Gerasimos Chatzoudis

Gerasimos Chatzoudis, Manos Plitsis, Spyridoula Stamouli et al.

Aphasia is a common speech and language disorder, typically caused by a brain injury or a stroke, that affects millions of people worldwide. Detecting and assessing Aphasia in patients is a difficult, time-consuming process, and numerous attempts to automate it have been made, the most successful using machine learning models trained on aphasic speech data. Like in many medical applications, aphasic speech data is scarce and the problem is exacerbated in so-called "low resource" languages, which are, for this task, most languages excluding English. We attempt to leverage available data in English and achieve zero-shot aphasia detection in low-resource languages such as Greek and French, by using language-agnostic linguistic features. Current cross-lingual aphasia detection approaches rely on manually extracted transcripts. We propose an end-to-end pipeline using pre-trained Automatic Speech Recognition (ASR) models that share cross-lingual speech representations and are fine-tuned for our desired low-resource languages. To further boost our ASR model's performance, we also combine it with a language model. We show that our ASR-based end-to-end pipeline offers comparable results to previous setups using human-annotated transcripts.

Gerasimos Chatzoudis, Konstantinos D. Polyzos, Zhuowei Li et al.

Understanding the internal activations of Vision Transformers (ViTs) is critical for building interpretable and trustworthy models. While Sparse Autoencoders (SAEs) have been used to extract human-interpretable features, they operate on individual layers and fail to capture the cross-layer computational structure of Transformers, as well as the relative significance of each layer in forming the last-layer representation. Alternatively, we introduce the adoption of Cross-Layer Transcoders (CLTs) as reliable, sparse, and depth-aware proxy models for MLP blocks in ViTs. CLTs use an encoder-decoder scheme to reconstruct each post-MLP activation from learned sparse embeddings of preceding layers, yielding a linear decomposition that transforms the final representation of ViTs from an opaque embedding into an additive, layer-resolved construction that enables faithful attribution and process-level interpretability. We train CLTs on CLIP ViT-B/32 and ViT-B/16 across CIFAR-100, COCO, and ImageNet-100. We show that CLTs achieve high reconstruction fidelity with post-MLP activations while preserving and even improving, in some cases, CLIP zero-shot classification accuracy. In terms of interpretability, we show that the cross-layer contribution scores provide faithful attribution, revealing that the final representation is concentrated in a smaller set of dominant layer-wise terms whose removal degrades performance and whose retention largely preserves it. These results showcase the significance of adopting CLTs as an alternative interpretable proxy of ViTs in the vision domain.

Gerasimos Chatzoudis, Zhuowei Li, Gemma E. Moran et al.

Steering vision foundation models at inference time without retraining or access to large labeled datasets is a desirable yet challenging objective, particularly in dynamic or resource-constrained settings. In this paper, we introduce Visual Sparse Steering (VS2), a lightweight, test-time method that guides vision models using steering vectors derived from sparse features learned by top-$k$ Sparse Autoencoders without requiring contrastive data. Specifically, VS2 surpasses zero-shot CLIP by 4.12% on CIFAR-100, 1.08% on CUB-200, and 1.84% on Tiny-ImageNet. We further propose VS2++, a retrieval-augmented variant that selectively amplifies relevant sparse features using pseudo-labeled neighbors at inference time. With oracle positive/negative sets, VS2++ achieves absolute top-1 gains over CLIP zero-shot of up to 21.44% on CIFAR-100, 7.08% on CUB-200, and 20.47% on Tiny-ImageNet. Interestingly, VS2 and VS2++ raise per-class accuracy by up to 25% and 38%, respectively, showing that sparse steering benefits specific classes by disambiguating visually or taxonomically proximate categories rather than providing a uniform boost. Finally, to better align the sparse features learned through the SAE reconstruction task with those relevant for downstream performance, we propose Prototype-Aligned Sparse Steering (PASS). By incorporating a prototype-alignment loss during SAE training, using labels only during training while remaining fully test-time unsupervised, PASS consistently, though modestly, outperforms VS2, achieving a 6.12% gain over VS2 only on CIFAR-100 with ViT-B/32.