4 citations · 6 across the 9 of their papers we have counts for
10 papers
Contribution Weights: A Geometrical Analysis of Self-Attention Transformers
Harry Jake Cunningham, Nicola Muca Cirone
Analyzing attention weights has become a standard approach for interpreting the information flow of Large Language Models (LLMs). However, this approach has significant limitations…
Kernel Learning for Mean-Variance Trading Strategies
Owen Futter, Nicola Muca Cirone, Blanka Horvath
In this article, we develop a kernel-based framework for constructing dynamic, pathdependent trading strategies under a mean-variance optimisation criterion. Building on the theore…
Structured Linear CDEs: Maximally Expressive and Parallel-in-Time Sequence Models
Benjamin Walker, Lingyi Yang, Nicola Muca Cirone +2
This work introduces Structured Linear Controlled Differential Equations (SLiCEs), a unifying framework for sequence models with structured, input-dependent state-transition matric…
ParallelFlow: Parallelizing Linear Transformers via Flow Discretization
Nicola Muca Cirone, Cristopher Salvi
We present a theoretical framework for analyzing linear attention models through matrix-valued state space models (SSMs). Our approach, Parallel Flows, provides a perspective that…
Fixed-Point RNNs: Interpolating from Diagonal to Dense
Sajad Movahedi, Felix Sarnthein, Nicola Muca Cirone +1
Linear recurrent neural networks (RNNs) and state-space models (SSMs) such as Mamba have become promising alternatives to softmax-attention as sequence mixing layers in Transformer…
Signature Reconstruction from Randomized Signatures
Mie Glückstad, Nicola Muca Cirone, Josef Teichmann
Controlled ordinary differential equations driven by continuous bounded variation curves can be considered a continuous time analogue of recurrent neural networks for the construct…
Rough kernel hedging
Nicola Muca Cirone, Cristopher Salvi
Building on the functional-analytic framework of operator-valued kernels and un-truncated signature kernels, we propose a scalable, provably convergent signature-based algorithm fo…
Genus expansion for non-linear random matrix ensembles with applications to neural networks
Nicola Muca Cirone, Jad Hamdan, Cristopher Salvi
We present a unified approach to studying certain non-linear random matrix ensembles and associated random neural networks at initialization. This begins with a novel series expans…
Theoretical Foundations of Deep Selective State-Space Models
Nicola Muca Cirone, Antonio Orvieto, Benjamin Walker +2
Structured state-space models (SSMs) such as S4, stemming from the seminal work of Gu et al., are gaining popularity as effective approaches for modeling sequential data. Deep SSMs…
Neural signature kernels as infinite-width-depth-limits of controlled ResNets
Nicola Muca Cirone, Maud Lemercier, Cristopher Salvi
Motivated by the paradigm of reservoir computing, we consider randomly initialized controlled ResNets defined as Euler-discretizations of neural controlled differential equations (…