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Dhia naouali

Penultimate-year Software Engineering Student, exploring frontier Machine Learning

Exploring the depths of Machine Learning, diffusion models, flow matching, SSL, interpretability and beyond I build, read, and experiment with PyTorch, JAX, and distributed systems

actively looking for research / engineering opportunities.

Articles & technical notes

World Models & VLAs

February-2026 (in progress, current focus)
Notes on world models and VLAs: learning in latent dreams, JEPA architectures, foundation vision models for model-predictive control, and physical consistency constraints

A Decade of Residuals: History & Effects on modern ML

January-2026
This article traces the evolution of residual connections and their influence on modern architectures, optimization dynamics, learned representations and the emergence of gating and hyper-connection mechanisms.

Learning Reinforcement Learning

December-2026 (in progress)
Notes on modern deep RL: policy gradients, actor–critic methods, Q-learning, exploration and practical training insights mainly from UC Berkley's CS285 and ETH Zurich's Robot learning course.

A Gentle Introduction to Distributed Training

November-2025
In this blog post we discuss distributed training by examining how parallelism strategies shard and schedule data, parameters and intermediate activations to control memory usage and execution flow.

JAX: Jit Autograd XLA

October-2025
In-depth reference on JAX: design and programming philosophy, distributed / multi-device training, async dispatch via XLA and high-performance usage patterns.

Mechanistic Interpretability

August-2025
An overview of techniques for reverse-engineering features, circuits, and representations in vision models using probing, disentanglement, and adversarial analysis.

Generative Adversarial Networks

July-2025
A concise exploration of how GANs work focusing on training schemes, architectures, objectives and the Generator-Discriminator dynamics.

Transformers as a flock of tokens

April-2025
This blog post breaks down transformers into alternating phases of token-to-token communication and representation-space transformation, viewing tokens as a flock evolving across layers.

A Representation Space Interpretation of Neural Networks

March-2025
This blog post offers a geometric reframing of neural networks, describing how successive layers sculpt and reorganize the data manifold in representation space.