We study signal propagation at initialization in transformers through the averaged partial Jacobian norm (APJN), a measure of gradient amplification across layers. We extend APJN analysis to transformers with bidirectional attention and permutation-symmetric input token configurations by deriving recurrence relations for activation statistics and APJNs across layers. Our theory predicts how attention modifies the asymptotic behavior of the APJN at large depth and matches APJNs measured in deep vision transformers. The criticality picture known from residual networks carries over to transformers: the pre-LayerNorm architecture exhibits power-law APJN growth, whereas transformers with LayerNorm replaced by elementwise $\tanh$-like nonlinearities have stretched-exponential APJN growth, indicating that the latter are subcritical. Applied to Dynamic Tanh (DyT) and Dynamic erf (Derf) transformers, the theory explains why these architectures can be more sensitive to initialization and optimization choices and require careful tuning for stable training.

Features of both words and sentences will be aggregated to enhance the representation ability.

Our method's efficacy is demonstrated on the class-conditional ImageNet generation benchmark, where

Vision transformers have contributed greatly to advancements in the computer vision domain, demonstrating state-of-the-art performance in diverse tasks ( e.g.,

In this work, we study the performance of byte-level and subword-level autoregressive models when trained using a fixed compute budget.

Figure 1: Overview of the Transformer block used in the PromptIR framework. As mentioned in section 3.1.2 Bias-free convolutions are utilized within this submodule. After MDT A Module the features are processed through the GDFN module. Our method effectively removes haze to produce visually better images.

We present ComSL, a speech-language model built atop a composite architecture of public pretrained speech-only and language-only models and optimized data-efficiently for spoken language tasks.