Regression tasks, which involve predicting continuous numeric values, have traditionally relied on numeric heads such as Gaussian parameterizations or pointwise tensor projections. These traditional approaches have strong distributional assumption requirements, require a lot of labeled data, and tend to break down when modeling advanced numerical distributions. New research on large language models introduces a different… →

Transformer-based language models process text by analyzing word relationships rather than reading in order. They use attention mechanisms to focus on keywords, but handling longer text is challenging. The Softmax function, which distributes attention, weakens as the input size grows, causing attention fading. This reduces the model’s focus on important words, making it harder to… →

Neural Ordinary Differential Equations are significant in scientific modeling and time-series analysis where data changes every other moment. This neural network-inspired framework models continuous-time dynamics with a continuous transformation layer governed by differential equations, which sets them apart from vanilla neural nets. While Neural ODEs have cracked down on handling dynamic series efficiently, cost-effective gradient… →

Directed graphs are crucial in modeling complex real-world systems, from gene regulatory networks and flow networks to stochastic processes and graph metanetworks. Representing these directed graphs presents significant challenges, particularly in causal reasoning applications where understanding cause-and-effect relationships is paramount. Current methodologies face a fundamental limitation in balancing directional and distance information within the representation… →