6 papers
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
Michal Moshkovitz, Suraj Srinivas, Lesia Semenova +7
Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice,…
Additive Models Explained: A Computational Complexity Approach
Shahaf Bassan, Michal Moshkovitz, Guy Katz
Generalized Additive Models (GAMs) are commonly considered *interpretable* within the ML community, as their structure makes the relationship between inputs and outputs relatively…
LLM4SFC: Sequential Function Chart Generation via Large Language Models
Ofek Glick, Vladimir Tchuiev, Marah Ghoummaid +2
While Large Language Models (LLMs) are increasingly used for synthesizing textual PLC programming languages like Structured Text (ST) code, other IEC 61131-3 standard graphical lan…
MATCH: Task-Driven Code Evaluation through Contrastive Learning
Marah Ghoummaid, Vladimir Tchuiev, Ofek Glick +2
AI-based code generation is increasingly prevalent, with GitHub Copilot estimated to generate 46% of the code on GitHub. Accurately evaluating how well generated code aligns with d…
Gradient-Free Training of Quantized Neural Networks
Noa Cohen, Omkar Joglekar, Dotan Di Castro +3
Training neural networks requires significant computational resources and energy. Methods like mixed-precision and quantization-aware training reduce bit usage, yet they still depe…
Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting
Liran Nochumsohn, Michal Moshkovitz, Orly Avner +2
Time series forecasting is critical in numerous real-world applications, requiring accurate predictions of future values based on observed patterns. While traditional forecasting t…