Task: Summarization
Input (English): Compressed forms of deep neural networks are essential in deploying large-scale
 computational models on resource-constrained devices. Contrary to analogous
 domains where large-scale systems are build as a hierarchical repetition of small-
 scale units, the current practice in Machine Learning largely relies on models with
 non-repetitive components. In the spirit of molecular composition with repeating
 atoms, we advance the state-of-the-art in model compression by proposing Atomic
 Compression Networks (ACNs), a novel architecture that is constructed by recursive
 repetition of a small set of neurons. In other words, the same neurons with the
 same weights are stochastically re-positioned in subsequent layers of the network.
 Empirical evidence suggests that ACNs achieve compression rates of up to three
 orders of magnitudes compared to fine-tuned fully-connected neural networks (88×
 to 1116× reduction) with only a fractional deterioration of classification accuracy
 (0.15% to 5.33%). Moreover our method can yield sub-linear model complexities
 and permits learning deep ACNs with less parameters than a logistic regression
 with no decline in classification accuracy.
Output (English): We advance the state-of-the-art in model compression by proposing Atomic Compression Networks (ACNs), a novel architecture that is constructed by recursive repetition of a small set of neurons.