Clustering is a widely used unsupervised learning technique involving an
intensive discrete optimization problem. Associative Memory models or AMs are
differentiable neural networks defining a recursive dynamical system, which
have been integrated with various deep learning architectures. We uncover a
novel connection between the AM dynamics and the inherent discrete assignment
necessary in clustering to propose a novel unconstrained continuous relaxation
of the discrete clustering problem, enabling end-to-end differentiable
clustering with AM, dubbed ClAM. Leveraging the pattern completion ability of
AMs, we further develop a novel self-supervised clustering loss. Our
evaluations on varied datasets demonstrate that ClAM benefits from the
self-supervision, and significantly improves upon both the traditional Lloyd's
k-means algorithm, and more recent continuous clustering relaxations (by upto
60% in terms of the Silhouette Coefficient).

提出了一种使用关联记忆模型实现的端到端可微分聚类方法 ClAM，并且使用自监督聚类损失进行了改进，实验结果表明，ClAM 明显优于传统的 k-means 算法和连续聚类方法。

具有关联记忆的端对端可微聚类

End-to-end Differentiable Clustering with Associative Memories

$\partial\mathbb{B}$ nets are differentiable neural networks that learn
discrete boolean-valued functions by gradient descent. $\partial\mathbb{B}$
nets have two semantically equivalent aspects: a differentiable soft-net, with
real weights, and a non-differentiable hard-net, with boolean weights. We train
the soft-net by backpropagation and then `harden' the learned weights to yield
boolean weights that bind with the hard-net. The result is a learned discrete
function. `Hardening' involves no loss of accuracy, unlike existing approaches
to neural network binarization. Preliminary experiments demonstrate that
$\partial\mathbb{B}$ nets achieve comparable performance on standard machine
learning problems yet are compact (due to 1-bit weights) and interpretable (due
to the logical nature of the learnt functions).

该研究介绍了一种利用梯度下降学习离散布尔值函数的可微分神经网络，该网络具有不可微分的硬网络和可微分的软网络，可以通过后向传播算法进行训练。在实验中，该网络展现了可比较的性能，同时由于使用了 1 比特权重的方式，紧凑并且具有解释性。