7 papers
Reversing Large Language Models for Efficient Training and Fine-Tuning
Eshed Gal, Moshe Eliasof, Javier Turek +3
Large Language Models (LLMs) are known for their expensive and time-consuming training. Thus, oftentimes, LLMs are fine-tuned to address a specific task, given the pretrained weigh…
TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows
Moshe Eliasof, Eldad Haber, Carola-Bibiane Schönlieb
We introduce TANGO -- a dynamical systems inspired framework for graph representation learning that governs node feature evolution through a learned energy landscape and its associ…
Synthetic Geology: Structural Geology Meets Deep Learning
Simon Ghyselincks, Valeriia Okhmak, Stefano Zampini +3
Reconstructing the structural geology and mineral composition of the first few kilometers of the Earth's subsurface from sparse or indirect surface observations remains a long-stan…
Graph Flow Matching: Enhancing Image Generation with Neighbor-Aware Flow Fields
Md Shahriar Rahim Siddiqui, Moshe Eliasof, Eldad Haber
Flow matching casts sample generation as learning a continuous-time velocity field that transports noise to data. Existing flow matching networks typically predict each point's vel…
Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data
Siddharth Rout, Eldad Haber, Stéphane Gaudreault
The modeling of dynamical systems is essential in many fields, but applying machine learning techniques is often challenging due to incomplete or noisy data. This study introduces…
Machine Learning for Airborne Electromagnetic Data Inversion: a Bootstrapped Approach
Ophir Greif, Bas Peters, Michael S. McMillan +2
Aircraft-based surveying to collect airborne electromagnetic data is a key method to image large swaths of the Earth's surface in pursuit of better knowledge of aquifer systems. De…