Search Results for author:
Found 17 papers, 7 papers with code
DNA 1.0 Technical Report
In this report, we present DNA 1. 0 8B Instruct, a state-of-the-art bilingual language model optimized for Korean and English language tasks.
PR-CARA: Proactive V2X Resource Allocation with Extended 1-Stage SCI and Deep Learning-based Sensing Matrix Estimator
Simulation results demonstrate that the proposed deep-learning-based proactive resource allocation algorithm, with the extended 1-stage SCI system, reduces packet collisions and improves the transmission signal-to-interference-plus-noise ratio (SINR), thereby significantly enhancing communication reliability compared to the benchmark resource allocation algorithm.
Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments
In this work, we present Robot Utility Models (RUMs), a framework for training and deploying zero-shot robot policies that can directly generalize to new environments without any finetuning.
Behavior Generation with Latent Actions
Unlike language or image generation, decision making requires modeling actions - continuous-valued vectors that are multimodal in their distribution, potentially drawn from uncurated sources, where generation errors can compound in sequential prediction.
Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement
Reinforcement learning (RL) often faces the challenges of uninformed search problems where the agent should explore without access to the domain knowledge such as characteristics of the environment or external rewards.
Detection of Pedestrian Turning Motions to Enhance Indoor Map Matching Performance
In summary, our research contributes to the development of a more accurate and reliable pedestrian navigation system by leveraging smartphone IMU data and advanced algorithms for turn detection in indoor environments.
Reciprocal Attention Mixing Transformer for Lightweight Image Restoration
To address these problems, we propose a lightweight IR network, Reciprocal Attention Mixing Transformer (RAMiT).
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
As previous representations for reinforcement learning cannot effectively incorporate a human-intuitive understanding of the 3D environment, they usually suffer from sub-optimal performances.
Outcome-directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal Generation
Current reinforcement learning (RL) often suffers when solving a challenging exploration problem where the desired outcomes or high rewards are rarely observed.
Restoration of Hand-Drawn Architectural Drawings Using Latent Space Mapping With Degradation Generator
This work presents the restoration of drawings of wooden built heritage.
DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning
Hierarchical Reinforcement Learning (HRL) has made notable progress in complex control tasks by leveraging temporal abstraction.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud
Moreover, even if the parameters are well adjusted, a partial under-segmentation problem can still emerge, which implies ground segmentation failures in some regions.
PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry
In this paper, a robust ground-optimized LiDAR odometry framework is proposed to facilitate the study to check the effect of ground segmentation on LiDAR SLAM based on the state-of-the-art (SOTA) method.
High-contrast, speckle-free, true 3D holography via binary CGH optimization
Here, we propose the practical solution to realize speckle-free, high-contrast, true 3D holography by combining random-phase, temporal multiplexing, binary holography, and binary optimization.
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction
The building sector consumes the largest energy in the world, and there have been considerable research interests in energy consumption and comfort management of buildings.
Low-Power Computer Vision: Status, Challenges, Opportunities
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
2018 Low-Power Image Recognition Challenge
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
