研究人员正在探索新方法来提高稀疏自编码器（SAE）的可解释性和鲁棒性。一种名为GRILL的方法旨在通过在对抗性鲁棒性评估期间恢复退化的梯度信号来揭示自编码器的隐藏漏洞。其他工作侧重于分析稀疏性和叠加对SAE损失的影响，引入可训练的有理函数作为编码器激活以增强灵活性，并提出余弦评分作为归一化表示上字典学习的默认方法。此外，研究正在调查SAE集成以改善重建误差和稳定性，并探索权重正则化技术以增加跨种子特征的一致性和可控性。
AI
arXiv:2505.03646v5 Announce Type: replace-cross Abstract: Adversarial robustness of deep autoencoders (AEs) has received less attention than that of discriminative models, although their compressed latent representations induce ill-conditioned mappings that can amplify small inpu…
arXiv cs.LG
TIER_1English(EN)·Mriganka Basu Roy Chowdhury, Eric McLaughlin Weiner·
arXiv:2606.18538v1 Announce Type: new Abstract: One of the major difficulties in the mechanistic interpretability of neural networks is the occurrence of polysemanticity, which suggests that each neuron is typically responsible for multiple different tasks, impeding a clean inter…
arXiv:2606.14990v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are standard tools for mechanistic interpretability, but current SAE families are constrained by fixed encoder nonlinearities such as ReLU, JumpReLU, and TopK. This hard-codes a particular sparsity mecha…
arXiv cs.LG
TIER_1English(EN)·Silen Naihin, Lev Stambler·
arXiv:2606.15054v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) detect features via inner product, so a feature's activation scales with both its directional alignment and the input's norm. Under BatchTopK, high-norm tokens inflate all pre-activations simultaneously, c…
arXiv:2606.15468v1 Announce Type: cross Abstract: Vision models can achieve strong performance on classification tasks, but the internal representations supporting their predictions are often difficult to interpret. This work investigates whether sparse autoencoders can decompose…
arXiv cs.LG
TIER_1Nederlands(NL)·Soham Gadgil, Chris Lin, Su-In Lee·
arXiv:2505.16077v2 Announce Type: replace Abstract: Sparse autoencoders (SAEs) are used to decompose neural network activations into human-interpretable features. Typically, features learned by a single SAE are used for downstream applications. However, it has recently been shown…
Sparse autoencoders exhibit feature stability patterns where stable features carry most predictive signal while unstable features reflect reproducible low-dimensional structure despite individual non-reproducibility.
arXiv stat.ML
TIER_1English(EN)·Piotr Jedryszek, Oliver M. Crook·
arXiv:2603.04198v2 Announce Type: replace Abstract: Sparse autoencoders (SAEs) are widely used to extract human-interpretable features from neural network activations, but their learned features can vary substantially across random seeds and training choices. To improve stability…
One of the major difficulties in the mechanistic interpretability of neural networks is the occurrence of polysemanticity, which suggests that each neuron is typically responsible for multiple different tasks, impeding a clean interpretation of their function. The seminal paper o…
