2 citations · 2 across the 26 of their papers we have counts for
29 papers
GarageWorks: A Shared Testbench Abstraction for Software Simulation and FPGA Testing
Kazutomo Yoshii
GarageWorks is a lightweight FPGA testing framework that enables the same cocotb testbench to be used for both software RTL simulation and FPGA-accelerated hardware simulation. A s…
GeoCFM: Positive-Only Conditional Flow Matching for Mineral Occurrence Sampling
Moshe Eliasof, Eldad Haber
Critical mineral discovery is a positive-only problem: deposits are observed as sparse locations, while unlabeled regions are not reliable negatives, and similar geophysical signat…
Score-Based Stabilization for Time-Dependent Problems
Eshed Gal, Eldad Haber, Uri Ascher
We propose a score-based stabilization framework for numerical simulation of partial differential equations, in which a learned score model defines a stabilization operator applied…
Target-Aware Data Augmentation for SAT Prediction
Eshed Gal, Uri Ascher, Eldad Haber
Learning-based approaches to NP-hard problems have shown increasing promise, but their progress is fundamentally constrained by the high cost of generating labeled training data. I…
Conservative Flows: A New Paradigm of Generative Models
Eshed Gal, Md Shahriar Rahim Siddiqui, Moshe Eliasof +1
Modern generative modeling is dominated by transport from a noise prior to data. We propose an alternative paradigm in which generation is performed by a discrete stochastic dynami…
PDE-SSM: A Spectral State Space Approach to Spatial Mixing in Diffusion Transformers
Eshed Gal, Moshe Eliasof, Siddharth Rout +1
The success of vision transformers-especially for generative modeling-is limited by the quadratic cost and weak spatial inductive bias of self-attention. We propose PDE-SSM, a spat…
Probabilistic Gaussian Homotopy: A Probability-Space Continuation Framework for Nonconvex Optimization
Eshed Gal, Samy Wu Fung, Eldad Haber
We introduce Probabilistic Gaussian Homotopy (PGH), a probability-space continuation framework for nonconvex optimization. Unlike classical Gaussian homotopy, which smooths the obj…
Preconditioned Flow Matching
Shadab Ahamed, Eshed Gal, Md Shahriar Rahim Siddiqui +3
Flow matching (FM) learns vector fields by regressing stochastic velocity targets along intermediate distributions . We identify a geometric optimization bottleneck in this re…
Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting
Carlos A. Pereira, Stéphane Gaudreault, Valentin Dallerit +9
Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection, diffusive mixing, thermodynamic…
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…
Fast and Flexible Probabilistic Forecasting of Dynamical Systems using Flow Matching and Physical Perturbation
Siddharth Rout, Eldad Haber, Stephane Gaudreault
Learning dynamical systems from incomplete or noisy data is inherently ill-posed, as a single observation may correspond to multiple plausible futures. While physics-based ensemble…
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…
Towards Efficient Training of Graph Neural Networks: A Multiscale Approach
Eshed Gal, Moshe Eliasof, Carola-Bibiane Schönlieb +3
Graph Neural Networks (GNNs) have become powerful tools for learning from graph-structured data, finding applications across diverse domains. However, as graph sizes and connectivi…
Inversion of Magnetic Data using Learned Dictionaries and Scale Space
Shadab Ahamed, Simon Ghyselincks, Pablo Chang Huang Arias +5
Magnetic data inversion is an important tool in geophysics, used to infer subsurface magnetic susceptibility distributions from surface magnetic field measurements. This inverse pr…
Iterative Flow Matching: Path Correction and Gradual Refinement for Enhanced Generative Modeling
Eldad Haber, Shadab Ahamed, Md. Shahriar Rahim Siddiqui +2
Generative models for image generation are now commonly used for a wide variety of applications, ranging from guided image generation for entertainment to solving inverse problems.…
Multiscale Training of Convolutional Neural Networks
Shadab Ahamed, Niloufar Zakariaei, Eldad Haber +1
Training convolutional neural networks (CNNs) on high-resolution images is often bottlenecked by the cost of evaluating gradients of the loss on the finest spatial mesh. To address…
DAWN-FM: Data-Aware and Noise-Informed Flow Matching for Solving Inverse Problems
Shadab Ahamed, Eldad Haber
Inverse problems, which involve estimating parameters from incomplete or noisy observations, arise in various fields such as medical imaging, geophysics, and signal processing. The…
Learning Regularization for Graph Inverse Problems
Moshe Eliasof, Md Shahriar Rahim Siddiqui, Carola-Bibiane Schönlieb +1
In recent years, Graph Neural Networks (GNNs) have been utilized for various applications ranging from drug discovery to network design and social networks. In many applications, i…
Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data
Bas Peters, Eldad Haber, Keegan Lensink
The large spatial/temporal/frequency scale of geoscience and remote-sensing datasets causes memory issues when using convolutional neural networks for (sub-) surface data segmentat…
Inverting airborne electromagnetic data with machine learning
Michael S. McMillan, Bas Peters, Ophir Greif +2
This study focuses on inverting time-domain airborne electromagnetic data in 2D by training a neural-network to understand the relationship between data and conductivity, thereby r…