10 citations · 19 across the 11 of their papers we have counts for
12 papers
DiMaS: Distribution Matching for Steering Vision-Language-Action Models
Pegah Khayatan, Sara Meziane, Jayneel Parekh +1
Flow-matching-based vision-language-action (VLA) models have emerged as powerful policies for robotic manipulation, yet a critical capability remains underexplored: fine-grained be…
When Prompts Override Vision: Prompt-Induced Hallucinations in LVLMs
Pegah Khayatan, Jayneel Parekh, Arnaud Dapogny +3
Despite impressive progress in capabilities of large vision-language models (LVLMs), these systems remain vulnerable to hallucinations, i.e., outputs that are not grounded in the v…
Learning to Steer: Input-dependent Steering for Multimodal LLMs
Jayneel Parekh, Pegah Khayatan, Mustafa Shukor +3
Steering has emerged as a practical approach to enable post-hoc guidance of LLMs towards enforcing a specific behavior. However, it remains largely underexplored for multimodal LLM…
Analyzing Finetuning Representation Shift for Multimodal LLMs Steering
Pegah Khayatan, Mustafa Shukor, Jayneel Parekh +2
Multimodal LLMs (MLLMs) have reached remarkable levels of proficiency in understanding multimodal inputs. However, understanding and interpreting the behavior of such complex model…
One Wave To Explain Them All: A Unifying Perspective On Feature Attribution
Gabriel Kasmi, Amandine Brunetto, Thomas Fel +1
Feature attribution methods aim to improve the transparency of deep neural networks by identifying the input features that influence a model's decision. Pixel-based heatmaps have b…
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models
Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi +2
Developing inherently interpretable models for prediction has gained prominence in recent years. A subclass of these models, wherein the interpretable network relies on learning hi…
A Concept-Based Explainability Framework for Large Multimodal Models
Jayneel Parekh, Pegah Khayatan, Mustafa Shukor +2
Large multimodal models (LMMs) combine unimodal encoders and large language models (LLMs) to perform multimodal tasks. Despite recent advancements towards the interpretability of t…
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi +2
This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to inter…
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi +2
This paper tackles post-hoc interpretability for audio processing networks. Our goal is to interpret decisions of a network in terms of high-level audio objects that are also liste…
A Framework to Learn with Interpretation
Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc
To tackle interpretability in deep learning, we present a novel framework to jointly learn a predictive model and its associated interpretation model. The interpreter provides both…
Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach
Valérie Beaudouin, Isabelle Bloch, David Bounie +6
The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep learning. Deep learning methods are remarkably accurate, but also opaque, which limits…
Speech-to-Singing Conversion in an Encoder-Decoder Framework
Jayneel Parekh, Preeti Rao, Yi-Hsuan Yang
In this paper our goal is to convert a set of spoken lines into sung ones. Unlike previous signal processing based methods, we take a learning based approach to the problem. This a…