output
19982026
most citedDeep Learning in Neural Networks: An Overview

18.2k citations

585 papers

cs.SE2026★ 8 cited

Enhancing Code Understanding for Impact Analysis by Combining Transformers and Program Dependence Graphs

Yanfu Yan, Nathan Cooper, Kevin Moran +3

Impact analysis (IA) is a critical software maintenance task that identifies the effects of a given set of code changes on a larger software project with the intention of avoiding…

eess.IV2026

Thinking Fast, Thinking Slow: Adaptive Multimodal Transformer-based Sensor Fusion for Depth Estimation on Ultra-low-power MCUs

Luca Crupi, Lorenzo Lamberti, Giovanni Badaracco +3

Artificial intelligence (AI)-based multimodal sensor fusion is a relevant topic gaining ever more traction across ultra-low-power (ULP) embedded and cyber-physical systems, as it i…

cs.LG2026

Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation

Francesco Sovrano, Gabriele Dominici, Marc Langheinrich

A central goal of explainable AI is to express large language model (LLM) decision logic symbolically and ground it in internal mechanisms. Existing rule-extraction methods usually…

cs.PL2026

Misleading Microbenchmarks on the Java Virtual Machines

Filippo Schiavio, Lubomír Bulej, Walter Binder

Developers often use microbenchmarks to choose the most performant implementation of a method or a class. On the Java Virtual Machine (JVM), this is commonly done using the Java Mi…

cs.PL2026

JEDI: Java Evaluation of Declarative and Imperative Queries

Filippo Schiavio, Walter Binder

The Java Stream API aims at increasing developer productivity thanks to an easy-to-read declarative syntax to express computations. It also simplifies parallel computing, providing…

cs.SE2026

Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineering

Francesco Sovrano, Gabriele Dominici, Alberto Bacchelli

Prompt-induced cognitive biases are changes in a general-purpose AI (GPAI) system's decisions caused solely by biased wording in the input (e.g., framing, anchors), not task logic.…