1 citations · 1 across the 9 of their papers we have counts for
9 papers
Rethinking Sign Language Translation: The Impact of Signer Dependence on Model Evaluation
Keren Artiaga, Sabyasachi Kamila, Haithem Afli +2
Sign Language Translation has advanced with deep learning, yet evaluations remain largely signer-dependent, with overlapping signers across train/dev/test. This raises concerns abo…
The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustness
Manuel Frank, Haithem Afli
Embedding benchmarks like MTEB report a single score per model, implicitly treating robustness as a static, scalar property. We argue that embedding robustness is multidimensional,…
Operationalising the Right to be Forgotten in LLMs: A Lightweight Sequential Unlearning Framework for Privacy-Aligned Deployment in Politically Sensitive Environments
Esen Kurt, Haithem Afli
Large Language Models (LLMs) are increasingly deployed in politically sensitive environments, where memorisation of personal data or confidential content raises regulatory concerns…
The Influence of Iconicity in Transfer Learning for Sign Language Recognition
Keren Artiaga, Conor Lynch, Haithem Afli +1
Most sign language recognition research relies on Transfer Learning (TL) from vision-based datasets such as ImageNet. Some extend this to alternatively available language datasets,…
PTEB: Towards Robust Text Embedding Evaluation via Stochastic Paraphrasing at Evaluation Time with LLMs
Manuel Frank, Haithem Afli
Current sentence embedding evaluations typically rely on static test beds like the Massive Text Embedding Benchmark (MTEB). While invaluable, repeated tuning on a fixed suite can i…
Predicting Country Instability Using Bayesian Deep Learning and Random Forest
Adam Zebrowski, Haithem Afli
Country instability is a global issue, with unpredictably high levels of instability thwarting socio-economic growth and possibly causing a slew of negative consequences. As a resu…
GASE: Generatively Augmented Sentence Encoding
Manuel Frank, Haithem Afli
We propose a training-free approach to improve sentence embeddings leveraging test-time compute by applying generative text models for data augmentation at inference time. Unlike c…
Federated Split Learning with Only Positive Labels for resource-constrained IoT environment
Praveen Joshi, Chandra Thapa, Mohammed Hasanuzzaman +2
Distributed collaborative machine learning (DCML) is a promising method in the Internet of Things (IoT) domain for training deep learning models, as data is distributed across mult…
Enabling All In-Edge Deep Learning: A Literature Review
Praveen Joshi, Mohammed Hasanuzzaman, Chandra Thapa +2
In recent years, deep learning (DL) models have demonstrated remarkable achievements on non-trivial tasks such as speech recognition and natural language understanding. One of the…