56 research outputs found
A jamming transition from under- to over-parametrization affects loss landscape and generalization
We argue that in fully-connected networks a phase transition delimits the
over- and under-parametrized regimes where fitting can or cannot be achieved.
Under some general conditions, we show that this transition is sharp for the
hinge loss. In the whole over-parametrized regime, poor minima of the loss are
not encountered during training since the number of constraints to satisfy is
too small to hamper minimization. Our findings support a link between this
transition and the generalization properties of the network: as we increase the
number of parameters of a given model, starting from an under-parametrized
network, we observe that the generalization error displays three phases: (i)
initial decay, (ii) increase until the transition point --- where it displays a
cusp --- and (iii) slow decay toward a constant for the rest of the
over-parametrized regime. Thereby we identify the region where the classical
phenomenon of over-fitting takes place, and the region where the model keeps
improving, in line with previous empirical observations for modern neural
networks.Comment: arXiv admin note: text overlap with arXiv:1809.0934
A Modern Take on the Bias-Variance Tradeoff in Neural Networks
The bias-variance tradeoff tells us that as model complexity increases, bias
falls and variances increases, leading to a U-shaped test error curve. However,
recent empirical results with over-parameterized neural networks are marked by
a striking absence of the classic U-shaped test error curve: test error keeps
decreasing in wider networks. This suggests that there might not be a
bias-variance tradeoff in neural networks with respect to network width, unlike
was originally claimed by, e.g., Geman et al. (1992). Motivated by the shaky
evidence used to support this claim in neural networks, we measure bias and
variance in the modern setting. We find that both bias and variance can
decrease as the number of parameters grows. To better understand this, we
introduce a new decomposition of the variance to disentangle the effects of
optimization and data sampling. We also provide theoretical analysis in a
simplified setting that is consistent with our empirical findings
Representation mitosis in wide neural networks
Deep neural networks (DNNs) defy the classical bias-variance trade-off:
adding parameters to a DNN that interpolates its training data will typically
improve its generalization performance. Explaining the mechanism behind this
``benign overfitting'' in deep networks remains an outstanding challenge. Here,
we study the last hidden layer representations of various state-of-the-art
convolutional neural networks and find evidence for an underlying mechanism
that we call "representation mitosis": if the last hidden representation is
wide enough, its neurons tend to split into groups which carry identical
information, and differ from each other only by a statistically independent
noise. Like in a mitosis process, the number of such groups, or ``clones'',
increases linearly with the width of the layer, but only if the width is above
a critical value. We show that a key ingredient to activate mitosis is
continuing the training process until the training error is zero
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