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Found 5 papers, 3 papers with code
Rethinking Optimization and Architecture for Tiny Language Models
Several design formulas are empirically proved especially effective for tiny language models, including tokenizer compression, architecture tweaking, parameter inheritance and multiple-round training.
PanGu-$π$: Enhancing Language Model Architectures via Nonlinearity Compensation
We then demonstrate that the proposed approach is significantly effective for enhancing the model nonlinearity through carefully designed ablations; thus, we present a new efficient model architecture for establishing modern, namely, PanGu-$\pi$.
Towards Transferable Unrestricted Adversarial Examples with Minimum Changes
Extensive experiments verify the effectiveness of our framework on balancing imperceptibility and transferability of the crafted adversarial examples.
Unrestricted Adversarial Attacks on ImageNet Competition
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input.
Towards Transferable Adversarial Perturbations with Minimum Norm
In this work, we propose a \emph{geometry-aware framework} to generate transferable adversarial perturbation with minimum norm for each input.
