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Found 5 papers, 2 papers with code
Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making
Resolving the dichotomy between the human-like yet constrained reasoning processes of Cognitive Architectures and the broad but often noisy inference behavior of Large Language Models (LLMs) remains a challenging but exciting pursuit, for enabling reliable machine reasoning capabilities in production systems.
LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations
The extraction of Metal-Organic Frameworks (MOFs) synthesis conditions from literature text has been challenging but crucial for the logical design of new MOFs with desirable functionality.
BARET : Balanced Attention based Real image Editing driven by Target-text Inversion
Image editing approaches with diffusion models have been rapidly developed, yet their applicability are subject to requirements such as specific editing types (e. g., foreground or background object editing, style transfer), multiple conditions (e. g., mask, sketch, caption), and time consuming fine-tuning of diffusion models.
Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences
Learning causal structure among event types from discrete-time event sequences is a particularly important but challenging task.
THP: Topological Hawkes Processes for Learning Causal Structure on Event Sequences
We further propose a causal structure learning method on THP in a likelihood framework.
