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Pareto-optimal clustering with the primal deterministic information bottleneck
Andrew K. Tan, Max Tegmark, Isaac L. Chuang
At the heart of both lossy compression and clustering is a trade-off between the fidelity and size of the learned representation. Our goal is to map out and study the Pareto fronti…
Fault-Tolerant Neural Networks from Biological Error Correction Codes
Alexander Zlokapa, Andrew K. Tan, John M. Martyn +3
It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons? In the grid…
Recurrent Neural Networks in the Eye of Differential Equations
Murphy Yuezhen Niu, Lior Horesh, Isaac Chuang
To understand the fundamental trade-offs between training stability, temporal dynamics and architectural complexity of recurrent neural networks~(RNNs), we directly analyze RNN arc…