4 papers · 1 filter
MI-to-Mid Distilled Compression (M2M-DC): An Hybrid-Information-Guided-Block Pruning with Progressive Inner Slicing Approach to Model Compression
Lionel Levine, Haniyeh Ehsani Oskouie, Sajjad Ghiasvand +1
We introduce MI-to-Mid Distilled Compression (M2M-DC), a two-scale, shape-safe compression framework that interleaves information-guided block pruning with progressive inner slicin…
Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models
Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Mahnoosh Alizadeh +1
Vision-Language Models (VLMs) such as CLIP have shown remarkable performance in cross-modal tasks through large-scale contrastive pre-training. To adapt these large transformer-bas…
Exploring the Impact of Dataset Statistical Effect Size on Model Performance and Data Sample Size Sufficiency
Arya Hatamian, Lionel Levine, Haniyeh Ehsani Oskouie +1
Having a sufficient quantity of quality data is a critical enabler of training effective machine learning models. Being able to effectively determine the adequacy of a dataset prio…
Exploring Cross-model Neuronal Correlations in the Context of Predicting Model Performance and Generalizability
Haniyeh Ehsani Oskouie, Sajjad Ghiasvand, Lionel Levine +1
As Artificial Intelligence (AI) models are increasingly integrated into critical systems, the need for a robust framework to establish the trustworthiness of AI is increasingly par…