The study by Eshaan Nichani, Alex Damian, and Jason D. Lee from Princeton University investigates how transformers learn causal structures using gradient descent. The research reveals that transformers’ success in…
Continue readingThe paper proposes a new structure-informed positional encoding framework for music generation with Transformers. The authors note that music generated by deep learning often lacks coherence and long-term organization, which…
Continue readingThe paper ‘Jack of All Trades, Master of Some’ introduces a transformer-based model, JAT, optimized for handling multi-modal data types and sequential decision-making tasks. The model demonstrates its versatility by…
Continue readingThis article presents Mamba-ND, a design that extends the Mamba architecture to multi-dimensional data. Transformers, the de-facto architecture for sequence modeling, have significant compute and memory complexity. Mamba, a state…
Continue readingThe paper discusses AttnLRP, a method that extends Layer-wise Relevance Propagation to handle attention layers in transformer models. It aims to provide better understanding of the reasoning process of these…
Continue readingThe article introduces Synchformer, a new model for audio-visual synchronization focused on ‘in-the-wild’ videos, such as those found on YouTube, where synchronization cues can be sparse. The authors propose a…
Continue readingThis article presents a novel methodology named Multimodal Pathway which aims to improve transformers using irrelevant data from other modalities. For instance, an ImageNet model can be improved with audio…
Continue readingThis paper introduces Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT), a new method for addressing the emerging challenge of scaling up Robotics Transformers (RT) for on-robot deployment. The authors propose…
Continue readingThis research article discusses the issue of over-smoothing in deep transformer models, where token representations become identical as the model’s depth grows. The authors propose a novel regularizer that penalizes…
Continue readingThe article introduces ATGNN (Audio Tagging Graph Neural Network), a deep learning model for audio tagging. It addresses the limitations of CNNs and Transformers by treating the spectrogram as a…
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