Search Results for author:
Found 13 papers, 2 papers with code
Topology Data Analysis-based Error Detection for Semantic Image Transmission with Incremental Knowledge-based HARQ
In particular, different from the conventional CRC, we introduce a topological data analysis (TDA)-based error detection method, which capably digs out the inner topological and geometric information of images, so as to capture semantic information and determine the necessity for re-transmission.
SheetAgent: A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models
To bridge the gap with the real-world requirements, we introduce $\textbf{SheetRM}$, a benchmark featuring long-horizon and multi-category tasks with reasoning-dependent manipulation caused by real-life challenges.
Enhancing Robotic Manipulation with AI Feedback from Multimodal Large Language Models
Recently, there has been considerable attention towards leveraging large language models (LLMs) to enhance decision-making processes.
DiffuserLite: Towards Real-time Diffusion Planning
Diffusion planning has been recognized as an effective decision-making paradigm in various domains.
Interplay of Semantic Communication and Knowledge Learning
In the swiftly advancing realm of communication technologies, Semantic Communication (SemCom), which emphasizes knowledge understanding and processing, has emerged as a hot topic.
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model
Aligning agent behaviors with diverse human preferences remains a challenging problem in reinforcement learning (RL), owing to the inherent abstractness and mutability of human preferences.
MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL
Recently, diffusion model shines as a promising backbone for the sequence modeling paradigm in offline reinforcement learning(RL).
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
For example, AdaptDiffuser not only outperforms the previous art Diffuser by 20. 8% on Maze2D and 7. 5% on MuJoCo locomotion, but also adapts better to new tasks, e. g., KUKA pick-and-place, by 27. 9% without requiring additional expert data.
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning
This paper addresses such a challenge by Decomposed Mutual INformation Optimization (DOMINO) for context learning, which explicitly learns a disentangled context to maximize the mutual information between the context and historical trajectories, while minimizing the state transition prediction error.
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model
Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks.
A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry
The bin packing problem exists widely in real logistic scenarios (e. g., packing pipeline, express delivery), with its goal to improve the packing efficiency and reduce the transportation cost.
Adaptive Large Neighborhood Search for Circle Bin Packing Problem
We address a new variant of packing problem called the circle bin packing problem (CBPP), which is to find a dense packing of circle items to multiple square bins so as to minimize the number of used bins.
Internet of Intelligence: The Collective Advantage for Advancing Communications and Intelligence
Afterwards, we highlight the potential huge impact of CI on both communications and intelligence.
