# The Lottery Ticket Hypothesis

Deep learning is black magic. For some reason, a neural network with millions of parameters is not cursed to overfit. Somewhere, either in the architecture or the training or the weights themselves, exists a magic that allows deep neural networks to generalize.  We are now only beginning to understand why this is.  As in most cases in science, a good starting place is to observe a paradox about the system, suggest a hypothesis to explain the paradox, and then test, test, test.

The Paradox: This week we discussed “The lottery ticket hypothesis: Finding sparse, trainable neural networks” by Frankle and Carbin, ICML, 2019. Recent studies have shown that a deep neural network can be pruned down to as little as 10% of the size of the original network with no loss in test prediction. This leads to a paradox: Training the small, pruned network (randomly initialized) leads to worse test prediction than training a large network and then pruning it. This is heresy to the ML doctrine of “Thou shalt start simple, and then go complex.” What is going on?

The Hypothesis: This paradox suggests that the initialization of the weights of the pruned network matters.  This leads to the Lottery Ticket Hypothesis:

The Lottery Ticket Hypothesis: “dense, randomly-initialized, feed-forward networks contain subnetworks (i.e., winning tickets) that—when trained in isolation—reach test prediction comparable to the original network in a similar number of iterations.”

These “winning tickets” start out with an initialization that make training particularly effective.  If the winning tickets were re-initialized randomly, they would no longer be winning tickets—training would not reach the same level of test prediction. This may explain why deep and wide networks tend to perform better than shallow, narrow networks—> the deeper, wider networks have more chances of having the winning ticket. It also suggests that training a neural network is akin to stochastic gradient descent seeking out these winning tickets.

The Test, Test, Test: To find the winning ticket, the authors employ iterative pruning. They start with a large neural network, train it a bit, set the smallest-magnitude weights to zero (these weights are no longer trainable), rewind the trainable parameters to their original initialized values, and train again. This is repeated until pruning drastically hurts test prediction. They find that these winning tickets can be as sparse as 3.7% of the original network’s size while having the same if not higher test prediction (Figure 1, solid lines). Interestingly, if they randomly re-initialize the weights of these pruned networks, test prediction decreases considerably  (Figure 1, dashed lines, ‘reinit’). Thus, we have a way to identify winning tickets, these winning tickets are a measly fraction of the size of the original network, and their original initialization is crucial to train them.  The authors do a thorough job of confirming the robustness of this effect (45 figures in all). However, they did not investigate the properties of the subnetworks (e.g., did pruning happen most in the deeper layers?).

One question I had remaining is if these winning tickets were necessary or sufficient to train the large network.  One analysis that could get at this question is the following. First, identify a winning ticket, and then re-initialize its weights in the original, large network. This ensures that this subnetwork is no longer is a winning ticket. Keep repeating this process.  After removing winning tickets, does the large network fail to train? How many winning tickets does the large network have?  Will the large network always have a winning ticket?  We could also do the reverse of this: For a large network, keep initializing subnetworks that are winning tickets.  Is it the case that with more winning tickets, the network trains faster with higher test prediction?

Implications for deep learning: There are several implications for deep learning. 1. Finding out what is special about the initializations of these winning tickets may help us improve initialization strategies. 2. We may be able to improve optimization techniques by better guiding stochastic gradient descent to find and train the winning ticket as fast as possible. 3. New theory that focuses on subnetworks may lead to more insight into deep learning.  4. These winning tickets may be helpful for solving other tasks (i.e., transfer learning). There have already been some follow up studies about these issues and re-examining the lottery ticket hypothesis:  Liu et al., 2019, Crowley et al., 2019, Frankle et al., 2019, Zhou et al, 2019.

Does the brain have winning tickets? I had one recurring thought while reading this paper: I know brains prune synaptic connections substantially during development—could the brain also consist of whittled-down winning tickets? After some searching, I realized that it is largely unknown the amount of pruning that occurs or when certain pruning happens.  What would be some tell-tale signs of winning tickets?  First, substantial pruning should occur (and likely does…perhaps as much as 50%). Second, randomly initializing a developing circuit should lead to a drop in performance after the same amount of training as a control subject (not sure if we can randomly set synaptic weights yet). Third, what would happen if we could prune small-magnitude synaptic connections ourselves during development? Could the brain recover? These tests could first be carried out in insects, where we have gene lines, optogenetics, whole-brain recordings, and well-labeled cell types.

# Insights on representational similarity in neural networks with canonical correlation

For this week’s journal club, we covered “Insights on representational similarity in neural networks with canonical correlation” by Morcos, Raghu, and Bengio, NeurIPS, 2018.  To date, many different convolutional neural networks (CNNs) have been proposed to tackle the object recognition problem, including Inception (Szegedy et al., 2015), ResNet (He et al., 2016), and VGG (Simonyan and Zisserman, 2015). These networks have vastly different architectures but all achieve high accuracy. How can this be the case? One possibility is that although the architectures vary, the representations (i.e., the way these networks encode information about the objects of natural images) are very similar.

To test this, we first need a metric of similarity. One approach has been “representation similarity analysis” (RSA) (Kriegskorte et al., 2008) which relies on distance matrices to test if two representations are similar. One potential problem with RSA is that some dimensions of the representations may be “noisy” (i.e., dimensions that do not pertain to encoding the input information). For example, during training, some dimensions of the activity of CNN neurons may vary substantially across epochs but are not relevant to encoding object information. These dimensions could mask the signal of relevant dimensions when analyzing a distance matrix.

One way to avoid this is to try to directly identify the relevant dimensions, allowing us to ignore the noisy dimensions. The authors relied on an old but trusted method called canonical correlation analysis (CCA), which was developed way back in the 1930s (Hotelling, 1936)! CCA has been a handy tool in computational neuroscience, relating the activity of neurons across two populations (Semedo et al., 2014) as well as relating population activity to the output of model neurons (Susillo et al., 2015). Newer methods have been developed that are more appropriate for various problems. These include partial least squares (Höskuldsson, 1988), kernel CCA (Hardoon et al., 2004), as well as a method I developed for my own work called distance covariance analysis (DCA) (Cowley et al., 2017).  The common thread among all of these methods is that they identify dimensions that encode similar information among two or more datasets.

Overview of CCA. CCA is a close relative to linear regression, but whereas linear regression aims at prediction, CCA focuses on correlation—and thus is most suitable for cases in which the investigator seeks intuition of the data.  Given two datasets (e.g., $\mathbf{X} \in \mathcal{R}^{k \times N} \textrm{ and } \mathbf{Y} \in \mathcal{R}^{p \times N}$, both centered, where $N$ is the number of samples), CCA seeks to identify a pair of dimensions $\mathbf{u} \in \mathcal{R}^k \textrm{ and } \mathbf{v} \in \mathcal{R}^p$ such that the Pearson’s correlation between the projections $\mathbf{u}^T \mathbf{X} \textrm{ and } \mathbf{v}^T \mathbf{Y}$ is the largest. In other words, CCA identifies linear combinations of the variables in $\mathbf{X} \textrm{ and } \mathbf{Y}$ that are the most linearly-related. CCA need not stop there—it can identify pairs of dimensions that monotonically decrease in correlation. In this way, we can ignore the dimensions with the smallest correlations (which likely are spurious). One fun fact about CCA is that any two identified dimensions in $\mathbf{X}$ are uncorrelated: $\textrm{corr}(\mathbf{u}_i^T \mathbf{X}, \mathbf{u}_j^T \mathbf{X}) = 0 \textrm{ for } i \neq j$ (and the same for $\mathbf{v}_i, \mathbf{v}_j$). This is different from PCA, whose identified dimensions are both uncorrelated and orthogonal.  The uncorrelatedness of CCA dimensions ensures that we do not include dimensions that contain redundant information. (Implementation details: CCA is solved with singular-value decomposition, but be sure to use a regularized form akin to ridge regression—it was unclear if the authors used regularization).

Figure 1. Generalizing networks converge to more similar solutions than memorizing networks.

Onto the results. The authors proposed a distance metric of CCA to uncover some intuitive characteristics about deep neural networks. First, they found that different initializations of generalizing networks (i.e., networks trained on labeled natural images) were more similar than different initializations of memorizing networks (i.e., networks trained on the same dataset with randomly-shuffled labels). This is expected, as natural labels likely put a constraint on generalizing networks. Interestingly, when comparing generalizing and memorizing networks (Fig. 1, yellow line, ‘Inter’), they found that generalizing and memorizing networks were as similar as different memorizing networks trained on the same fixed dataset. This suggests that overfitted networks converge on very different solutions for the same problem. Also interesting was that earlier layers of both generalizing and memorizing networks seem to converge on similar solutions, while the later layers diverged. This suggests that earlier layers rely more on the structure of natural images while the later layers rely more on the structure of the labels. Second, they found that wider networks (i.e., networks with more filters per layer) converge to more similar solutions than those of narrower networks.  They argue that this supports the “lottery-ticket” hypothesis that wider networks are more likely to have a sub-network that fits the desired function.  Finally, they found that networks trained with different initializations and learning rates on the same problem converge to different groups of solutions. This highlights the need to try different initializations when training neural networks.

This paper left me thinking a lot about representation in the visual cortex of the brain. Does visual cortical population activity have stable and “noisy” dimensions?  If we reduced the number of visual cortical neurons per visual cortical area (either via lesion or pharmacological intervention) in a developing animal, would these animals have severe perceptual deficits (i.e., their visual system did not have the right lottery ticket when developing)?  Lastly, it seems plausible that humans start out with different initializations of their visual cortices—does that suggest different humans have converged on different solutions to solving visual perception?  If so, it suggests that inter-subject variability may be larger than previously thought.