioned Driver Attention Dataset (Supplementary Material)
In addition, the attention data in our dataset is captured in both manual and autopilot modes using eye-tracking devices of different resolutions.
Learning Dynamic View Synthesis With Few RGBD Cameras
Experiments on the HTI dataset show that our method outperforms the baseline per-frame image fidelity and spatial-temporal consistency.
A Minimax Framework for Two-Agent Scheduling with Inertial Constraints
Autonomous agents are promising in applications such as intelligent transportation and smart manufacturing, and scheduling of agents has to take their inertial constraints into consideration.
CoCAtt: A Cognitive-Conditioned Driver Attention Dataset
In addition, the attention data in our dataset is captured in both manual and autopilot modes using eye-tracking devices of different resolutions.
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning
In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL).
Model Predictive Control
Multi-agent Reinforcement Learning
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AdaSpeech 3: Adaptive Text to Speech for Spontaneous Style
While recent text to speech (TTS) models perform very well in synthesizing reading-style (e. g., audiobook) speech, it is still challenging to synthesize spontaneous-style speech (e. g., podcast or conversation), mainly because of two reasons: 1) the lack of training data for spontaneous speech; 2) the difficulty in modeling the filled pauses (um and uh) and diverse rhythms in spontaneous speech.
Effectively Leveraging Attributes for Visual Similarity
This enables our model to identify that two images contain the same attribute, but can have it deemed irrelevant (e. g., due to fine-grained differences between them) and ignored for measuring similarity between the two images.
AdaSpeech 2: Adaptive Text to Speech with Untranscribed Data
In adaptation, we use untranscribed speech data for speech reconstruction and only fine-tune the TTS decoder.
Building Mental Models through Preview of Autopilot Behaviors
Effective human-vehicle collaboration requires an appropriate un-derstanding of vehicle behavior for safety and trust.
AutoPreview: A Framework for Autopilot Behavior Understanding
The behavior of self driving cars may differ from people expectations, (e. g. an autopilot may unexpectedly relinquish control).
Generalized Maximum Entropy for Supervised Classification
The maximum entropy principle advocates to evaluate events' probabilities using a distribution that maximizes entropy among those that satisfy certain expectations' constraints.
To Explain or Not to Explain: A Study on the Necessity of Explanations for Autonomous Vehicles
Explainable AI, in the context of autonomous systems, like self-driving cars, has drawn broad interests from researchers.
SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking
We present a novel self quality evaluation metric SQE for parameters optimization in the challenging yet critical multi-object tracking task.
Can AI decrypt fashion jargon for you?
Finally, we trained a deep learning model that can explicitly predict and explain high level fashion concepts in a product image with its low level and domain specific fashion features.
A Classification Framework for Partially Observed Dynamical Systems
We present a general framework for classifying partially observed dynamical systems based on the idea of learning in the model space.
Deep Learning Stock Volatility with Google Domestic Trends
We have applied a Long Short-Term Memory neural network to model S&P 500 volatility, incorporating Google domestic trends as indicators of the public mood and macroeconomic factors.
Computational Finance
Variational Inference for Diffusion Processes
Diffusion processes are a family of continuous-time continuous-state stochastic processes that are in general only partially observed.
