From the 1 of 57 linked papers with an AI index.
40 papers · 1 filter
M+Adam: Low-Precision Training via Additive-Multiplicative Optimization
Xiaoyuan Liang, Sebastian Loeschcke, Mads Toftrup +1
The paper introduces M+Adam, an optimizer that blends additive and multiplicative updates to enable stable low‑precision training of large language models without keeping high‑prec…
BRIDGE: Building Representations In Domain Guided Program Synthesis
Robert Joseph George, Carson Eisenach, Udaya Ghai +3
Large language models can generate plausible code, but remain brittle for formal verification in proof assistants such as Lean. A central scalability challenge is that verified syn…
Mechanistic Interpretability with Sparse Autoencoder Neural Operators
Bahareh Tolooshams, Ailsa Shen, Anima Anandkumar
We introduce sparse autoencoder neural operators (SAE-NOs), a new class of sparse autoencoders that operate in function spaces rather than fixed-dimensional Euclidean representatio…
Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage
Xin Ju, Jiachen Yao, Anima Anandkumar +2
Accurate characterization of subsurface flow is critical for Carbon Capture and Storage (CCS) but remains challenged by the ill-posed nature of inverse problems with sparse observa…
Self-Supervised Learning via Flow-Guided Neural Operator on Time-Series Data
Duy Nguyen, Jiachen Yao, Jiayun Wang +2
Self-supervised learning (SSL) is a powerful paradigm for learning from unlabeled time-series data. However, popular methods such as masked autoencoders (MAEs) rely on reconstructi…
Decoupled Diffusion Sampling for Inverse Problems on Function Spaces
Thomas Y. L. Lin, Jiachen Yao, Lufang Chiang +2
We propose a data-efficient, physics-aware generative framework in function space for inverse PDE problems. Existing plug-and-play diffusion posterior samplers represent physics im…