Search Results for author:
Found 9 papers, 3 papers with code
SRTFD: Scalable Real-Time Fault Diagnosis through Online Continual Learning
Fault diagnosis (FD) is essential for maintaining operational safety and minimizing economic losses by detecting system abnormalities.
CANDY: A Benchmark for Continuous Approximate Nearest Neighbor Search with Dynamic Data Ingestion
Although numerous AKNN algorithms and benchmarks have been developed recently to evaluate their effectiveness, the dynamic nature of real-world data presents significant challenges that existing benchmarks fail to address.
StreamPrompt: Learnable Prompt-guided Data Selection for Efficient Stream Learning
Moreover, StreamPrompt introduces Prompt Attunement, a mechanism that enhances the efficiency of prompt learning.
Online Continual Knowledge Learning for Language Models
In this paper, we introduce a novel problem in the realm of continual learning: Online Continual Knowledge Learning (OCKL).
Harnessing Scalable Transactional Stream Processing for Managing Large Language Models [Vision]
Large Language Models (LLMs) have demonstrated extraordinary performance across a broad array of applications, from traditional language processing tasks to interpreting structured sequences like time-series data.
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning
In this paper, we propose a scalable value-decomposition exploration (SVDE) method, which includes a scalable training mechanism, intrinsic reward design, and explorative experience replay.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
MolMiner: You only look once for chemical structure recognition
Due to a backlog of decades and an increasing amount of these printed literature, there is a high demand for the translation of printed depictions into machine-readable formats, which is known as Optical Chemical Structure Recognition (OCSR).
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning
However, due to the slow sample collection and poor sample exploration, there are still some problems in multi-agent reinforcement learning, such as unstable model iteration and low training efficiency.
A Framework for Fast Polarity Labelling of Massive Data Streams
Many of the existing sentiment analysis techniques are based on supervised learning, and they demand the availability of valuable training datasets to train their models.
