13,399 research outputs found
Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach
Modern neural networks are highly overparameterized, with capacity to
substantially overfit to training data. Nevertheless, these networks often
generalize well in practice. It has also been observed that trained networks
can often be "compressed" to much smaller representations. The purpose of this
paper is to connect these two empirical observations. Our main technical result
is a generalization bound for compressed networks based on the compressed size.
Combined with off-the-shelf compression algorithms, the bound leads to state of
the art generalization guarantees; in particular, we provide the first
non-vacuous generalization guarantees for realistic architectures applied to
the ImageNet classification problem. As additional evidence connecting
compression and generalization, we show that compressibility of models that
tend to overfit is limited: We establish an absolute limit on expected
compressibility as a function of expected generalization error, where the
expectations are over the random choice of training examples. The bounds are
complemented by empirical results that show an increase in overfitting implies
an increase in the number of bits required to describe a trained network.Comment: 16 pages, 1 figure. Accepted at ICLR 201
Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays.
Resistive RAM crossbar arrays offer an attractive solution to minimize off-chip data transfer and parallelize on-chip computations for neural networks. Here, we report a hardware/software co-design approach based on low energy subquantum conductive bridging RAM (CBRAM®) devices and a network pruning technique to reduce network level energy consumption. First, we demonstrate low energy subquantum CBRAM devices exhibiting gradual switching characteristics important for implementing weight updates in hardware during unsupervised learning. Then we develop a network pruning algorithm that can be employed during training, different from previous network pruning approaches applied for inference only. Using a 512 kbit subquantum CBRAM array, we experimentally demonstrate high recognition accuracy on the MNIST dataset for digital implementation of unsupervised learning. Our hardware/software co-design approach can pave the way towards resistive memory based neuro-inspired systems that can autonomously learn and process information in power-limited settings
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