3 accepted papers
Task arithmetic is a straightforward yet highly effective strategy for model merging, enabling the resultant model to exhibit multi-task capabilities. Recent research indicates that models demonstrating linearity enhance the performance of task arithmetic. In contrast to existing methods that rely o…
Recent advancements in large language models (LLMs) have shown promising ability to perform commonsense reasoning, bringing machines closer to human-like understanding. However, deciphering the internal reasoning processes of LLMs remains challenging due to the complex interdependencies among genera…
Brain-machine interfaces (BMIs) have been an important research area in closed-loop neuroscience and neuroengineering. In real-time neuroscience applications, many issues require special consideration, such as trial variability, spike sorting noise or multi-unit activity. For a BMI application of de…