M ETA -L EARNING D EEP
E NERGY -B
ASED M EMORY M ODELS
Anonymous authors
Paper under double-blind review
A BSTRACT
We study the problem of learning associative memory – a system which is able to retrieve a remembered pattern based on its distorted or incomplete version.
Attractor networks provide a sound model of associative memory: patterns are stored as attractors of the network dynamics and associative retrieval is performed by running the dynamics starting from a query pattern until it converges to an attractor.
In such models the dynamics are often implemented as an optimization procedure that minimizes an energy function, such as in the classical Hopﬁeld network.
In general it is difﬁcult to derive a writing rule for a given dynamics and energy that is both compressive and fast.
Thus, most research in energybased memory has been limited either to tractable energy models not expressive enough to handle complex high-dimensional objects such as natural images, or to models that do not offer fast writing.
We present a novel meta-learning approach to energy-based memory models (EBMM) that allows one to use an arbitrary neural architecture as an energy model and quickly store patterns in its weights.
We demonstrate experimentally that our EBMM approach can build compressed memories for synthetic and natural data, and is capable of associative retrieval that outperforms existing memory systems in terms of the reconstruction error and compression rate.
I NTRODUCTION
Associative memory has long been of interest to neuroscience and machine learning communities
(Willshaw et al., 1969; Hopﬁeld, 1982; Kanerva, 1988).
This interest has generated many proposals for associative memory models, both biological and synthetic.
These models address the problem of storing a set of patterns in such a way that a stored pattern can be retrieved based on a partially known or distorted version.
This kind of retrieval from memory is known as auto-association.
Due to the generality of associative retrieval, successful implementations of associative memory models have the potential to impact many applications.
Attractor networks provide one well-grounded foundation for associative memory models (Amit & Amit, 1992).
Patterns are stored in such a way that they become attractors of the update dynamics deﬁned by the network.
Then, if a query pattern that preserves sufﬁcient information for association lies in the basin of attraction for the original stored pattern, a trajectory initialized by the query will converge to the stored pattern.
A variety of implementations of the general attractor principle have been proposed.
The classical
Hopﬁeld network (Hopﬁeld, 1982), for example, deﬁnes a simple quadratic energy function whose parameters serve as a memory.
The update dynamics in Hopﬁeld networks iteratively minimize the energy by changing elements of the pattern until it converges to a minimum, typically corresponding to one of the stored patterns.
The goal of the writing process is to ﬁnd parameter values such that the stored patterns become attractors for the optimization process and such that, ideally, no spurious attractors are created.
Many different learning rules have been proposed for Hopﬁeld energy models, and the simplicity of the model affords compelling closed-form analysis (Storkey & Valabregue, 1999).
At the same time, Hopﬁeld memory models have fundamental limitations: (1) It is not possible to add capacity for more stored patterns by increasing the number of parameters since the number of parameters in a
Hopﬁeld network is quadratic in the dimensionality of the patterns.
The model lacks a means of modelling the higher-order dependencies that exist in real-world data.
E ( x ;¯ ✓ )
Input x
Energy E x
Input x
Energy E ✓ = write( { x 1 , x 2 , x 3 } ;¯ ✓ )
E ( x ; ✓ ) xxx˜ x
Query read(˜ x ; ✓ ) are attractors
In domains such as natural images, the potentially large dimensionality of an input makes it both ineffective and often unnecessary to model global dependencies among raw input measurements.
In fact, many auto-correlations that exist in real-world perceptual data can be efﬁciently compressed without signiﬁcant sacriﬁce of ﬁdelity using either algorithmic (Wallace, 1992; Candes & Tao, 2004) or machine learning tools (Gregor et al., 2016; Toderici et al., 2017).
The success of existing deep learning techniques suggests a more efﬁcient recipe for processing high-dimensional inputs by modelling a hierarchy of signals with restricted or local dependencies (LeCun et al., 1995).
In this paper we use a similar idea for building an associative memory: use a deep network’s weights to store and retrieve data .
Fast writing rules A variety of energy-based memory models have been proposed since the original Hopﬁeld network to mitigate its limitations (Hinton et al., 2006b; Du & Mordatch, 2019).
Restricted Boltzmann Machines (RBMs) (Hinton, 2012) add capacity to the model by introducing latent variables, and deep variants of RBMs (Hinton et al., 2006b; Salakhutdinov & Larochelle, 2010) afford more expressive energy functions.
Unfortunately, training Boltzmann machines remains challenging, and while recent probabilistic models such as variational auto-encoders (Kingma &
Welling, 2013; Rezende et al., 2014) are easier to train, they nevertheless pay the price for expressivity in the form of slow writing.
While Hopﬁeld networks memorize patterns quickly using a simple
Hebbian rule, deep probabilistic models are slow in that they rely on gradient training that requires many updates (typically thousands or more) to settle new inputs into the weights of a network.
Hence, writing memories via parametric gradient based optimization is not straightforwardly applicable to memory problems where fast adaptation is a crucial requirement.
In contrast, and by explicit design, our proposed method enjoys fast writing , requiring few parameter updates (we employ just 5 steps) to write new inputs into the weights of the net once meta-learning is complete.
It also enjoys fast reading , requiring few gradient descent steps (again just 5 in our experiments) to retrieve a pattern.
Furthermore, our writing rules are also fast in the sense that they use O ( N ) operations to store N patterns in the memory – this scaling is the best one can hope for without additional assumptions.
We propose a novel approach that leverages meta-learning to enable fast storage of patterns into the weights of arbitrarily structured neural networks, as well as fast associative retrieval.
Our networks output a single scalar value which we treat as an energy function whose parameters implement a distributed storage scheme.
We use gradient-based reading dynamics and meta-learn a writing rule in the form of truncated gradient descent over the parameters deﬁning the energy function.
We show that the proposed approach enables compression via efﬁcient utilization of network weights, as well as fast-converging attractor dynamics.
R ETRIEVAL IN ENERGY - BASED MODELS
We focus on attractor networks as a basis for associative memory.
Attractor networks deﬁne update dynamics for iterative evolution of the input pattern: x ( k +1)
=
f ( x ( k ) ) .
For simplicity, we will assume that this process is discrete in time and deterministic, however there are examples of both continuous-time (Yoon et al., 2013) and stochastic dynamics (Aarts & Korst, 1988).
A ﬁxed-point attractor of deterministic dynamics can be deﬁned as a point x for which it converges, i.e. x = f ( x ) .
Learning the associative memory in the attractor network is then equivalent to learning [Figure] the dynamics f such that its ﬁxed-point attractors are the stored patterns and the corresponding basins of attraction are sufﬁciently wide for retrieval.
An energy-based attractor network is deﬁned by the energy function E ( x ) mapping an input object x ∈ X to a real scalar value.
A particular model may then impose additional requirements on the energy function.
For example if the model has a probabilistic interpretation, the energy function is usually a negative unnormalized logarithm of the object probability log p ( x ) = − E ( x ) + const, implying that the energy has to be well-behaved for the normalizing constant to exist. In our case no such constraints are put on the energy.
The attractor dynamics in energy-based models is often implemented either by iterative energy optimization (Hopﬁeld, 1982) or sampling (Aarts & Korst, 1988).
In the optimization case considered further in the paper, attractors are conveniently deﬁned as local minimizers of the energy function.
While a particular energy function may suggest a number of different optimization schemes for retrieval, convergence to a local minimum of an arbitrary function is NP-hard.
Thus, we consider a class of energy functions that are differentiable on X ⊆ R d , bounded from below and deﬁne the update dynamics over k = 1 , . .
, K steps via gradient descent:
[Equation]
With appropriately set step sizes { γ ( k ) }
Kk =
this procedure asymptotically converges to a local minimum of energy E ( x ) (Nesterov, 2013).
Since asymptotic convergence may be not enough for practical applications, we truncate the optimization procedure (1) at K steps and treat x ( K ) as a result of the retrieval.
While vanilla gradient descent (1) is sufﬁcient to implement retrieval, in our experiments we employ a number of extensions, such as the use of Nesterov momentum and projected gradients, which are thoroughly described in Appendix B.
Relying on the generic optimization procedure allows us to translate the problem of designing update dynamics with desirable properties to constructing an appropriate energy function, which in general is equally difﬁcult.
In the next section we discuss how to tackle this difﬁculty.
M ETA - LEARNING GRADIENT - BASED WRITING RULES
As discussed in previous sections, our ambition is to be able to use any scalar-output neural network as an energy function for associate retrieval.
We assume a parametric model E ( x ; θ ) differentiable in both x and θ , and bounded from below as a function of x .
These are mild assumptions that are often met in the existing neural architectures with an appropriate choice of activation functions, e.g. tanh.
The writing rule then compresses input patterns X
= { x 1 , x 2 , . . .
, x N } into parameters θ such that each of the stored patterns becomes a local minimum of E ( x ; θ ) or, equivalently, creates a basin of attraction for gradient descent in the pattern space.
This property can be practically quantiﬁed by the reconstruction error, e.g. mean squared error, between the stored pattern x and the pattern read (˜ x ; θ ) retrieved from a distorted version of x :
[Equation]
Here we assume a known, potentially stochastic distortion model p (˜ x | x ) such as randomly erasing certain number of dimensions, or salt-and-pepper noise.
While one can consider loss (2) as a function of network parameters θ and call minimization of this loss with a conventional optimization method a writing rule — it will require many optimization steps to obtain a satisfactory solution and thus does not fall into our deﬁnition of fast writing rules (Santoro et al., 2016).
Hence, we explore a different approach to designing a fast writing rule inspired by recently proposed gradient-based meta-learning techniques (Finn et al., 2017) which we call meta-learning energy-based memory models (EBMM).
Namely we perform many write and read optimization procedures with a small number of iterations for several sets of write and read observations, and backpropagate into the initial parameters of θ — to learn a good starting location for fast optimization.
As usual, we assume that we have access to the underlying data distribution p d ( X ) over datasets of interest X from which we can sample sufﬁciently many training datasets, even if the actual dataset our memory model will be used to store (at test time) is not available at the training time (Santoro et al., 2016).
The straightforward application of gradient-based meta-learning to the loss (2) is problematic, because we generally cannot evaluate or differentiate through the expectation over stochasticity of the distortion model in a way that is reliable enough for adaptation, because as the dimensionality of the pattern space grows the number of possible (and representative) distortions grows exponentially.
Instead, we deﬁne a different writing loss W , minimizing which serves as a proxy for ensuring that input patterns are local minima for the energy E ( x ; θ ) , but does not require costly retrieval of exponential number of distorted queries.
[Equation]
As one can see, the writing loss (3) consists of three terms.
The ﬁrst term is simply the energy value which we would like to be small for stored patterns relative to non-stored patterns.
The condition for x to be a local minimum of E ( x ; θ ) is two-fold: ﬁrst, the gradient at x is zero, which is captured by the second term of the writing loss, and, second, the hessian is positive-deﬁnite.
The latter condition is difﬁcult to express in a form that admits efﬁcient optimization and we found that meta-learning using just ﬁrst two terms in the writing loss is sufﬁcient.
Finally, the third term limits deviation from initial or prior parameters ¯ θ which we found helpful from optimization perspective (see Appendix D for more details).
We use truncated gradient descent on the writing loss (3) to implement the writing rule:
[Equation] X
[Equation]
To ensure that gradient updates (4) are useful for minimization of the reconstruction error (2) we train the combination of retrieval and writing rules end-to-end, meta-learning initial parameters ¯ θ , learning rate schedules r =
( { γ ( k ) }
Kk =1 , { η ( t ) }
Tt =1 ) and meta-parameters τ =
( α, β ) to perform well on random sets of patterns from the data distribution p d [Equation]
[Equation]
Crucially, the proposed EBMM implements both read ( x , θ ) and write ( X ) operations via truncated gradient descent which can be itself differentiated through in order to set up a tractable meta-learning problem.
While truncated gradient descent is not guaranteed to converge, reading and writing rules are trained jointly to minimize the reconstruction error (2) and thus ensure that they converge sufﬁciently fast.
This property turns this potential drawback of the method to its advantage over [Figure] provably convergent, but slow models.
It also relaxes the necessity of stored patterns to create too well-behaved basins of attraction because if, for example, a stored pattern creates a nuisance attractor in the dangerous proximity of the main one, the gradient descent (1) might successfully pass it with appropriately learned step sizes γ .
E XPERIMENTS
In this section we experimentally evaluate EBMM on a number of real-world image datasets.
The performance of EBMM is compared to a set of relevant baselines: Long-Short Term Memory (LSTM) (Hochreiter & Schmidhuber, 1997), the classical Hopﬁeld network (Hopﬁeld, 1982),
Memory-Augmented Neural Networks (MANN) (Santoro et al., 2016) (which are a variant of the
Differentiable Neural Computer (Graves et al., 2016)), Memory Networks (Weston et al., 2014),
Differentiable Plasticity model of Miconi et al.
(2018) (a generalization of the Fast-weights RNN (Ba et al., 2016)) and Dynamic Kanerva Machine (Wu et al., 2018).
Some of these baselines failed to learn at all for real-world images.
In the Appendix A.2 we provide additional experiments with random binary strings with a larger set of representative models.
The experimental procedure is the following: we write a ﬁxed-sized batch of images into a memory model, then corrupt a random block of the written image to form a query and let the model retrieve the originally stored image.
By varying the memory size and repeating this procedure, we perform distortion/rate analysis, i.e. we measure how well a memory model can retrieve a remembered pattern for a given memory size. Meta-learning have been performed on the canonical train splits of each dataset and testing on the test splits. Batches were sampled uniformly, see Appendix ?? for the performance study on correlated batches.
We deﬁne memory size as a number of float32 numbers used to represent a modiﬁable part of the model.
In the case of EBMM it is the subset of all network weights that are modiﬁed by the gradient descent (4), for other models it is size of the state, e.g. the number of slots × the slot size for a Memory Network.
To ensure fair comparison, all models use the same encoder (and decoder, when applicable) networks, which architectures are described in Appendix C.
In all experiments EBMM used K = 5 read iterations and T = 5 write iterations.
4. O MNIGLOT CHARACTERS
We begin with experiments on the Omniglot dataset (Lake et al., 2015) which is now a standard evaluation of fast adaptation models.
For simplicity of comparison with other models, we downscaled the images to 32 × 32 size and binarized them using a 0 .
5 threshold.
We use Hamming distance as the evaluation metric.
For training and evaluation we apply a 16 × 16 randomly positioned binary distortions (see Figure 2 for example).
We explored two versions of EBMM for this experiment that use parts of fully-connected (FC, see
Appendix C.2) and convolutional (conv, Appendix C.3) layers in a 3-block ResNet (He et al., 2016) as writable memory.
[Figure] Figure 3a contains the distortion-rate analysis of different models which in this case is the Hamming distance as a function of memory size.
We can see that there are two modes in the model behaviour.
For small memory sizes, learning a lossless storage becomes a hard problem and all models have to ﬁnd an efﬁcient compression strategy, where most of the difference between models can be observed.
However, after a certain critical memory size it becomes possible to rely just on the autoencoding which in the case of a relatively simple dataset such as Omniglot can be efﬁciently tackled by the ResNet architecture we are using.
Hence, even Memory Networks that do not employ any compression mechanisms beyond using distributed representations can retrieve original images almost perfectly.
In this experiment MANN has been able to learn the most efﬁcient compression strategy, but could not make use of larger memory.
EBMM performed well both in the high and low compression regimes with convolutional memory being more efﬁcient over the fully-connected memory.
Further, in CIFAR and ImageNet experiments we only use the convolutional version of
EBMM.
We visualize the process of associative retrieval in Figure 2.
The model successfully detected distorted parts of images and clearly managed to retrieve the original pixel intensities.
We also show energy levels of the distorted query image, the recalled images through 5 read iterations, and the original image.
In most cases we found the energy of the retrieved images to match to energy of the originals, however, an error would occur when they sometimes do not match (see the green example).
4. R EAL IMAGES FROM CIFAR-
We conducted a similiar study on the CIFAR dataset.
Here we used the same network architecture as in the Omniglot experiment.
The only difference in the experimental setup is that we used squared error as an evaluation metric since the data is continuous RGB images.
Figure 3b contains the corresponding distortion-rate analysis.
EBMM clearly dominates in the comparison.
One important reason for that is the ability of the model to detect the distorted part of the image so it can avoid paying the reconstruction loss for the rest of the image.
Moreover, unlike
Omniglot where images can be almost perfectly reconstructed by an autoencoder with a large enough code, CIFAR images have much more variety and larger channel depth.
This makes an efﬁcient joint storage of a batch as important as an ability to provide a good decoding of the stored original.
Gradient descent iterations shown in Figure 4 demonstrate the successful application of the model to natural images.
Due to the higher complexity of the dataset, the reconstructions are imperfect, however the original patterns are clearly recognizable.
Interestingly, the learned optimization schedule starts with one big gradient step providing a coarse guess that is then gradually reﬁned. [Figure] [Figure]
4. I MAGE N ET 64 X
We further investigate the ability of EBMM to handle complex visual datasets by applying the model to 64 × 64 ImageNet.
Similar to the CIFAR experiment, we construct queries by corrupting a quarter of the image with 32 × 32 random masks.
The model is based on a 4-block version of the CIFAR network.
While the network itself is rather modest compared to existing ImageNet classiﬁers, the sequential training regime resembling large-state recurrent networks prevents us from using anything signiﬁcantly bigger than a CIFAR model.
Due to prohibitively expensive computations required by experimenting at this scale, we also had to decrease the batch size to 32.
The distortion-rate analysis (Figure 5a) shows the behaviour similar to the CIFAR experiment.
EBMM pays less reconstruction error than other models and MANN demonstrates better performance than Memory Networks for smaller memory sizes; however, the asymptotic behaviour of these two models will likely match.
The qualitative results are shown in Figure 5b.
Despite the arguably more difﬁcult images, EBMM is able to capture shape and color information, although not in high detail.
We believe this could likely be mitigated by using larger models.
Additionally, using techniques such as perceptual losses (Johnson et al., 2016) instead of naive pixel-wise reconstruction errors can improve visual quality with the existing architectures, but we leave these ideas for future work.
4. A NALYSIS OF ENERGY LEVELS
We were also interested in whether energy values provided by EBMM are interpretable and whether they can be used for associative retrieval.
We took an Omniglot model and inspected energy levels of different types of patterns.
It appears that, despite not explicitly trained to, EBMM in many cases could discriminate between in-memory and out-of-memory patterns, see Figure 6.
Moreover, distorted patterns had even higher energy than simply unknown patterns.
Out-of-distribution patterns, here modelled as binarized CIFAR images, can be seen as clear outliers.
R ELATED WORK
Deep neural networks are capable of both compression (Parkhi et al., 2015; Kraska et al., 2018), and memorizing training patterns (Zhang et al., 2016).
Taken together, these properties make deep networks an attractive candidate for memory models, with both exact recall and compressive capabilities.
However, there exists a natural trade-off between the speed of writing and the realizable capacity of a model (Ba et al., 2016).
Approaches similar to ours in their use of gradient descent dynamics, but lacking fast writing, have been proposed by Hinton et al.
(2006a)
and
recently revisited by Du & Mordatch (2019) together with another stochastic deep energy model (Krotov & Hopﬁeld, 2016).
In general it is difﬁcult to derive a writing rule for a given dynamics equation or an energy model which we attempt to address in this work.
The idea of meta-learning (Thrun & Pratt, 2012; Hochreiter et al., 2001) has found many successful applications in few-shot supervised (Santoro et al., 2016;
Vinyals et al., 2016) and unsupervised learning (Bartunov & Vetrov, 2016;
Reed et al., 2017).
Our model is particularly inﬂuenced by works of Andrychowicz et al.
and Finn et al.
(2017), which experiment with meta-learning efﬁcient optimization schedules and, perhaps, can be seen as an ultimate instance of this principle since we implement both learning and inference procedures as optimization.
Perhaps the most prominent existing application of meta-learning for associative retrieval is found in the Kanerva Machine (Wu et al., 2018), which combines a variational auto-encoder with a latent linear model to serve as an addressable memory.
The Kanerva machine beneﬁts from a high-level representation extracted by the auto-encoder.
However, its linear model can only represent convex combinations of memory slots and is thus less expressive than distributed storage realizable in weights of a deep network.
We described literature on associative and energy-based memory in Section 1, but other types of memory should be mentioned in connection with our work.
Many recurrent architectures aim at maintaining efﬁcient compressive memory (Graves et al., 2016;
Rae et al., 2018).
Models developed by Ba et al. (2016) and Miconi et al. (2018) enable associative recall by combining standard RNNs with structures similar to Hopﬁeld network.
And, recently Munkhdalai et al.
(2019) explored the idea of using arbitrary feed-forward networks as a key-value storage.
Finally, the idea of learning a surrogate model to deﬁne a gradient ﬁeld useful for a problem of interest has a number of incarnations.
Putzky & Welling (2017) jointly learn an energy model and an optimizer to perform denoising or impainting of images.
Marino et al.
(2018) use gradient descent on an energy deﬁned by variational lower bound for improving variational approximations.
And, Belanger et al.
(2017) formulate a generic framework for energy-based prediction driven by gradient descent dynamics.
Modern deep learning made a departure from the earlier works on energy-based models such as
Boltzmann machines and approach image manipulation tasks using techniques such as aforementioned variational autoencoders or generative adversarial networks (GANs) (Goodfellow et al., 2014). While these models indeed constitute state of the art for learning powerful prior models of data that can perform some kind of associative retrieval, they naturally lack fast memory capabilities. In contrast, the approach proposed in this work addresses the problem of jointly learning a strong prior model and an efﬁcient memory which can be used in combination with these techniques. For example, one can replace the plain reconstruction error with a perceptual loss incurred by a GAN discriminator or use EBMM to store representations extracted by a VAE.
While both VAEs and GANs can be also be equipped with memory (as done e.g. by Wu et al.
, energy-based formulation allows us to employ arbitrary neural network parameters as an associative storage and make use of generality of gradient-based meta-learning.
As we show in the additional experiments on binary strings in Appendix A.2, EBMM is applicable not only to high-dimensional natural data, but also to uniformly generated binary strings where no prior can be useful.
At the same time, evaluation of non-memory baselines in Appendix A.3 demonstrates that the combination of a good prior and memory as in EBMM achieves signiﬁcantly better performance than just a prior, even suited with a much larger network.
C ONCLUSION
We introduced a novel learning method for deep associative memory systems.
Our method beneﬁts from the recent progress in deep learning so that we can use a very large class of neural networks both for learning representations and for storing patterns in network weights.
At the same time, we are not bound by slow gradient learning thanks to meta-learning of fast writing rules.
We showed that our method is applicable in a variety of domains from non-compressible (binary strings; see Appendix) to highly compressible (natural images) and that the resulting memory system uses available capacity efﬁciently.
We believe that more elaborate architecture search could lead to stronger results on par with state-of-the-art generative models.
The existing limitation of EBMM is the batch writing assumption, which is in principle possible to relax.
This would enable embedding of the model in reinforcement learning agents or into other tasks requiring online-updating memory.
It would be also interesting to explore a stochastic variant of
EBMM that could return different associations in the presence of uncertainty caused by compression.
Finally, many general principles of learning attractor models with desired properties are yet to be discovered and we believe that our results provide a good motivation for this line of research.
A A DDITIONAL EXPERIMENTAL DETAILS
We train all models using AdamW optimizer (Loshchilov & Hutter, 2017) with learning rate 5 × 10 −and weight decay 10 − 6 , all other parameters set to AdamW defaults.
We also apply gradient clipping by global norm at 0 . 05 .
All models were allowed to train for 2 × 10 6 gradient updates or 1 week whichever ended ﬁrst.
All baseline models always made more updates than EBMM.
One instance of each model has been trained.
Error bars showed on the ﬁgures correspond to 5- and 95-percentiles computed on a 1000 of random batches.
In all experiments we used initialization scheme proposed by He et al.
A
F AILURE MODES OF BASELINE MODELS
Image retrieval appeared to be difﬁcult for a number of baselines.
LSTM failed to train due to quadratic growth of the hidden-to-hidden weight matrix with increase of the hidden state size.
Even moderately large hidden states were prohibitive for training on a modern
GPU.
Differential plasticity additionally struggled to train when using a deep representation instead of the raw image data.
We hypothesize that it was challenging for the encoder-decoder pair to train simultaneously with the recurrent memory, because in the binary experiment, while not performing the best, the model managed to learn a memorization strategy.
Finally, the Kanerva machine could not handle the relatively strong noise we used in this task.
By design, Kanerva machine is agnostic to the noise model and is trained simply to maximize the data likelihood, without meta-learning a particular de-noising scheme.
In the presence of the strong noise it failed to train on sequences longer than 4 images.
A. E XPERIMENTS WITH RANDOM BINARY PATTERNS
Besides highly-structured patterns such as Omniglot or ImageNet images we also conducted experiments on random binary patterns – the classical setting in which associative memory models have been evaluated.
While such random patterns are not compressible in expectation due to lack of any internal structure, by this experiment we examine the efﬁciency of a learned coding scheme, i.e. how well can each of the models store binary information in the ﬂoating point format.
We generate random 128 -dimensional patterns, each dimension of which takes values of − 1 or + with equal probability, corrupt half of the bits and use this as a query for associative retrieval.
We compare EBMM employing a simple fully recurrent network (an RNN using the same input at each iteration, see Appendix C.1) as an energy model, against a classical Hopﬁeld network (Hopﬁeld, 1982) using different writing rules (Storkey & Valabregue, 1999) and a recently proposed differential plasticity model (Miconi et al., 2018).
It is worth noting the differentiable plasticity model is a generalized variant of Fast Weights (Ba et al., 2016), where the plasticity of each activation is modulated separately.
We also consider an LSTM (Hochreiter & Schmidhuber, 1997), Memory network (Weston et al., 2014) and a Memory-Augmented Neural Network (MANN) used by Santoro et al.
(2016) which is a variant of the DNC (Graves et al., 2016).
Since the Hopﬁeld network has limited capacity that is strongly tied to input dimensionality and that cannot be increased without adding more inputs, we use its memory size as a reference and constrain all other baseline models to use the same amount of memory.
For this task it equals to 128 ∗ (128 − 1) / 2 + 128 to parametrize a symmetric matrix and a frequency vector.
We measure
Hamming distance between the original and the retrieved pattern for each system, varying the number of stored patterns.
We found it difﬁcult to train the recurrent baselines on this task, so we let all models clamp non-distorted bits to their true values at retrieval which signiﬁcantly stabilized training.
As we can see from the results shown in Table 1, EBMM learned a highly efﬁcient associative memory.
Only the EBMM and the memory network could achieve near-zero error when storingvectors and even though EBMM could not handle 96 vectors with this number of parameters, it was the most accurate memory model. A. E VALUATION OF NON - MEMORY BASELINES
For a more complete experimental study, we additionally evaluate a number of baselines which have no capacity to adapt to patterns that otherwise would be written into the memory but still learn a prior over a domain and hence can serve as a form of associative memory. One should note, however, that even though such models can, in principle, achieve relatively good performance by pretraining on large datasets and adopting well-designed architectures, they ultimately fail in situations where a strong memory is more important a good prior. One very distinctive example of such setting is the binary string experiment presented in the previous section, where no prior can be useful at all. EBMM, in contrast, naturally learn both the prior in the form of shared features and memory in the form of writable weights.
Our ﬁrst non-memory baseline is a Variational Auto-encoder (VAE) model with 256 latent Gaussian variables and Bernoulli likelihood.
VAE deﬁnes an energy equal to the negative joint log-likelihood:
[Equation]
We consider attractor dynamics similar to the one used by Wu et al.
We start from a conﬁguration x (0) =
˜ x , z (0) = µ ( x ) , where µ ( x ) is the output of a Gaussian encoder q
( z | x ) =
N ( z | µ ( x ) , diag ( σ ( x ))) .
Then we alternate between updating these two parts of the conﬁguration as follows:
[Equation]
Since we use a simple factorized Bernoulli likelihood model, the exact minimization with respect to x can be performed analytically.
One can hope that under certain circumstances a well-trained VAE would assign lower energy levels to less distorted versions of ˜ x and an image We used 50, 100 and 200 iterations for Omniglot, CIFAR and ImageNet experiments respectively, in each case also performing a grid search for the learning rate γ .
Another baseline is a Denoising Auto-encoder (DAE) which is trained to reconstruct the original pattern x from its distorted version ˜ x using a bottleneck of 256 hidden units.
This model is trained exactly as our model using (2) as an objective and hence, in contrast to VAE, can adapt to a particular noise pattern, which makes it a stronger baseline.
Finally, we consider the Deep Image Prior (Ulyanov et al., 2018), a method that can be seen as a very special case of the energy-based model which also iteratively adapts parameters of a convolutional network to generate the most plausible reconstruction.
We used the network provided by the authors and performed 2000 Adam updates for each of the images.
Similarly to how it was implemented in the paper, the network was instructed about location of the occluded block which arguably is a strong advantage over all other models.
The quantitative results of the comparison are provided in the Table 2.
Clearly, the most efﬁcient of the baselines is DAE, largely because it was explicitly trained against a known distortion model.
In
B ASELINE
O MNIGLOT
CIFAR
I MAGE N ET 25%
BLOCK NOISE
V ARIATIONAL
A UTO - ENCODER 109.
D ENOISING A UTO - ENCODER 36.
D EEP I MAGE P RIOR – – 170.36* 15% SALT AND PEPPER NOISE
V ARIATIONAL
A UTO - ENCODER 84.
D ENOISING A UTO - ENCODER 9.
D
[Table]
MAGE P
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contrast, VAE failed to recover from noise producing a signiﬁcantly shifted observation distribution.
Deep Image Prior performed signiﬁcantly better, however, since it only adapts to a single distorted image instead of a whole distribution, it could not over-perform DAE with a much simpler architecture.
As expected, none of the non-memory baselines performed even comparably to models with memory we consider in the main experiments section.
A. C OMPARISON WITH D YNAMIC K ANERVA M ACHINE
As we reported earlier, Dynamic Kanerva Machine failed to perform better than a random guess under the strong block noise we used in the main paper.
In this appendix we evaluate DKM in simpler conditions where it can be reasonably compared to EBMM.
Thus, we trained DKM on batches ofimages and used a relatively simple 15% salt and pepper noise.
Figure 7 contains the reconstruction error in the same format as Figure 3a.
One can see that DKM generally performs poorly and does not show a consistent improvement with increasing memory size.
As can be seen on Figure 8, DKM is able to retrieve patterns that are visually similar to the originally stored ones, but also introduces a fair amount of undesirable variability and occasionally converges to a spurious pattern.
The errors increase with stronger block noise and larger batch sizes.
B R EADING IN EBMM B. P ROJECTED GRADIENT DESCENT
We described the basic reading procedure in section 2, however, there is a number of extensions we found useful in practice.
Since in all experiments we work with data constrained to the [0 , 1] interval, one has to ensure that the read data also satisﬁes this constraint.
One strategy that is often used in the literature is to model the output as an argument to a sigmoid function (logits).
This may not work well for values close to the interval boundaries due to vanishing gradient, so instead we adopted a projected gradient descent, i.e.
[Equation]
where the proj function clips data to the [0 , 1] interval.
Quite interestingly, this formulation allows more ﬂexible behavior of the energy function.
If a stored pattern x has one of the dimensions exactly on the feasible interval boundary, e.g. x j = 0 , then ∇ x j E ( x ) does not necessarily have to be zero, since x j will not be able to go beyond zero.
We provide more information on the properties of storied patterns in further appendices.
B. N ESTEROV MOMENTUM
Another extension we found useful is to employ Nesterov momentum (Nesterov, 1983) into the optimization scheme and we use it in all our experiments.
[Equation]
B. S TEP SIZES
To encourage learning converging attractor dynamics we constrained step sizes γ to be a nonincreasing sequence:
[Equation]
Then the actual parameters to meta-learn is the initial step size γ (1) and the logits η .
We apply a similar parametrization to the momentum learning rates ψ .
B. S TEP - WISE RECONSTRUCTION LOSS
As has often been found helpful in the literature (Belanger et al., 2017; Antoniou et al., 2018)
we apply the reconstruction loss (2) not just to the ﬁnal iterate of the gradient descent, but to all iterates simultaneously:
[-|| x i − x ( k ) i || 
 (cid:105) .-][Equation]
C A RCHITECTURE DETAILS
Below we provide pseudocode for computational graphs of models used in the experiments.
All modules containing memory parameters are speciﬁcally named as memory .
C. G ATED RNN
We used a fairly standard recurrent architecture only equipped with an update gate as in (Chung et al., 2014).
We unroll the RNN for 5 steps and compute the energy value from the last hidden state.
hidden_size =
input_size =
# 128 * (128 - 1) / 2 + 128 parameters in total dynamic_size =
(input_size - 1) //state
= repeat_batch(zeros(hidden_size)) memory =
Linear(input_size, dynamic_size) gate =
Sequential([
Linear(input_size + hidden_size, hidden_size), sigmoid ]) static =
Linear(input_size
+ hidden_size, hidden_size - dynamic_size) for hop in xrange(5): z = concat(x, state) dynamic_part =
memory(x) static_part = static(z) c
= tanh(concat(dynamic_part, static_part)) u = gate(z) state =
u * c + (1 - u) * state energy = Linear(1)(state)
C.
R ES N ET , FULLY - CONNECTED MEMORY channels =
hidden_size =
representation_size =
static_size =
representation_size - dynamic_size state = repeat_batch(zeros(hidden_size))
encoder =
Sequential([
ResBlock(channels * 1, kernel=[3, 3], stride=2, downscale=False),
ResBlock(channels * 2, kernel=[3, 3], stride=2, downscale=False),
ResBlock(channels * 3, kernel=[3, 3], stride=2, downscale=False), flatten, Linear(256),
LayerNorm() ]) gate =
Sequential([
Linear(hidden_size), sigmoid ]) hidden =
Sequential([
Linear(hidden_size), tanh ]) x = encoder(x) memory =
Linear(input_size, dynamic_size) dynamic_part
=
memory(x)
static_part = Linear(static_size)(x) x = tanh(concat(dynamic_part, static_part)) for hop in xrange(3): z = concat(x, state)
c = hidden(z) c = LayerNorm()(c) u
= gate(z) state =
u * c + (1 - u) *
c h = tanh(Linear(1024)()(state)) energy =
Linear(1)(h)
The encoder module is also shared with all baseline models together with its transposed version as a decoder.
C. R ES N ET , CONVOLUTIONAL MEMORY channels =
x = ResBlock(channels * 1, kernel=[3, 3], stride=2, downscale=True)(x) x
= ResBlock(channels * 2, kernel=[3, 3], stride=2, downscale=True)(x) def resblock_bottleneck(x, channels, bottleneck_channels, downscale=
False): static_size = channels -
dynamic_size z
= x x = Conv2D(bottleneck_channels, [1, 1])(x) x = LayerNorm()(x) x = tanh(x) if downscale: memory_part = Conv2D(dynamic_size, kernel=[3, 3], stride=2, downscale=True)(x)
static_part = Conv2D(static_size, kernel=[3, 3], stride=2, downscale=True)(x) else: memory_part = Conv2D(dynamic_size, kernel=[3, 3], stride=1,
downscale=
False)(x)
static_part = Conv2D(static_size, kernel=[3, 3], stride=1, downscale= if downscale: recurrent =
Sequential([
Conv2D(hidden_size, kernel=[3, 3], stride=1),
LayerNorm(), tanh ])
update_gate =
Sequential([
Conv2D(hidden_size, kernel=[1, 1], stride=1),
LayerNorm(), sigmoid ]) hidden_size =
hidden_state
=
repeat_batch(zeros(4, 4, hidden_size)) for hop in xrange(3): z = concat([x, hidden_state], -1) candidate = recurrent(z) u = update_gate(z) hidden_state
= u * candidate + (1. - u) *
hidden_state x = Linear(1024)(x) x = tanh(x) energy = Linear(1)
C.
R ES N ET I MAGE N ET
This network is effectively a slightly larger version of the ResNet with convolutional memory described above.
channels =
dynamic_size =
x = ResBlock(channels * 1, kernel=[3, 3], stride=2, downscale=True)(x) x
= ResBlock(channels * 2, kernel=[3, 3], stride=2, downscale=True)(x) x = resblock_bottleneck(x, channels * 4, channels * 2, True) x = resblock_bottleneck(x, channels * 4, channels * 2, True) recurrent =
Sequential([
Conv2D(hidden_size, kernel=[3, 3], stride=1),
LayerNorm(), tanh ])
update_gate =
Sequential([
Conv2D(hidden_size, kernel=[1, 1], stride=1),
LayerNorm(), sigmoid ]) hidden_size =
hidden_state
=
repeat_batch(zeros(4, 4, hidden_size)) for hop in xrange(3): z = concat([x, hidden_state], -1) candidate = recurrent(z) u = update_gate(z)
[Equation]
C. T HE ROLE OF SKIP - CONNECTIONS IN ENERGY MODELS
Gradient-based meta-learning and EBMM in particular rely on the expressiveness of not just the forward pass of a network, but also the backward pass that is used to compute a gradient.
This may require special considerations about the network architecture.
One may notice that all energy models considered above have an element of recurrency of some sort.
While the recurrency itself is not crucial for good performance, skip-connections, of which recurrency is a special case, are.
We can illustrate this by considering an energy function of the following form:
[Equation]
Here we can think of h as a representation from which the energy is computed.
We allow the representation to be ﬁrst computed as f ( x ) and then to be reﬁned by adding g ( f ( x )) .
During retrieval, we use gradient of the energy with respect to x which can be computed as
[Equation]
One can see, that with a skip-connection the model is able to reﬁne the gradient together with the energy value.
A simple way of incorporating such skip-connections is via recurrent computation.
We allow the model to use a gating mechanism that can modulate the reﬁnement and prevent from unnecessary updates.
We found that usually a small number of recurrent steps (3-5) is enough for good performance.
D E XPLANATIONS ON THE WRITING LOSS
Our setting deviates from the standard gradient-based meta-learning as described in (Finn et al., 2017).
In particular, we are not using the same loss function (naturally deﬁned by the energy function) in adaptation and inference phases.
As we explain in section 3, writing loss (3) besides just the energy term also contains the gradient term and the prior term.
Even though we found it sufﬁcient to use just the energy value as the writing loss, perhaps not surprisingly, minimizing the gradient norm appeared to help optimization especially in the early training (see Figure 9) and lead to better ﬁnal results.
We use an individual learning rate per each writable layer and each of the three loss terms, initialized at 10 − 4 and learned together with other parameters.
We used softplus function to ensure that all learning rates remain non-negative.
[Figure]