4 accepted papers
Current evaluation paradigms for generative models rely heavily on retrieval-based metrics such as exact match accuracy, creating a bottleneck particularly in domains requiring scientific discovery and creative reasoning. These metrics penalize any deviation from the training distribution, treating …
Solutions to time-varying problems are crucial for research areas such as predicting changes in human body shape over time. While recurrent neural networks have made significant advancements in this field, their reliance on centralized processing has led to challenges such as model silos and data is…
Inertial navigation enables self-contained localization using only Inertial Measurement Units (IMUs), making it widely applicable in various domains such as navigation, augmented reality, and robotics. However, existing methods suffer from drift accumulation due to the sensor noise and difficulty ca
The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these l…