18 citations · 69 across the 60 of their papers we have counts for
19 papers · 1 filter
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…
Graph Neural Regularizers for PDE Inverse Problems
William Lauga, James Rowbottom, Alexander Denker +3
We present a framework for solving a broad class of ill-posed inverse problems governed by partial differential equations (PDEs), where the target coefficients of the forward opera…
On The Expressive Power of GNN Derivatives
Yam Eitan, Moshe Eliasof, Yoav Gelberg +3
Despite significant advances in Graph Neural Networks (GNNs), their limited expressivity remains a fundamental challenge. Research on GNN expressivity has produced many expressive…
Revisiting Node Affinity Prediction in Temporal Graphs
Or Feldman, Krishna Sri Ipsit Mantri, Moshe Eliasof +1
Node affinity prediction is a common task that is widely used in temporal graph learning with applications in social and financial networks, recommender systems, and more. Recent w…
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…
Understanding and Improving Laplacian Positional Encodings For Temporal GNNs
Yaniv Galron, Fabrizio Frasca, Haggai Maron +2
Temporal graph learning has applications in recommendation systems, traffic forecasting, and social network analysis. Although multiple architectures have been introduced, progress…
Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks
Ali Hariri, Álvaro Arroyo, Alessio Gravina +6
ChebNet, one of the earliest spectral GNNs, has largely been overshadowed by Message Passing Neural Networks (MPNNs), which gained popularity for their simplicity and effectiveness…
Improving the Effective Receptive Field of Message-Passing Neural Networks
Shahaf E. Finder, Ron Shapira Weber, Moshe Eliasof +2
Message-Passing Neural Networks (MPNNs) have become a cornerstone for processing and analyzing graph-structured data. However, their effectiveness is often hindered by phenomena su…
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…
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
Andrea Ceni, Alessio Gravina, Claudio Gallicchio +3
The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph State-Space Models (GSSMs). However, exi…
One-Step Offline Distillation of Diffusion-based Models via Koopman Modeling
Nimrod Berman, Ilan Naiman, Moshe Eliasof +2
Diffusion-based generative models have demonstrated exceptional performance, yet their iterative sampling procedures remain computationally expensive. A prominent strategy to mitig…
FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks
Or Feldman, Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb +2
Aggregating temporal signals from historic interactions is a key step in future link prediction on dynamic graphs. However, incorporating long histories is resource-intensive. Henc…
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…
Towards Invariance to Node Identifiers in Graph Neural Networks
Maya Bechler-Speicher, Moshe Eliasof, Carola-Bibiane Schonlieb +2
Message-Passing Graph Neural Networks (GNNs) are known to have limited expressive power, due to their message passing structure. One mechanism for circumventing this limitation is…
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.…
DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations
Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb, Bruno Ribeiro +2
Pre-trained Vision Transformers now serve as powerful tools for computer vision. Yet, efficiently adapting them for multiple tasks remains a challenge that arises from the need to…
On the Effectiveness of Random Weights in Graph Neural Networks
Thu Bui, Carola-Bibiane Schönlieb, Bruno Ribeiro +2
Graph Neural Networks (GNNs) have achieved remarkable success across diverse tasks on graph-structured data, primarily through the use of learned weights in message passing layers.…
GRAMA: Adaptive Graph Autoregressive Moving Average Models
Moshe Eliasof, Alessio Gravina, Andrea Ceni +3
Graph State Space Models (SSMs) have recently been introduced to enhance Graph Neural Networks (GNNs) in modeling long-range interactions. Despite their success, existing methods e…
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…