109 citations · 112 across the 11 of their papers we have counts for
11 papers
Mapping Dynamic, Hierarchical Quantum Circuits
Marouane Benbetka, Merwan Bekkar, Bokyeong Yoon +2
Qubit mapping is a critical pass in quantum compilation. Despite various advances, dynamic circuits, those exhibiting data dependent control-flow, often resulting from qubit measur…
Dependence-Driven, Scalable Quantum Circuit Mapping with Affine Abstractions
Marouane Benbetka, Merwan Bekkar, Riyadh Baghdadi +1
Qubit Mapping is a critical task in Quantum Compilation, as modern Quantum Processing Units (QPUs) are constrained to nearest-neighbor interactions defined by a qubit coupling grap…
Neural Architecture Search with Mixed Bio-inspired Learning Rules
Imane Hamzaoui, Riyadh Baghdadi
Bio-inspired neural networks are attractive for their adversarial robustness, energy frugality, and closer alignment with cortical physiology, yet they often lag behind back-propag…
Sign-Symmetry Learning Rules are Robust Fine-Tuners
Aymene Berriche, Mehdi Zakaria Adjal, Riyadh Baghdadi
Backpropagation (BP) has long been the predominant method for training neural networks due to its effectiveness. However, numerous alternative approaches, broadly categorized under…
Exploring the Knowledge Mismatch Hypothesis: Hallucination Propensity in Small Models Fine-tuned on Data from Larger Models
Phil Wee, Riyadh Baghdadi
Recently, there has been an explosion of large language models created through fine-tuning with data from larger models. These small models able to produce outputs that appear qual…
A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler
Mohammed Tirichine, Nassim Ameur, Nazim Bendib +4
Code optimization is a crucial task that aims to enhance code performance. However, this process is often tedious and complex, highlighting the necessity for automatic code optimiz…
Combining Neural Architecture Search and Automatic Code Optimization: A Survey
Inas Bachiri, Hadjer Benmeziane, Smail Niar +3
Deep Learning models have experienced exponential growth in complexity and resource demands in recent years. Accelerating these models for efficient execution on resource-constrain…
Curriculum Learning for Small Code Language Models
Marwa Naïr, Kamel Yamani, Lynda Said Lhadj +1
Code language models have emerged as useful tools for various programming tasks, yet they often struggle when it comes to complex ones. In this paper, we explore the potential of c…
Leveraging High-Resolution Features for Improved Deep Hashing-based Image Retrieval
Aymene Berriche, Mehdi Adjal Zakaria, Riyadh Baghdadi
Deep hashing techniques have emerged as the predominant approach for efficient image retrieval. Traditionally, these methods utilize pre-trained convolutional neural networks (CNNs…
Automatic Generation of Python Programs Using Context-Free Grammars
Kamel Yamani, Marwa Naïr, Riyadh Baghdadi
In recent years, data has emerged as the new gold, serving as a powerful tool for creating intelligent systems. However, procuring high-quality data remains challenging, especially…
Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights
Shail Dave, Riyadh Baghdadi, Tony Nowatzki +3
Machine learning (ML) models are widely used in many important domains. For efficiently processing these computational- and memory-intensive applications, tensors of these over-par…