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Found 5 papers, 1 papers with code
Marco-LLM: Bridging Languages via Massive Multilingual Training for Cross-Lingual Enhancement
We have collected a substantial amount of multilingual data for several low-resource languages and conducted extensive continual pre-training using the Qwen2 models.
Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
Currently OpenAI o1 sparks a surge of interest in the study of large reasoning models (LRM).
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies
In recent years, spiking neural networks (SNNs) have attracted substantial interest due to their potential to replicate the energy-efficient and event-driven processing of biological neurons.
Understanding the Functional Roles of Modelling Components in Spiking Neural Networks
By designing and evaluating several variants of the classic model, we systematically investigate the functional roles of key modelling components, leakage, reset, and recurrence, in leaky integrate-and-fire (LIF) based SNNs.
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks
Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical compounds, and biological systems.
