Search Results for author:
Found 10 papers, 8 papers with code
AgentGym: Evolving Large Language Model-based Agents across Diverse Environments
Building generalist agents that can handle diverse tasks and evolve themselves across different environments is a long-term goal in the AI community.
VANP: Learning Where to See for Navigation with Self-Supervised Vision-Action Pre-Training
However, most robotic visual navigation methods rely on deep learning models pre-trained on vision tasks, which prioritize salient objects -- not necessarily relevant to navigation and potentially misleading.
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
In this paper, we propose R$^3$: Learning Reasoning through Reverse Curriculum Reinforcement Learning (RL), a novel method that employs only outcome supervision to achieve the benefits of process supervision for large language models.
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG
An accurate and efficient epileptic seizure onset detection can significantly benefit patients.
The Rise and Potential of Large Language Model Based Agents: A Survey
Many efforts have been made to develop intelligent agents, but they mainly focus on advancement in algorithms or training strategies to enhance specific capabilities or performance on particular tasks.
RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction
However, general matching methods lack explicit modeling of the above matching pattern.
Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language Model
Pretrained language models have achieved remarkable success in various natural language processing tasks.
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER
In this work, we conduct an empirical study on the "Unlabeled Entity Problem" and find that it leads to severe confusion between "O" and entities, decreasing class discrimination of old classes and declining the model's ability to learn new classes.
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents
Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation.
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework
Although the Trajectory Prediction (TP) model has achieved great success in computer vision and robotics fields, its architecture and training scheme design rely on heavy manual work and domain knowledge, which is not friendly to common users.
