27 citations · 38 across the 9 of their papers we have counts for
9 papers
LLT: Local Linear Transformer for PDE Operator Learning
Oded Ovadia, Eli Turkel
Neural operators have become a common approach for learning PDE solution maps and accelerating numerical simulations. Transformer-based neural operators are of particular interest,…
Knowledge-Instruct: Effective Continual Pre-training from Limited Data using Instructions
Oded Ovadia, Meni Brief, Rachel Lemberg +1
While Large Language Models (LLMs) acquire vast knowledge during pre-training, they often lack domain-specific, new, or niche information. Continual pre-training (CPT) attempts to…
SECQUE: A Benchmark for Evaluating Real-World Financial Analysis Capabilities
Noga Ben Yoash, Meni Brief, Oded Ovadia +4
We introduce SECQUE, a comprehensive benchmark for evaluating large language models (LLMs) in financial analysis tasks. SECQUE comprises 565 expert-written questions covering SEC f…
Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM Performance -- A Case Study in Finance
Meni Brief, Oded Ovadia, Gil Shenderovitz +3
The application of large language models (LLMs) in domain-specific contexts, including finance, has expanded rapidly. Domain-specific LLMs are typically evaluated based on their pe…
Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs
Oded Ovadia, Menachem Brief, Moshik Mishaeli +1
Large language models (LLMs) encapsulate a vast amount of factual information within their pre-trained weights, as evidenced by their ability to answer diverse questions across dif…
Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling
Maria Luisa Taccari, Oded Ovadia, He Wang +3
This paper presents a comprehensive comparison of various machine learning models, namely U-Net, U-Net integrated with Vision Transformers (ViT), and Fourier Neural Operator (FNO),…
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)
Oded Ovadia, Vivek Oommen, Adar Kahana +3
Extrapolation remains a grand challenge in deep neural networks across all application domains. We propose an operator learning method to solve time-dependent partial differential…
ViTO: Vision Transformer-Operator
Oded Ovadia, Adar Kahana, Panos Stinis +2
We combine vision transformers with operator learning to solve diverse inverse problems described by partial differential equations (PDEs). Our approach, named ViTO, combines a U-N…
A Convolutional Dispersion Relation Preserving Scheme for the Acoustic Wave Equation
Oded Ovadia, Adar Kahana, Eli Turkel
We propose an accurate numerical scheme for approximating the solution of the two dimensional acoustic wave problem. We use machine learning to find a stencil suitable even in the…