Recent studies show the efficacy of Mamba models in various domains, but understanding their dynamics and mechanisms is challenging. Tel Aviv University researchers propose reformulating Mamba computation to enhance interpretability, linking Mamba to self-attention layers. They develop explainability tools for Mamba models, shedding light on their inner representations and potential downstream applications.
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Recent Studies on Mamba Models
Recent studies have shown that Mamba models, also known as Selective State Space Layers, are highly effective in various domains such as language and image processing, medical imaging, and data analysis. These models offer linear complexity during training and fast inference, significantly boosting throughput and enabling efficient handling of long-range dependencies.
Enhancing Explainability in Deep Neural Networks
Several methods have been developed to enhance explainability in deep neural networks, particularly in NLP, computer vision, and attention-based models. For example, AttentionRollout analyzes inter-layer pairwise attention paths, combining LRP scores with attention gradients for class-specific relevance. Tel Aviv University researchers have proposed reformulating Mamba computation to address gaps in understanding using a data-control linear operator, enabling the application of interpretability techniques from transformer realms to Mamba models.
Reformulating Mamba Computation
The researchers have reformulated selective state-space (S6) layers as self-attention, allowing the extraction of attention matrices. Visualizations of attention matrices show similarities between Mamba and Transformer models in capturing dependencies. Explainability metrics indicate that Mamba models perform comparably to Transformers in perturbation tests, demonstrating sensitivity to perturbations.
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