Search Results for author:
Found 9 papers, 5 papers with code
JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents.
GROOT: Learning to Follow Instructions by Watching Gameplay Videos
We propose to follow reference videos as instructions, which offer expressive goal specifications while eliminating the need for expensive text-gameplay annotations.
Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
We investigate the challenge of task planning for multi-task embodied agents in open-world environments.
Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction
We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment for developing human-level multi-task agents.
Automatic Relation-aware Graph Network Proliferation
However, the currently used graph search space overemphasizes learning node features and neglects mining hierarchical relational information.
Ranked #2 on
Link Prediction
on TSP/HCP Benchmark set
IR-GAN: Image Manipulation with Linguistic Instruction by Increment Reasoning
Conditional image generation is an active research topic including text2image and image translation.
Conditional Image Generation
Generative Adversarial Network
+1
DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing
To address these limitations, we design a Dynamic Style Manipulation Network (DyStyle) whose structure and parameters vary by input samples, to perform nonlinear and adaptive manipulation of latent codes for flexible and precise attribute control.
Edge-featured Graph Neural Architecture Search
Recently, researchers study neural architecture search (NAS) to reduce the dependence of human expertise and explore better GNN architectures, but they over-emphasize entity features and ignore latent relation information concealed in the edges.
Rethinking Graph Neural Architecture Search from Message-passing
Inspired by the strong searching capability of neural architecture search (NAS) in CNN, this paper proposes Graph Neural Architecture Search (GNAS) with novel-designed search space.
