arXiv subjects

Fabio Ayres

Publications and source records attributed to Fabio Ayres.

4 recordsLinked to original sources

arXiv2026

Diagnosing Neural Convergence with Topological Alignment Spectra

Representational similarity in neural networks is inherently scale-dependent, yet widely used metrics such as Centered Kernel Alignment (CKA) and Procrustes analysis provide only global scalar estimates. These scalars often fail to distinguish micro-scale geometric jitter (local noise) from macro-scale semantic reorganization, compressing multi-scale structural relationships into a single uninformative value. We introduce the Topological Alignment Spectrum (TAS), a multi-scale diagnostic tool that sweeps normalized mean Jaccard similarity over varying neighborhood sizes. By normalizing the metric over an analytically-derived expected range (from expected overlap under randomness to perfect alignment), TAS yields a dimension-invariant metric over a spectrum of scales, where one indicates perfect structural alignment, zero reflects chance-level agreement, and negative values signal active anti-alignment at specific scales. Experiments on synthetic point clouds demonstrate that TAS allows the recognition of distinct types of alignment perturbation: local jitter harms fine-grained neighborhoods but preserves cluster-level structure, while cluster-center shuffling preserves local similarity but disrupts global alignment -- phenomena that remain invisible or conflated under global, single-scalar metrics. Applying TAS to the MultiBERTs collection reveals that fine-tuning induces comprehensive topological reorganization across scales, challenging the view of task adaptation as merely conservative or localized. While models from different random seeds remain locally divergent, semantic clusters emerge as the dominant scale of alignment. TAS thus offers a granular, topology-aware alternative for diagnosing convergence and representational stability in deep networks.

cs.LG↗

arXiv2024

On Class Separability Pitfalls In Audio-Text Contrastive Zero-Shot Learning

Recent advances in audio-text cross-modal contrastive learning have shown its potential towards zero-shot learning. One possibility for this is by projecting item embeddings from pre-trained backbone neural networks into a cross-modal space in which item similarity can be calculated in either domain. This process relies on a strong unimodal pre-training of the backbone networks, and on a data-intensive training task for the projectors. These two processes can be biased by unintentional data leakage, which can arise from using supervised learning in pre-training or from inadvertently training the cross-modal projection using labels from the zero-shot learning evaluation. In this study, we show that a significant part of the measured zero-shot learning accuracy is due to strengths inherited from the audio and text backbones, that is, they are not learned in the cross-modal domain and are not transferred from one modality to another.

cs.SD↗

arXiv2023

Well-Connected Communities in Real-World and Synthetic Networks

Integral to the problem of detecting communities through graph clustering is the expectation that they are "well connected". In this respect, we examine five different community detection approaches optimizing different criteria: the Leiden algorithm optimizing the Constant Potts Model, the Leiden algorithm optimizing modularity, Iterative K-Core Clustering (IKC), Infomap, and Markov Clustering (MCL). Surprisingly, all these methods produce, to varying extents, communities that fail even a mild requirement for well connectedness. To remediate clusters that are not well connected, we have developed the "Connectivity Modifier" (CM), which, at the cost of coverage, iteratively removes small edge cuts and re-clusters until all communities produced are well connected. Results from real-world and synthetic networks illustrate a tradeoff users make between well connected clusters and coverage, and raise questions about the "clusterability" of networks and models of community structure.

cs.SI↗

arXiv2023

Unsupervised Improvement of Audio-Text Cross-Modal Representations

Recent advances in using language models to obtain cross-modal audio-text representations have overcome the limitations of conventional training approaches that use predefined labels. This has allowed the community to make progress in tasks like zero-shot classification, which would otherwise not be possible. However, learning such representations requires a large amount of human-annotated audio-text pairs. In this paper, we study unsupervised approaches to improve the learning framework of such representations with unpaired text and audio. We explore domain-unspecific and domain-specific curation methods to create audio-text pairs that we use to further improve the model. We also show that when domain-specific curation is used in conjunction with a soft-labeled contrastive loss, we are able to obtain significant improvement in terms of zero-shot classification performance on downstream sound event classification or acoustic scene classification tasks.

cs.SD↗