Search Results for author:
Found 22 papers, 9 papers with code
LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning
Through a series of simulations in a box-packing domain, we quantitatively demonstrate the effectiveness of LLM^3 in solving TAMP problems and the efficiency in selecting action parameters.
GeoGalactica: A Scientific Large Language Model in Geoscience
To our best knowledge, it is the largest language model for the geoscience domain.
A Multimodal Ecological Civilization Pattern Recommendation Method Based on Large Language Models and Knowledge Graph
However, the current representative recommendation methods are not suitable for recommending ecological civilization patterns in a geographical context.
Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks
By leveraging operational data as a foundation for the agent's actions, we enhance the explainability of the agent's actions, foster more robust recommendations, and minimize error.
Unveiling Optimal SDG Pathways: An Innovative Approach Leveraging Graph Pruning and Intent Graph for Effective Recommendations
However, current recommendation algorithms in the field of computer science fall short in adequately addressing the spatial heterogeneity related to environment and sparsity of regional historical interaction data, which limits their effectiveness in recommending sustainable development patterns.
Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network
Liver tumor segmentation and classification are important tasks in computer aided diagnosis.
Asymmetric Patch Sampling for Contrastive Learning
Asymmetric appearance between positive pair effectively reduces the risk of representation degradation in contrastive learning.
T-NGA: Temporal Network Grafting Algorithm for Learning to Process Spiking Audio Sensor Events
This work proposes a self-supervised method called Temporal Network Grafting Algorithm (T-NGA), which grafts a recurrent network pretrained on spectrogram features so that the network works with the cochlea event features.
Evolved Massive Stars at Low-metallicity IV. Using 1.6 $μ$m "H-bump" to identify red supergiant stars: a case study of NGC 6822
Further separating RSG candidates from the rest of the LSG candidates is done by using semi-empirical criteria on NIR CMDs and resulted in 323 RSG candidates.
Solar and Stellar Astrophysics Astrophysics of Galaxies
Multi Receptive Field Network for Semantic Segmentation
Semantic segmentation is one of the key tasks in computer vision, which is to assign a category label to each pixel in an image.
Ranked #20 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
The Bright Side and the Dark Side of Hybrid Organic Inorganic Perovskites
The previously developed bistable amphoteric native defect (BAND) model is used for a comprehensive explanation of the unique photophysical properties and for understanding the remarkable performance of perovskites as photovoltaic materials.
Applied Physics Materials Science
Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis
In this paper, we propose a deep neural network based model to predict the time evolution of field values in transient electrodynamics.
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks
Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user's mental state.
Bayesian Inference
Explainable Artificial Intelligence (XAI)
+1
A Bayesian Finite Mixture Model with Variable Selection for Data with Mixed-type Variables
To address these challenges, we propose a Bayesian finite mixture model to simultaneously conduct variable selection, account for biomarker LOD and obtain clustering results.
Hybrid Density- and Partition-based Clustering Algorithm for Data with Mixed-type Variables
However, algorithms that can cluster data with mixed variable types (continuous and categorical) remain limited, despite the abundance of data with mixed types particularly in the medical field.
VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning
One of the main challenges of advancing task-oriented learning such as visual task planning and reinforcement learning is the lack of realistic and standardized environments for training and testing AI agents.
Differentially Private Generative Adversarial Network
Generative Adversarial Network (GAN) and its variants have recently attracted intensive research interests due to their elegant theoretical foundation and excellent empirical performance as generative models.
Collaborative Deep Reinforcement Learning
Motivated by human collaborative learning, in this paper we propose a collaborative deep reinforcement learning (CDRL) framework that performs adaptive knowledge transfer among heterogeneous learning agents.
An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition
We propose in this paper an atomic action-based Bayesian model that constructs Allen's interval relation networks to characterize complex activities with structural varieties in a probabilistic generative way: By introducing latent variables from the Chinese restaurant process, our approach is able to capture all possible styles of a particular complex activity as a unique set of distributions over atomic actions and relations.
Multispectral Deep Neural Networks for Pedestrian Detection
Multispectral pedestrian detection is essential for around-the-clock applications, e. g., surveillance and autonomous driving.
Visual Tracking via Reliable Memories
In this paper, we propose a novel visual tracking framework that intelligently discovers reliable patterns from a wide range of video to resist drift error for long-term tracking tasks.
A New Framework for Sign Language Recognition based on 3D Handshape Identification and Linguistic Modeling
Current approaches to sign recognition by computer generally have at least some of the following limitations: they rely on laboratory conditions for sign production, are limited to a small vocabulary, rely on 2D modeling (and therefore cannot deal with occlusions and off-plane rotations), and/or achieve limited success.
