Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Output, Gated, and Special Activations: Softmax, GLU, SIREN, and More
Published:
Not every activation is a hidden-layer curve. Some produce probabilities, some implement learned gates, some shrink values toward zero, and some are designed for very specialized settings such as implicit neural representations.
Modern Activation Functions: GELU, SiLU, Mish, and Smooth Gating
Published:
Once ReLU became the default, researchers started asking a better question: can we keep the easy optimization while making the activation smoother, softer, and more expressive? This chapter covers the modern answers.
Activation Functions in Neural Networks: Why Non-Linearity Matters
Published:
Activation functions are the reason neural networks can model curved decision boundaries instead of collapsing into one giant linear map. This chapter builds the intuition first, then walks through the classical functions that shaped deep learning.
FoPE: Fourier Position Embedding for Length Generalization
Published:
FoPE rethinks long-context positional encoding from a frequency-domain perspective. Instead of only stretching RoPE heuristically, it explicitly improves attention’s periodic extension so Transformers generalize more gracefully to longer sequences.
Position Interpolation: Extending RoPE with Minimal Fine-Tuning
Published:
Position Interpolation rescales positions before applying RoPE so a model trained on short contexts can be adapted to longer ones with surprisingly little fine-tuning. It became the reference baseline for long-context RoPE extension.
XPos: Length-Extrapolatable Rotary Embeddings
Published:
XPos modifies RoPE with a multiplicative decay that keeps relative rotations while stabilising magnitude at long distance. It is one of the cleanest attempts to make rotary embeddings extrapolate better.
p-RoPE: What Makes Rotary Positional Encodings Useful?
Published:
This paper does two things at once: it explains what RoPE is really doing inside a trained LLM, and it proposes p-RoPE, a partial rotary variant that drops the lowest frequencies to preserve stronger semantic channels.
SheafPool: Basis-Invariant Graph Readout for Sheaf Neural Networks
Published:
SheafPool solves a key missing piece in sheaf GNNs: graph-level pooling. Instead of averaging stalk vectors in arbitrary local bases, it aligns them into a shared canonical frame and builds a readout that is invariant to local basis changes.
GAPE: Remember to Forget — Gated Adaptive Positional Encoding
Published:
GAPE is a drop-in RoPE augmentation that adds content-aware attention logit biases: a query-gate suppresses irrelevant distant context while a key-gate preserves salient distant tokens. Provably sharper attention and improved long-context robustness — no architecture changes needed.
PolyNSD: Polynomial Neural Sheaf Diffusion
Published:
PolyNSD replaces the NSD propagation operator with a degree-K Chebyshev polynomial in the normalised sheaf Laplacian, achieving SOTA on homo- and heterophilic benchmarks with only diagonal restriction maps and dramatically lower memory usage.
Z-SASLM: Zero-Shot Style Blending via Spherical Interpolation
Published:
Z-SASLM is a zero-shot, fine-tuning-free style blending pipeline that replaces linear latent interpolation with SLERP along the geodesic of the hypersphere, preserving latent manifold structure when blending multiple styles. Published at CVPR 2025 Workshop.
HetSheaf: Heterogeneous Graphs Meet Cellular Sheaves
Published:
HetSheaf encodes graph heterogeneity directly in the sheaf data structure — type-aware stalks and restriction maps conditioned on node and edge types — instead of specialised architectural components, achieving +2pp on HGB with 10× fewer parameters.
LongRoPE: Extending Context to 2 Million Tokens
Published:
LongRoPE (Microsoft, 2024) pushes RoPE-based context to 2M tokens by searching for optimal per-dimension rescaling factors — far outperforming NTK or YaRN at extreme lengths.
YaRN: Yet Another RoPE Extensionn Method
Published:
YaRN combines NTK scaling for high-frequency dimensions with linear interpolation for low-frequency ones, plus a temperature correction — achieving better long-context performance with minimal fine-tuning.
NTK-Aware Scaling: Extending Context Without Fine-Tuning
Published:
NTK-Aware Scaling extends the context window of RoPE-based models by rescaling frequencies using Neural Tangent Kernel theory — with no fine-tuning required.
The Transformer Block: Putting It All Together
Published:
A single Transformer block combines attention, residuals, layer norm, and an FFN into one reusable unit. Understanding this block is understanding the Transformer.
Feed-Forward Networks: The Forgotten Half of Transformers
Published:
The FFN block holds two-thirds of a Transformer’s parameters and does most of its factual recall. Yet it is almost always overlooked in introductions to attention.
Residual Connections: Why Transformers Can Be Deep
Published:
Without residual connections, training a 96-layer Transformer would be practically impossible. The skip connection is a simple addition that solves the vanishing gradient problem and enables arbitrary depth.
Layer Normalization in Transformers
Published:
Layer norm is not optional plumbing. It determines training stability, gradient flow, and whether deep Transformers converge at all. Pre-LN vs Post-LN is not a detail — it changes training dynamics fundamentally.
Encoder vs Decoder vs Encoder-Decoder Transformers
Published:
BERT, GPT, and T5 are all Transformers — but their architectures are fundamentally different. One comparison table clarifies the entire landscape.
Cross-Attention: How Models Attend to Another Sequence
Published:
Cross-attention lets one sequence query information from a completely different sequence. It is the bridge between encoder and decoder, and the core of multimodal AI.
Attention Masks: Causal, Padding, and Bidirectional
Published:
The difference between GPT, BERT, and T5 is largely a masking decision. Learn how causal, padding, and bidirectional masks shape what each token is allowed to see.
Query, Key, Value: The Intuition Behind QKV
Published:
Q, K, and V are not arbitrary labels. They map precisely onto search queries, database labels, and retrieved content — a framework you already understand.
Scaled Dot-Product Attention: Why the √d Matters
Published:
Dividing by √d_k is not just a trick — it prevents softmax from saturating and dying in high-dimensional spaces. Here’s the math and the intuition.
ALiBi: Attention with Linear Biases
Published:
ALiBi skips traditional positional embeddings entirely and just subtracts a distance penalty from attention scores. Zero extra parameters, excellent extrapolation. Press et al., 2022.
RoPE: Rotary Position Embeddings
Published:
RoPE encodes position by rotating query and key vectors by an angle proportional to position. The clever result: absolute encoding produces relative attention for free — and it’s now the dominant PE for large language models.
Relative Positional Encodings: It’s All About Distance
Published:
Instead of asking ‘where am I?’, relative PEs ask ‘how far are these two tokens apart?’ Shaw et al. and T5 both use this idea to build models that generalise better to variable-length inputs.
Learned Positional Encodings: Data-Driven Position
Published:
Instead of a fixed formula, why not just train position embeddings from scratch — like word embeddings? That’s exactly what BERT and GPT-1 do. Here’s how and when it works.
Sinusoidal Positional Encodings: The Original Solution
Published:
The PE method from the 2017 ‘Attention Is All You Need’ paper uses sine and cosine waves at different frequencies. Learn why this elegant choice encodes position without any training.
Positional Encodings: Why Position Matters
Published:
Transformers see all tokens at once — which means without help they’d treat ‘cat ate mouse’ and ‘mouse ate cat’ the same. Positional encodings fix this. Here’s the full landscape.
Multi-Head Attention: Many Eyes on the Data
Published:
One attention head sees one relationship. Multiple heads running in parallel let the model capture syntax, semantics, and coreference simultaneously — here’s how.
Self-Attention: Teaching Machines to Focus
Published:
Self-attention is the core of every Transformer. Learn how Query, Key, and Value vectors let every token directly attend to every other — and why that matters.
Transformers: The Architecture That Changed AI
Published:
A self-contained guide to the Transformer — the engine behind GPT, BERT, and modern AI. Learn how attention replaces recurrence and why every major AI system uses it.
GNNs for Computer Vision: Scene Graphs and Beyond
Published:
Computer vision tasks increasingly require relational reasoning — understanding how objects relate to each other, not just what they are. Scene graph generation, visual question answering, action recognition from skeletons, and 3D point cloud processing all benefit from GNN-based relational modelling.
GNNs for Robotics: Planning, Manipulation, and Multi-Agent Systems
Published:
Robots interact with structured environments: objects have relationships, joints form kinematic chains, agents communicate through interaction graphs. GNNs encode these relational structures — enabling generalisation across object configurations, robot morphologies, and multi-agent scenarios.
GNNs for Knowledge Graphs: Reasoning and Completion
Published:
Knowledge graphs encode human knowledge as typed entity-relation triples. GNNs enable structure-aware entity representation, multi-hop reasoning, knowledge base completion, and entity alignment — tasks that shallow embedding methods cannot fully solve.
GNNs for Traffic Forecasting
Published:
Traffic prediction is a canonical spatio-temporal graph task: sensors on roads form a fixed graph, and speed/volume measurements evolve over time. GNNs capture spatial correlations between sensors; RNNs or convolutions capture temporal patterns. Together they achieve state-of-the-art traffic forecasting.
GNNs for Social Networks: Influence, Communities, and Misinformation
Published:
Social networks are large sparse graphs with rich node features (user profiles) and heterogeneous edges (friendship, follow, retweet). GNNs predict user behaviour, detect communities, identify influential spreaders, and flag misinformation — tasks with significant real-world impact.
GNNs for Recommender Systems
Published:
Recommendation is naturally a graph problem: users and items are nodes, interactions are edges. GNNs on bipartite user-item graphs capture higher-order collaborative filtering signals — friends of friends liked this — that matrix factorisation cannot represent.
GNNs for Molecules: Drug Discovery and Material Design
Published:
Graph neural networks are transforming computational drug discovery. Molecules are natural graphs, and GNNs learn molecular representations that predict toxicity, solubility, binding affinity, and synthesis feasibility — tasks that previously required expensive laboratory experiments.
Polynomial Neural Sheaf Diffusion
Published:
Polynomial Neural Sheaf Diffusion (PNSD) replaces the fixed diffusion operator (I - Δ_F) with a learnable polynomial of the Sheaf Laplacian. This gives the model spectral flexibility — it can learn to amplify or suppress different frequency components of the sheaf signal.
Equivariant Sheaf Neural Networks
Published:
Sheaves with orthogonal restriction maps define a connection on the graph — a parallel transport structure over edges. This connects sheaf GNNs to differential geometry and enables equivariant processing of data with local coordinate frames at each node.
Sheaf Neural Networks and Heterophily
Published:
Sheaf GNNs are the principled solution to heterophily: by learning per-edge maps that transform features before comparison, they can perform diffusion that converges within classes and diverges across classes — the exact opposite of standard GCN’s collapse.
Diagonal, Orthogonal, and General Sheaf Maps
Published:
The restriction maps in a cellular sheaf can be constrained to different matrix classes: scalars, diagonal matrices, orthogonal matrices, or general matrices. Each class offers a different trade-off between expressivity and computational cost.
Neural Sheaf Diffusion: Learning Sheaves End-to-End
Published:
Neural Sheaf Diffusion (Bodnar et al., 2022) learns the sheaf restriction maps from data using a neural network, then performs diffusion with the learned Sheaf Laplacian. This gives a principled, topology-grounded GNN that handles heterophily without heuristic fixes.
The Sheaf Laplacian: Spectral Theory for Sheaves
Published:
The Sheaf Laplacian generalises the graph Laplacian by incorporating per-edge restriction maps. Its spectrum reveals how consistent data is under the sheaf. Sheaf diffusion with this Laplacian generalises GCN to handle heterophilic graphs.
What Is a Sheaf? From Topology to Graph Learning
Published:
A sheaf is a mathematical object from algebraic topology that assigns vector spaces to cells and linear maps between them. On graphs, sheaves assign feature spaces to nodes and edges, with restriction maps encoding how node features relate across edges.
Why Message Passing Is Not Enough: The Case for Sheaves
Published:
Standard message passing aggregates neighbour features and averages. On heterophilic graphs (where neighbours often disagree), this is harmful. Cellular sheaves provide a mathematically principled framework to model per-edge relationships between node features — going beyond mere averaging.
Molecular GNNs: Learning on Atoms and Bonds
Published:
Molecules are graphs. Molecular GNNs predict chemical properties from structure. The best models use 3D coordinates and bond angles — not just connectivity.
Tensor Field Networks and Geometric Deep Learning
Published:
Tensor Field Networks (TFN) were the first architecture to achieve SE(3) equivariance using spherical harmonics and Clebsch-Gordan tensor products. They laid the theoretical foundation for NequIP and MACE — the current state-of-the-art in equivariant molecular force fields.
SE(3)-Transformers: Attention with 3D Symmetry
Published:
SE(3)-Transformers extend self-attention to 3D point clouds and molecular graphs while maintaining SE(3) equivariance. Attention weights are learned between node pairs; values are equivariant features built from spherical harmonics.
EGNN: E(n)-Equivariant Graph Neural Networks
Published:
EGNN achieves E(n)-equivariance with a simple update rule: positions updated via weighted sums of relative position vectors, features updated via invariant distances. No spherical harmonics needed.
Equivariance: What It Means and Why It Matters
Published:
Equivariance formalises the idea that a function should ‘commute with symmetry transformations.’ A rotation-equivariant model applied to rotated input gives the rotated output — no extra training needed. This is the foundation for geometric deep learning.
Why Geometry Matters in Graph Neural Networks
Published:
Many real-world graphs are embedded in 3D space — molecules, proteins, point clouds, crystal structures. Standard GNNs ignore coordinates and only use connectivity. Geometric GNNs incorporate spatial positions and must respect physical symmetries.
Spatio-Temporal GNNs: Learning on Graphs Through Time
Published:
Spatio-temporal GNNs combine spatial message passing with temporal sequence modelling. They are the dominant approach for traffic forecasting, weather prediction, and any task where measurements at sensor nodes evolve over time on a fixed graph.
Graph Neural ODEs: Continuous-Time Graph Dynamics
Published:
Neural ODEs replace discrete layer-by-layer computation with continuous dynamics governed by a differential equation. Graph Neural ODEs apply this to graph data — treating node embeddings as a dynamical system evolving in continuous time.
Temporal Graph Networks: Learning from Events
Published:
TGN (Temporal Graph Network) is the leading framework for continuous-time dynamic graphs. It maintains a per-node memory that is updated upon each interaction, enabling efficient inductive link prediction on event streams.
Static vs Dynamic Graphs: When Structure Changes Over Time
Published:
Most GNN research assumes a fixed graph. Real graphs evolve: edges appear and disappear, node features drift, new nodes arrive. Dynamic graph learning addresses how to model and predict on graphs whose structure changes over time.
Temporal Knowledge Graphs: Facts That Change Over Time
Published:
Most knowledge graphs treat facts as timeless — but facts change. Barack Obama was president from 2009 to 2017. Temporal Knowledge Graphs add timestamps to triples, requiring models to reason about what was true when.
Knowledge Graph Embeddings vs GNNs
Published:
Knowledge graph completion can be solved with shallow KG embeddings (TransE, DistMult, ComplEx) or with structural GNNs (R-GCN, CompGCN). Each approach has different inductive biases and failure modes. Understanding when to use each is the central design decision for KG tasks.
HAN: Heterogeneous Graph Attention Networks
Published:
HAN combines meta-path decomposition with two levels of attention: node-level attention weights neighbours along a meta-path, and semantic-level attention weights different meta-paths. This lets the model learn which relationships matter most for a given task.
R-GCN: Relational Graph Convolutional Networks
Published:
R-GCN extends GCN to multi-relational graphs by learning a separate weight matrix for each relation type. It handles knowledge graphs with typed edges and powers both entity classification and link prediction tasks.
Heterogeneous Graphs: When Nodes and Edges Have Types
Published:
Most real-world graphs are heterogeneous — they contain multiple node types (users, items, tags) and edge types (clicks, rates, authors). Standard GNNs treat all nodes and edges identically, making them blind to this type structure.
Graph Classification: From Node Embeddings to Graph Embeddings
Published:
Graph classification is the task of predicting a label for an entire graph. It requires composing message passing (node embeddings), readout (graph embedding), and a classifier — and all three choices interact to determine model expressiveness.
Set2Set and Attention Readout: Order-Invariant Graph Summaries
Published:
Mean and sum readout treat all nodes equally. Attention readout learns which nodes matter most for a given task. Set2Set goes further — it uses an LSTM to iteratively query the node set, producing richer graph representations than single-pass pooling.
TopKPool and SAGPool: Sparse Graph Pooling
Published:
Instead of soft cluster assignment (DiffPool), TopKPool and SAGPool select a subset of the most important nodes — producing a smaller but sparser graph at each level. Hard selection is scalable but requires careful score learning.
DiffPool: Learning Hierarchical Graph Pooling
Published:
DiffPool learns to hierarchically cluster nodes into super-nodes across layers — like a convolutional pyramid for graphs. Unlike flat global pooling, it captures multi-scale graph structure by differentiably assigning nodes to clusters.
Global Pooling in GNNs: Mean, Sum, and Max
Published:
To predict a property of an entire graph, node embeddings must be aggregated into a single vector. The choice of global pooling — mean, sum, or max — is not arbitrary: each has distinct expressive power and fits different tasks.
Sign Ambiguity in Laplacian Eigenvectors
Published:
Laplacian eigenvectors are only defined up to sign: if u is an eigenvector, so is -u. This seemingly minor issue creates a fundamental problem for learning with LapPE. Here is the problem, its consequences, and how SignNet solves it.
Structural vs Positional Encodings in Graphs
Published:
Positional encodings say where a node is in the graph. Structural encodings say what role it plays. They are complementary — and confusing them leads to poor design choices.
Shortest-Path Encodings for Graph Transformers
Published:
Shortest-path distances between nodes can be encoded as attention biases or node features — directly informing the model about graph proximity without requiring message passing.
Random Walk Positional Encodings
Published:
Random walk positional encodings encode each node’s structural context by computing the probability of returning to it from itself in k steps — a computationally efficient alternative to Laplacian eigenvectors with no sign ambiguity.
Laplacian Eigenvectors as Graph Positional Encodings
Published:
The k smallest eigenvectors of the graph Laplacian form a natural positional embedding space — the graph’s own coordinate system. They capture global structure, symmetry, and community membership.
Why GNNs Need Positional Encodings
Published:
Message-passing GNNs are permutation-equivariant by design — they cannot assign unique positions to nodes. Without positional encodings, symmetric nodes are indistinguishable. Here is why that matters and how to fix it.
Depth in GNNs: Why Deeper Is Not Always Better
Published:
In Transformers, depth = expressiveness. In GNNs, depth = both expressiveness AND over-smoothing. The optimal GNN depth is rarely more than 3-4 layers — fundamentally different from the hundreds of layers in modern LLMs.
Over-smoothing vs Over-squashing: The Difference
Published:
Oversmoothing and oversquashing are both problems with deep GNNs, but they affect different nodes, have different causes, and require different fixes. Confusing them leads to applying the wrong solution.
Oversquashing: When Too Much Information Passes Through Bottlenecks
Published:
Oversquashing occurs when exponentially many node features must be compressed into a fixed-size embedding through a bottleneck edge. It is the reason GNNs struggle with long-range dependencies — not just oversmoothing.
Oversmoothing: When All Node Embeddings Become the Same
Published:
Stack enough GNN layers and all node embeddings converge to the same vector — making the model useless. Oversmoothing is not a training problem; it is a mathematical inevitability of iterated averaging.
The Weisfeiler-Lehman Test: How Powerful Are GNNs?
Published:
The 1-WL graph isomorphism test provides the exact upper bound on message-passing GNN expressivity. GIN achieves this bound. Any pair of graphs that 1-WL cannot distinguish cannot be distinguished by any MPNN.
MPNN: The General Message Passing Neural Network Framework
Published:
The MPNN framework (Gilmer et al., 2017) unifies GCN, GAT, GIN, GraphSAGE, and almost all spatial GNNs under one abstraction: message functions, aggregation, and update. Understanding MPNN means understanding the whole GNN family.
Graphormer: Transformers with Structural Biases for Graphs
Published:
Graphormer encodes graph structure directly into Transformer attention via three biases: node centrality, spatial encoding (shortest paths), and edge encoding. It won the OGB-LSC 2021 competition on molecular property prediction.
Graph Transformers: Bringing Attention to Graphs
Published:
Graph Transformers replace or augment local message passing with full pairwise attention — every node attends to every other node. This solves long-range dependencies and over-squashing at the cost of O(N²) computation.
APPNP: Personalized PageRank Meets Graph Neural Networks
Published:
APPNP decouples feature transformation from propagation. A neural network transforms features first; then Personalized PageRank propagates the result. This enables deep propagation without over-smoothing.
SGC: Simple Graph Convolution
Published:
SGC removes all nonlinearities between GCN layers and collapses the entire propagation into a single pre-computed matrix power. Surprisingly, it matches GCN on most benchmarks — revealing that nonlinearities between layers may be unnecessary.
ChebNet: Spectral Graph Convolutions via Chebyshev Polynomials
Published:
| ChebNet avoids the expensive full eigendecomposition by approximating spectral filters with Chebyshev polynomials — achieving O( | E | ) computation and spatial locality without sacrificing expressiveness. |
Graph Fourier Transform: The Spectral View of Graphs
Published:
The Graph Fourier Transform decomposes a signal on a graph into frequency components using the Laplacian’s eigenvectors. This spectral view is the mathematical foundation behind spectral GNNs like ChebNet and GCN.
Graph Tasks: Node, Edge, and Graph-Level Prediction
Published:
GNNs can predict at three levels: properties of individual nodes, existence or type of edges, or properties of entire graphs. Each level requires a different output head and training setup.
Homophily vs Heterophily: When Neighbours Are Similar or Different
Published:
Most GNNs assume nearby nodes are similar — the homophily assumption. When this breaks (heterophilic graphs), standard message passing hurts performance. Understanding this distinction is essential for modern GNN design.
Directed, Undirected, Weighted, and Heterogeneous Graphs
Published:
Not all graphs are equal. Directed edges, edge weights, multiple node/edge types — each variant requires different GNN design choices.
What Is a Graph? Nodes, Edges, Features, and Labels
Published:
A graph is a set of nodes connected by edges — but the power of GNNs comes from the features attached to nodes and edges, and the labels we want to predict.
GIN: Graph Isomorphism Network — The Most Expressive GNN
Published:
How powerful can a GNN be? Xu et al. (2019) answered with a theoretical bound — and GIN is the architecture that achieves it. The secret: use sum aggregation and an MLP, not mean or max.
GraphSAGE: Inductive Learning on Large Graphs
Published:
GCN and GAT learn embeddings for fixed graphs — add a new node and you’re stuck. GraphSAGE (Hamilton et al., 2017) learns an aggregation function instead, so it can generate embeddings for entirely new nodes at inference time.
GAT: Graph Attention Networks
Published:
GCN assigns the same (degree-based) weight to every neighbour. GAT learns which neighbours actually matter — using attention coefficients on edges. More expressive, more interpretable.
GCN: Graph Convolutional Networks
Published:
GCN (Kipf & Welling, 2016) is the ‘hello world’ of GNNs. It simplifies spectral graph convolution into a single elegant layer: normalised neighbourhood averaging with a learned linear transformation.
Message Passing: The Universal GNN Framework
Published:
Every GNN — GCN, GAT, GraphSAGE, GIN — is a special case of message passing. Learn the three-step loop that defines them all: compute messages, aggregate, update.
The Graph Laplacian: Spectral Graph Theory Explained Simply
Published:
The Graph Laplacian is L = D - A. Its eigenvectors reveal the graph’s community structure; its eigenvalues tell you how well-connected the graph is. It’s also the mathematical bridge from spectral theory to GNNs like GCN.
The Graph Adjacency Matrix: A Graph in Matrix Form
Published:
Before understanding GNNs, you need to understand how graphs are represented mathematically. The adjacency matrix is the foundation — a simple grid that tells you which nodes are connected.
Graph Neural Networks: Learning on Graphs
Published:
Graphs are everywhere — molecules, social networks, road maps, knowledge bases. Graph Neural Networks learn from this relational structure by propagating information between connected nodes. Here’s the complete picture.
PartecipationsAndTalks
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
projects
AdaViT (Adaptive Vision Transformers)
Adaptive Vision Transformer with dynamic token sparsification and halting for efficient image classification.
ALPR: Automatic License Plate Recognition System
End-to-end real-time license plate detection and OCR pipeline with dual GUIs — one for security managers, one for drivers — built with PyTorch and Streamlit.
(AMR) Autonomous Mobile Robotics Cleaning Robot
Autonomous mobile robot for indoor cleaning with navigation, obstacle avoidance, and task orchestration.
Archaic Italian Modernization: Historical Italian Rewriting with Transformers and LLM Judges
Automatic modernization of 13th-15th century Italian into modern Italian using multilingual transformers, prompted LLMs, and LLM-as-a-judge evaluation.
AutoDriveCarSimulator: Autonomous Driving with CNNs
A simulation platform for developing and testing autonomous driving algorithms — using CNNs to map raw camera frames to steering and throttle commands.
BioHeat PINNs: Temperature Estimation with Bio-Heat Equation using Physics-Informed Neural Networks
Physics-Informed Neural Networks for real-time temperature estimation via the Pennes Bio-Heat Equation, supporting hyperthermia therapy control.
CareConnect: AI-Driven Hospital Environment Monitoring System
An AI system for querying hospital environmental sensor data via natural language chat, generating real-time graphs, and triggering automated actions via LangChain and MQTT.
Clustering-Deepening: Clustering Algorithms for Object Tracking & Image Segmentation
An in-depth study of clustering algorithms — from k-Means to DBSCAN and GMMs — applied to object tracking and image segmentation.
ElectricCompany-TicketingSystem: IT Infrastructure & Ticketing for an Electric Consultancy
End-to-end analysis and implementation of IT infrastructure (disaster recovery, smart working, fleet management) and a full ticketing system for an electric consultancy firm.
EmailSpamDetector: Spam Detection with Bidirectional LSTMs
Classifies spam and ham emails using a Bidirectional LSTM — capturing both forward and backward temporal context in email text for high-accuracy filtering.
HelpDeskSystem: Web-Based Customer Support Platform
A full-stack web help desk for issue tracking and customer support — with ticket management, user authentication, and real-time status updates.
Home-Automation: Smart Home with IoT and Arduino
End-to-end smart home system — from a physical miniature house build to Arduino-powered sensors, automated routines, and a companion mobile app.
InstaSocial: Photo-Sharing Social Platform
A full-stack Instagram-like photo sharing app — upload, explore, like, and comment — built with Vue.js frontend, Go REST API, and Docker deployment.
Java-CategoryTheory: A Category Theory Library in Java
A Java library that models core Category Theory constructs — categories, functors, natural transformations — and demonstrates their practical role in software design.
MLPipelineOptimizationStudy: End-to-End ML Pipeline Exploration
A systematic exploration of ML pipeline optimisation — covering preprocessing, feature engineering, model selection, and hyperparameter tuning across multiple algorithms.
MoonBot Navigation
Autonomous lunar rover navigation and interaction — winner of the TESP 2025 Competition.
NSIO: Neural Search Indexing Optimization
Optimising the Differentiable Search Index (DSI) with data augmentation and parameter-efficient fine-tuning (LoRA, QLoRA, AdaLoRA) — evaluated on MS MARCO.
PC-Performance-Monitoring: Statistical Analysis & ML for System Metrics
Collects, analyses, and visualises PC performance metrics — then applies ML clustering to detect anomalies and performance degradation patterns.
QRCodeGenerator: Custom Static QR Code Generator
Generate static, unlimited-use QR codes with custom styles, embedded icons, and optional captions — entirely in Python.
RealTime-VLM: Real-Time Vision-Language Model Inference in the Browser
Browser-based real-time VLM inference — continuously captures webcam frames and feeds them to any OpenAI-compatible vision API with sub-second latency.
RoboMAT
MATLAB library for robotics simulations, kinematics, dynamics, control, and path planning.
RTAD5G: Real-Time Anomaly Detection in 5G Networks
A real-time anomaly detection pipeline for 5G network telemetry, developed in collaboration with Hewlett Packard Enterprise (HPE).
SkinMe: Deep Learning for Skin Disease Detection
A deep learning application that classifies skin conditions from dermoscopic images using CNNs and LSTMs, supporting early diagnosis assistance.
StyleAligned: Zero-Shot Style Alignment in Text-to-Image Generation
A zero-shot framework for consistent style transfer in text-to-image generation — using minimal shared attention to propagate a reference style without fine-tuning.
UniDrive: University Carpooling App
A Flutter/Dart mobile app that connects university students for ride-sharing — schedule, match, and split commutes within the campus community.
XGNNs: Model-level Explanation of Graph Neural Networks with RL through Graph Generation
Model-level explanations for GNNs via reinforcement-learned graph generation on MUTAG.
Z-SASLM: Zero-Shot Multi-Style Image Synthesis via Spherical Linear Interpolation
CVPR 2025 workshop paper — a zero-shot framework for smooth multi-style image synthesis using Spherical Linear Interpolation in the latent space of diffusion models.
publications
Heterogeneous Sheaf Neural Networks
Published in arXiv preprint arXiv:2409.08036, 2024
HetSheaf is a cellular-sheaf framework for heterogeneous graphs that encodes node and edge types through type-aware local feature spaces and learned restriction maps — without specialised architectural components. The companion SheafPool readout is invariant to basis changes and enables graph-level prediction. Gains of up to +2 pp on the Heterogeneous Graph Benchmark with up to 10× fewer parameters.
Recommended citation: Braithwaite, L.; Borgi, A.; Onorato, G.; Tarantelli, K.; Restuccia, F.; Silvestri, F.; Liò, P. (2024). "Heterogeneous Sheaf Neural Networks." arXiv:2409.08036.
Go to the Webpage | Download Paper | Download Bibtex
Z-SASLM: Zero-Shot Style-Aligned SLI Blending for Latent Manipulation
Published in CVPR (Computer Vision and Pattern Recognition) 2025 Workshops (Nashville, USA 🇺🇸), 2025
Z-SASLM introduces a zero-shot, fine-tuning-free approach to style alignment in diffusion models by blending multiple reference styles directly in latent space using spherical linear interpolation (SLI) with learned, context-aware weights. The method avoids model retraining, preserves content semantics, and yields consistent style transfer across prompts and seeds.
Recommended citation: Borgi, A.; Maiano, L.; Amerini, I. (2025). "Z-SASLM: Zero-Shot Style-Aligned SLI Blending for Latent Manipulation." CVPR 2025 Workshops.
Go to the Webpage | Download Paper | Download Poster | Download Bibtex | GitHub Code
Polynomial Neural Sheaf Diffusion: A Spectral Filtering Approach on Cellular Sheaves
Published in arXiv preprint arXiv:2512.00242, 2025
ArXiv preprint on Polynomial Neural Sheaf Diffusion: A Spectral Filtering Approach on Cellular Sheaves.
Recommended citation: Borgi, A.; Silvestri F.; Liò P. (2025). "Polynomial Neural Sheaf Diffusion: A Spectral Filtering Approach on Cellular Sheaves.
Go to the Webpage | Download Paper | Download Bibtex
Remember to Forget: Gated Adaptive Positional Encoding
Published in arXiv preprint arXiv:2605.10414, 2026
GAPE (Gated Adaptive Positional Encoding) addresses core limitations of RoPE in long-context language models. A content-aware bias is injected directly into attention logits while preserving rotary geometry: query-dependent and key-dependent gates suppress irrelevant distant tokens while protecting salient context, improving attention sharpness and long-context performance on retrieval and standard benchmarks.
Recommended citation: Ali, R.; Borgi, A.; Irwin, C.; Severino, M.; Liò, P. (2026). "Remember to Forget: Gated Adaptive Positional Encoding." arXiv:2605.10414.
Go to the Webpage | Download Paper | Download Bibtex
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.