The rise and potential of large language model based agents: A survey
For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds
human intelligence. AI agents, which are artificial entities capable of sensing the …
human intelligence. AI agents, which are artificial entities capable of sensing the …
Foundation Models Defining a New Era in Vision: a Survey and Outlook
Vision systems that see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …
fundamental to understanding our world. The complex relations between objects and their …
Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions
Autonomous driving has achieved significant milestones in research and development over
the last two decades. There is increasing interest in the field as the deployment of …
the last two decades. There is increasing interest in the field as the deployment of …
Slca: Slow learner with classifier alignment for continual learning on a pre-trained model
The goal of continual learning is to improve the performance of recognition models in
learning sequentially arrived data. Although most existing works are established on the …
learning sequentially arrived data. Although most existing works are established on the …
Incorporating neuro-inspired adaptability for continual learning in artificial intelligence
Continual learning aims to empower artificial intelligence with strong adaptability to the real
world. For this purpose, a desirable solution should properly balance memory stability with …
world. For this purpose, a desirable solution should properly balance memory stability with …
Hierarchical decomposition of prompt-based continual learning: Rethinking obscured sub-optimality
Prompt-based continual learning is an emerging direction in leveraging pre-trained
knowledge for downstream continual learning, and has almost reached the performance …
knowledge for downstream continual learning, and has almost reached the performance …
Survey on large language model-enhanced reinforcement learning: Concept, taxonomy, and methods
With extensive pretrained knowledge and high-level general capabilities, large language
models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in …
models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in …
Ranpac: Random projections and pre-trained models for continual learning
Continual learning (CL) aims to incrementally learn different tasks (such as classification) in
a non-stationary data stream without forgetting old ones. Most CL works focus on tackling …
a non-stationary data stream without forgetting old ones. Most CL works focus on tackling …