T HE
H IGH -D IMENSIONAL
G EOMETRY
OF
B INARY
N EURAL N ETWORKS
Anonymous authors
Paper under double-blind review
A BSTRACT
Recent research has shown that one can train a neural network with binary weights and activations at train time by augmenting the weights with a high-precision continuous latent variable that accumulates small changes from stochastic gradient descent.
However, there is a dearth of work to explain why one can effectively capture the features in data with binary weights and activations.
Our main result is that the neural networks with binary weights and activations trained using the method of Courbariaux, Hubara et al.
(2016) work because of the high-dimensional geometry of binary vectors.
In particular, the ideal continuous vectors that extract out features in the intermediate representations of these BNNs are well-approximated by binary vectors in the sense that dot products are approximately preserved.
Furthermore, the results and analysis used on BNNs are shown to generalize to neural networks with ternary weights and activations.
Compared to previous research that demonstrated good classiﬁcation performance with BNNs, our work explains why these BNNs work in terms of HD geometry.
Our theory serves as a foundation for understanding not only BNNs but a variety of methods that seek to compress traditional neural networks.
Furthermore, a better understanding of multilayer binary neural networks serves as a starting point for generalizing BNNs to other neural network architectures such as recurrent neural networks.
I NTRODUCTION
The rapidly decreasing cost of computation has driven many successes in the ﬁeld of deep learning in recent years.
Consequently, researchers are now considering applications of deep learning in resource limited hardware such as neuromorphic chips, embedded devices and smart phones (Esser et al.
(2016), Neftci et al.
(2016), Andri et al.
A recent realization for both theoretical researchers and industry practitioners is that traditional neural networks can be compressed because they are highly over-parameterized.
While there has been a large amount of experimental work dedicated to compressing neural networks (Sec. 2), we focus on the particular approach that replaces costly 32-bit ﬂoating point multiplications with cheap binary operations.
Our analysis reveals a simple geometric picture based on the geometry of high dimensional binary vectors that allows us to understand the successes of the recent efforts to compress neural networks.
Courbariaux et al.
(2016) and Hubara et al.
(2016) showed that one can efﬁciently train neural networks with binary weights and activations that have similar performance to their continuous counterparts.
Such BNNs execute 7 times faster using a dedicated GPU kernel at test time.
Furthermore, they argue that such BNNs require at least a factor of 32 fewer memory accesses at test time that should result in an even larger energy savings.
There are two key ideas in their papers (Fig. 1).
First, a continuous weight, w c , is associated with each binary weight, w b , that accumulates small changes from stochastic gradient descent.
Second, the non-differentiable binarize function ( θ ( x ) = 1 if x > 0 and − 1 otherwise) is replaced with a continuous one during backpropagation.
These modiﬁcations allow one to train neural networks that have binary weights and activations with stochastic gradient descent.
While the work showed how to train such networks, the existence of neural networks with binary weights and activations needs to be reconciled with previous work that has sought to understand weight matrices as extracting out continuous features in data (e.g. Zeiler &
Fergus (2014)).
Summary of contributions:
Binarize
F-­prop: y = f f (x) = 𝜽 (x)
Backprop: δx = δy · d/dx[f b (x)] x y
Binarize
Binarize W ck
W bk
A k
MatMul or Conv+MP
Batch Norm
A k+ a b
The forward function simply binarizes the inputs.
In the backward propagation step, one normally computes the derivative of the cost with respect to the input of a transformer via the Jacobian of the forward function and the derivative of the cost with respect to the output of that transformer ( δu ≡ dC/du where C is the cost function used to train the network).
Since the binarize function is non-differentiable, the straight-through estimator (Bengio et al.
(2013)), which is a smoothed version of the forward function, is used for the backward function .
Angle Preservation Property: We demonstrate that binarization approximately preserves the direction of high-dimensional vectors.
In particular, we show that the angle between a random vector (from a standard normal distribution) and its binarized version converges to arccos (cid:112) 2 /π
≈ 37 ◦ as the dimension of the vector goes to inﬁnity.
This angle is an exceedingly small angle in high dimensions.
Furthermore, we show that this property is present in the weight vectors of a network trained using the method of Courbariaux et al.
Dot Product Proportionality Property: First, we empirically show that the weight-activation dot products, an important intermediate quantity a neural network, are approximately proportional under the binarization of the weight vectors.
Next, we argue that if these weight activation dot products are proportional, then the continuous weights in the Courbariaux et al.
(2016) method aren’t just a learning artifact.
The continuous weights obtained from the BNN training algorithm (which decouples the forward and backward propagation steps) are an approximation of the weights one would learn if the network were trained with continuous weights and regular backpropagation. 3.
Generalized Binarization Transformation (GBT): We show that the computations done by the ﬁrst layer of the network are fundamentally different than the computations being done in the rest of the network because correlations in the data result in high variance principal components that are not randomly oriented relative to the binarization.
Thus we recommend an architecture that uses a continuous convolution for the ﬁrst layer to embed the image in a high-dimensional binary space, after which it can be manipulated with cheap binary operations.
Furthermore, we illustrate how a GBT (rotate, binarize, rotate back) is useful for embedding low dimensional data in a high-dimensional binary space.
Generalization to Ternary Neural Networks: We show that the same analysis applies to ternary neural networks.
In particular, the angle between a random vector from a standard normal distribution and the ternarized version of that vector predicts the empirical distribution of such angles in a network trained on CIFAR10.
Furthermore, the dot product proportionality property is shown to hold for ternary neural networks.
R ELATED W ORK
Neural networks that achieve good performance on tasks such as IMAGENET object recognition are highly computationally intensive.
For instance, AlexNet has 61 million parameters and executes 1.
billion operations to classify one 224 by 224 image (30 thousand operations/pixel) (Rastegari et al.
Researchers have sought to reduce this computational cost for embedded applications using a number of different approaches.
The ﬁrst approach is to try and compress a pre-trained network.
Kim et al.
(2015) uses a Tucker decomposition of the kernel tensor and ﬁne tunes the network afterwards.
Han et al.
(2015b) train a network, prune low magnitude connections, and retrain.
Han et al.
(2015a) extend their previous work to additionally include a weight sharing quantization step and Huffman coding of the weights.
More recently, Han et al.
(2017) train a dense network, sparsify it, and then retrain a dense network with the pruned weights initialized to zero.
Second, researchers have sought to train networks using either low precision ﬂoating point numbers or ﬁxed point numbers, which allow for cheaper multiplications (Courbariaux et al.
(2014), Gupta et al.
(2015), Judd et al.
Gysel et al.
(2016), Lin et al.
(2016), Lai et al.
Third, one can train networks that have quantized weights and or activations.
Bengio et al.
(2013) looked at estimators for the gradient through a stochastic binary unit.
Courbariaux et al.
(2015) train networks with binary weights, and then later with binary weights and activations (Courbariaux et al.
Rastegari et al.
(2016) replace a continuous weight matrix with a scalar times a binary matrix (and have a similar approximation for weight activation dot products).
Kim & Smaragdis (2016) train a network with weights restricted in the range − 1 to 1 and then use a noisy backpropagation scheme train a network with binary weights and activations.
Alemdar et al.
, Li et al.
(2016) and Zhu et al.
(2016) focus on networks with ternary weights.
Further work seeks to quantize the weights and activations in neural networks to an arbitrary number of bits (Zhou et al.
(2016), Hubara et al.
Zhou et al.
(2017) use weights and activations that are zero or powers of two.
Lin et al.
and
Zhou et al.
(2016) quantize backpropagation in addition to the forward propagation.
Beyond merely seeking to compress neural networks, the analysis of the internal representations of neural networks is useful for understanding how to to compress them.
Agrawal et al.
(2014) found that feature magnitudes in higher layers do not matter (e.g. binarizing features barely changes classiﬁcation performance).
Merolla et al.
(2016) analyze the robustness of neural network representations to a collection of different distortions.
Dosovitskiy & Brox (2016) observe that binarizing features in intermediate layers of a CNN and then using backpropagation to ﬁnd an image with those features leads to relatively little distortion of the image compared to dropping out features.
These papers naturally lead into our work where we are seeking to better understand the representations in neural networks based on the geometry of high-dimensional binary vectors.
T HEORY AND E XPERIMENTS
We investigate the internal representations of neural networks with binary weights and activations.
A binary neural network is trained on CIFAR-10 (same learning algorithm and architecture as in
Courbariaux et al.
Experiments on MNIST were carried out using both fully connected and convolutional networks and produced similar results.
The CIFAR-10 convolutional neural network has six layers of convolutions, all of which have a 3 by 3 spatial kernel.
The number of feature maps in each layer are 128, 128, 256, 256, 512, 512.
After the second, fourth, and sixth convolutions, there is a 2 by 2 max pooling operation.
Then there are two fully connected layers with 1024 units each.
Each layer has a batch norm layer in between.
The experiments using ternary neural networks use the same network architecture.
The dimensionality of the weight vectors in these networks (i.e. convolution converted to a matrix multiply) is the patch size ( = 3 ∗ 3 = 9 ) times the number of channels.
3. P RESERVATION OF D IRECTION D URING B INARIZATION
In this section, we analyze the angle distributions (i.e. geometry) of high-dimensional binary vectors.
This is crucial for understanding binary neural networks because we can imagine that at each layer of a neural network, there are some ideal continuous weight vectors that extract out features.
A binary neural network approximates these ideal continuous vectors with a binary vectors.
In low dimensions, binarization strongly impacts the direction of a vector.
However, we argue that binarization does not substantially change the direction of a high-dimensional continuous vector.
It is often the case that the geometric properties of high-dimensional vectors are counter-intuitive.
For instance, one key idea in the hyperdimensional computing theory of Kanerva (2009) is that two random, high-dimensional vectors of dimension d
whose entries are chosen uniformly from the set {− 1 , 1 } are approximately orthogonal.
The result follows from the central limit theorem because the cosine angle between two such random vectors is normally distributed with µ = 0 and σ ∼ 1 / √ d .
Then cos θ ≈ 0 implies that θ ≈ π 2 .
Building upon this work, we study the way in which binary vectors are distributed relative to continuous vectors.
As binarizing a continuous vector gives the binary vector closest in angle to that continuous vector, we can get a sense of how binary vectors are distributed relative to continuous vectors in high dimensions by binarizing continuous vectors.
The standard normal distribution, which serves as an informative null distribution because it is rotationally invariant, is used to generate random continuous vectors which are then binarized.
This analysis gives a fundamental insight into understanding the recent success of binary neural networks.
Binarizing a random continuous vector changes its direction by a small amount relative to the angle between two random vectors in moderately high dimensions (Fig. 2a).
Binarization changes the direction of a vector by approximately 37 ◦ in high dimensions.
This seems like a large change based on our low-dimensional intuition.
Indeed, the angle between two randomly chosen vectors from a rotationally invariant distribution is uniform in two dimensions.
However, two randomly chosen vectors are approximately orthogonal in high dimensions.
Thus while it is common for two random vectors to have an angle less than 37 ◦ in low dimensions, it is exceedingly rare in high dimensions.
Therefore 37 ◦ is a small angle in high dimensions.
In order to test our theory of the binarization of random vectors chosen from a rotationally invariant distribution, we train a multilayer binary CNN on CIFAR10 (using the Courbariaux et al.
(2016) method) and study the weight vectors 1 of that network.
Remarkably, there is a close correspondence between the experimental results and the theory for the angles between the binary and continuous weights (Fig. 2b).
For each layer, the distribution of the angles between the binary and continuous weights is sharply peaked near the d → ∞ expectation of arccos (cid:112) 2 /π .
We note that there is a small but systematic deviation from the theory towards larger angles for the higher layers of the network (Fig. 6).
Ternary neural networks are considered in (SI Sec. 5.5) and yield a similar result.
3. D OT P RODUCT P ROPORTIONALITY AS A S UFFICIENT C ONDITION FOR A PPROXIMATING A N ETWORK WITH C ONTINUOUS W EIGHTS
Given the previous discussion, an important question to ask is: are the so-called continuous weights a learning artifact without a clear correspondence to the binary weights?
While we know that w b = θ ( w c ) , there are many continuous weights that map onto a particular binary weight vector.
Which one is found when using the straight-through estimator to backpropagate through the binarize function?
Remarkably, there is a clear answer to this question.
In numerical experiments, we see that one gets the continuous weight vector such that the dot products of the activations with the prebinarization and post-binarization weights are highly correlated (Fig. 3).
In equations, a · w b ∼ a · w c .
We call this relation the Dot Product Proportionality (DPP) property.
The proportionality constant, which is subsequently normalized away by a batch norm layer, depends on the magnitudes of the continuous and binary weight vectors and the cosine angle between the binary and continuous weight vectors.
The theoretical consequences of the DPP property are explored in the rest of this section.
We show that the modiﬁed gradient of the BNN training algorithm can be viewed as an estimator of the gradient that would be used to train the continuous weights in traditional backpropagation.
This establishes the fundamental point that while the weights and activations are technically binary, they are operating as if the weights are continuous.
For instance, one could imagine using an exhaustive search over all binary weights in the network.
However, the additional structure in the problem associated with taking dot products makes the optimization simpler than that.
Furthermore, we show that if the dot products of the activations with the pre-binarized and post-binarized weights are proportional then straight-through estimator gradient is proportional to the continuous weight network gradient.
The key to the analysis is to focus on the transformers in the network whose forward and [Figure] backward propagation functions are not related in the way that they would normally be related in typical gradient descent.
Suppose that there is a neural network where two tensors, u , and v and the associated derivatives of the cost with respect to those tensors, δu and δv , are allocated.
Suppose that the loss as a function of v is L ( x ) | x = v .
Further, suppose that there are two potential forward propagation functions, f and g .
If the network is trained under normal conditions using g as the forward propagation function then the following computations are done:
[Equation]
) where L (cid:48) ( x ) denotes the derivative of L with respect to the vector x .
In a modiﬁed backpropagation scheme, the following computations are done
[Equation]
A sufﬁcient condition for δu to be the same in both cases is L (cid:48)
( x = f ( u )) ∼ L (cid:48)
( x = g ( u )) where a ∼ b means that the vector a is a scalar times the vector b .
Now this general observation is applied to the binarize transformer of Fig.
Here, u is the continuous weight, w c , f ( u ) is the pointwise binarize function, g ( u ) is the identity function 2 , and L is the loss of the network as a function of the weights in a particular layer.
Given the network architecture,
L ( x ) =
M ( a · x ) where a are the activations corresponding to that layer and M is the loss as a function of the weight-activation dot products.
Then L (cid:48)
( x ) =
M (cid:48) ( a · x ) (cid:12) a where (cid:12) denotes a pointwise multiply.
Thus the sufﬁcient condition is M (cid:48) ( a · w b ) ∼ M (cid:48) ( a · w c ) .
Since the dot products are followed by a batch normalization, M ( k(cid:126)x ) =
M ( (cid:126)x ) → M (cid:48) ( (cid:126)x ) =
kM (cid:48)
( k(cid:126)x ) .
Therefore, it is sufﬁcient that a · w b ∼ a · w c , which is the DPP property.
When the DPP only approximately holds, the second derivative can be used to bound the error between the two gradients of the two learning procedures.
In summary, the learning dynamics where g is used for the forward and backward passes (i.e. training the network with continuous weights) is approximately equivalent to the modiﬁed learning dynamics ( f on the forward pass, and g on the backward pass) when we have the DPP property.
While we demonstrated that the BNN learning dynamics approximate the dynamics that one would have by training a network with continuous weights using a mixture of empirical and theoretical arguments, the ideal result would be that the learning algorithm implies the DPP property.
It should be noted that in the case of stochastic binarization where E ( w b ) = w c is chosen by deﬁnition, the
DPP property is true by design.
However, it is remarkable that the property still holds in the case of deterministic binarization, which is revealing of the fundamental nature of the representations used in neural networks.
While the main focus of this section is the binarization of the weights, the arguments presented can also be applied to the binarize block that corresponds to the non-linearity of the network.
The analogue of the DPP property for this binarize block is: w b · a c ∼ w b · a b where a c denotes the pre-binarized (post-batch norm) activations and a b = a denotes the binarized activations.
This property is empirically veriﬁed to hold.
For the sake of completeness, the dot product histogram corresponding to w c · a c ∼ w b · a b is also computed, although it doesn’t directly correspond to removing one instance of a binarize transformer.
This property is also empirically veriﬁed to hold (SI, Fig. 5).
Impact on Classiﬁcation: It is natural to ask to what extent the classiﬁcation performance depends on the binarization of the weights.
In experiments on CIFAR10, if the binarization of the weights on all of the convolutional layers is removed, the classiﬁcation performance drops by only 3 percent relative to the original network.
Looking at each layer individually, removing the weight binarization for the ﬁrst layer accounts for this entire percentage, and removing the binarization of the weights for each other layer causes no degradation in performance.
This result is evident by looking at the 2D dot product histograms in Fig 3.
The off-diagonal quadrants show where switching the weights from binary to continuous changes the sign of the binarized weight-activation dot product.
In all of the layers except the ﬁrst layer, there are very few dot products in the off-diagonal quadrants.
Thus we recommend the use of the dot product histograms for studying the performance of binary neural networks.
Removing the binarization of the activations has a substantial impact on the classiﬁcation performance because that removes the main non-linearity of the network.
3. I NPUT C ORRELATIONS AND THE G ENERALIZED B INARIZATION T RANSFORMATION
Not surprisingly, some distributions are impacted more strongly by binarization than others.
A binary neural network must adapt its internal representations in such a way to not be degraded too much by binarization at each layer.
In this section we explore the idea that the principal components of the input to the binarization function should be randomly oriented relative to the binarization.
While the network can adapt the higher level representations to satisfy this property, the part of the network that interfaces with the input doesn’t have that ﬂexibility.
We make the novel observation that the difﬁculties in training the ﬁrst layer of the network are tied to the intrinsic correlations in the input data.
In order to be more precise, we deﬁne the Generalized Binarization Transformation (GBT)
[Equation]
where x is a column vector, R is a ﬁxed rotation matrix, and θ is the pointwise binarization function from before.
The rows of R are called the axes of binarization.
If R is the identity matrix, then θ R = θ and the axes of binarization are the canonical basis vectors ( ..., 0 , 1 , 0 , ... ) .
R can either be chosen strategically or randomly.
The GBT changes the distribution being binarized through a rotation.
For appropriate choices of the rotation, R , the directions of the input vectors, x , are changed insigniﬁcantly by binarization.
The angle between a vector and its binarized version is dependent on the dot product: x · θ R ( x ) , which is equal to x T θ R ( x ) =
( Rx ) T θ ( Rx ) =
y · θ ( y )
where
y = Rx .
As a concrete example of the beneﬁts [Figure] of the GBT, consider the case where x ∼ N (0 , Σ) and Σ i,j = δ i,j exp(2 ki ) for k = 0 .
1 (therefore y ∼ N (0 , R Σ R T ) ).
As the dimension goes to inﬁnity, the angle between a vector drawn from this distribution and its binarized version approaches π/ 2 .
Thus binarization is destructive to vectors from this distribution.
However, if the GBT is applied with a ﬁxed random matrix 3 , the angle between the vector and its binarized version converges to 37 ◦ (Fig. 4).
Thus a random rotation can compensate for the errors incurred from directly binarizing a non-isotropic Gaussian.
Moving into how this analysis applies to a binary neural network, the network weights must approximate the important directions in the activations using binary vectors.
For instance, Gabor ﬁlters are intrinsic features in natural images and are often found in the ﬁrst layer weights of neural networks trained on natural images (e.g. Olshausen et al.
; Krizhevsky et al.
While the network has ﬂexibility in the higher layers, the ﬁrst layer must interface directly with the input where the features are not necessarily randomly oriented.
For instance, consider the 27 dimensional input to the ﬁrst set of convolutions in our network: 3 color channels of a 3 by 3 patch of an image from
CIFAR10 with the mean removed.
3 PCs capture 90 percent of the variance of this data and 4 PCs capture 94.5 percent of the variance.
The ﬁrst two PCs are spatially uniform colors.
More generally, large images such as those in IMAGENET have the same issue.
Translation invariance of the image covariance matrix implies that the principal components are the ﬁlters of the 2D Fourier transform.
Scale invariance implies a 1 /f 2 power spectrum, which results in the largest PCs corresponding to low frequencies (Field (1987)).
Another manifestation of this issue can be seen in our trained networks.
The ﬁrst layer has a much smaller dot product correlation than the other layers (Fig. 3).
To study this, we randomly permute the activations in order to generate a distribution with the same marginal statistics as the original data but independent joint statistics (a different permutation for each input image).
Such a transformation gives a distribution with a correlation equal to the normalized dot product of the weight vectors {+π/π d =[ u,θ ( u )] [ u,θ R ( u )] π/π d = π/π d =8 
 A p e r m W c Layer 1 : r = 0.+}
Layer 2 : r = 0.
Layer 3 : r = 0.
{+150 0 150 Aperm W b 
 A p e r m W c Layer 4 : r = 0. 150 0 150 Aperm W b 2525 Layer 5 : r = 0.+}
[Figure]
[Figure]
{+Aperm W b 
+}
[Figure]
Layer
[Figure]
: r = 0.
[Figure]
(SI Sec. 3).
The correlations for the higher layers decrease substantially but the correlation in the ﬁrst layer increases (Fig. 4, right).
For the ﬁrst layer, the shufﬂing operation randomly permutes the pixels in the image.
Thus we demonstrate that the binary weight vectors in the ﬁrst layer are not well-aligned with the continuous weight vectors relative to the input data.
Our theoretically grounded analysis is consistent with previous work.
Han et al.
(2015b)
ﬁnd that compressing the ﬁrst set of convolutional weights of a particular layer by the same fraction has the highest impact on performance if done on the ﬁrst layer.
Zhou et al.
ﬁnd that accuracy degrades by about 0.5 to 1 percent on SHVN when quantizing the ﬁrst layer weights.
Thus it is recommended to rotate the input data before normalization or to use continuous weights for the ﬁrst layer.
[-π/π d =[ u,θ ( u )] [ u,θ R ( u )] π/π d = π/π d =8 
-]
[-150 0 150 Aperm W b 
-]
C ONCLUSION
Neural networks with binary weights and activations have similar performance to their continuous counterparts with substantially reduced execution time and power usage.
We provide an experimentally veriﬁed theory for understanding how one can get away with such a massive reduction in precision based on the geometry of HD vectors.
First, we show that binarization of high-dimensional vectors preserves their direction in the sense that the angle between a random vector and its binarized version is much smaller than the angle between two random vectors (Angle Preservation
Property).
Second, we take the perspective of the network and show that binarization approximately preserves weight-activation dot products (Dot Product Proportionality Property).
More generally, when using a network compression technique, we recommend looking at the weight activation dot product histograms as a heuristic to help localize the layers that are most responsible for performance degradation.
Third, we discuss the impacts of the low effective dimensionality of the data on the ﬁrst layer of the network.
We recommend either using continuous weights for the ﬁrst layer or a
Generalized Binarization Transformation.
Such a transformation may be useful for architectures like LSTMs where the update for the hidden state declares a particular set of axes to be important (e.g. by taking the pointwise multiply of the forget gates with the cell state).
Finally, we show that neural networks with ternary weights and activations can also be understood with our approach.
More broadly speaking, our theory is useful for analyzing a variety of neural network compression techniques that transform the weights, activations or both to reduce the execution cost without degrading performance.
S UPPLEMENTARY I NFORMATION 5. E XPECTED A NGLES
Random n dimensional vectors are drawn from a rotationally invariant distribution.
The angle between two random vectors and the angle between a vector and its binarized version are compared.
A rotationally invariant distribution can be factorized into a pdf for the magnitude of the vector times a distribution on angles.
In the expectations that we are calculating, the magnitude cancels out and there is only one rotationally invariant distribution on angles.
Thus it sufﬁces to compute these expectations using a Gaussian.
Lemmas: 1. Consider a vector, v , chosen from a standard normal distribution of dimension n .
Let ρ =
[Equation]
√
[Equation]
21 + ... + v 2 n .
Then ρ is distributed according to: g ( ρ ) =
1 √ π
[Equation]
[Table]
[Equation]
where Γ is the Gamma function.
Proof: Begin by considering the integral
[Equation]
where I is an indicator function.
The desired distribution comes from taking the derivative of this cumulative distribution g ( ρ 0 ) =
ddρ 0 G ( ρ 0 ) .
Consider the generalized spherical coordinate transformation: v 1 = r cos φ 1 , v 2 = r sin φ 1 cos φ 2 , v 3 =
r sin φ 1 sin φ 2 cos φ 3 , ... v n − 1 =
r sin φ 1 · · · sin φ n − 2 cos φ n − 1 , v n = r sin φ 1 · · · sin φ n − 2 sin φ n − 1 where φ 1 , . .
φ n − 2 ∈ [0 , π ] and φ n − 1 ∈ [0 , 2 π ] .
The generalized spherical volume element is dv =
r n − 1 sin n − 2 ( φ 1 ) sin n − 3 ( φ 2 ) · · · sin( φ n − 2 ) drdφ 1 · · · dφ n − 1 .
Thus we can write out the integral
[Equation]
The integral factorizes and all of the terms are independent of ρ 0 except the integral over φ 1 ≡ φ .
Thus G ( ρ 0 ) ∼ (cid:82) π 0 I (cos φ < ρ 0 ) sin n − 2 ( φ ) dφ .
Using the substitution ρ = cos φ (which is also consistent with the deﬁnition of ρ above), dρ = − sin( φ ) dφ , sin ρ = (1 − ρ 2 ) 0 .
5 , so G ( ρ 0 ) ∼ (cid:82) 1 − 1 I ( ρ < ρ 0 )(1 − ρ 2 ) ( n − 3) / 2 dρ .
Taking the derivative with respect to ρ 0 and using the fundamental theorem of calculus gives g ( ρ ) ∼ (1 − ρ 2 ) ( n − 3) / 2 .
The normalization constant is equal to a beta function that evaluates to the desired result (substitute t = ρ 2 ).
[Equation]
Cases:
• Distribution of angles between two random vectors.
Since a Gaussian is a rotationally invariant distribution, we can say without loss of generality that one of the vectors is (1 , 0 , 0 , . . .
Then the cosine angle between those two vectors is ρ as deﬁned above.
While the exact distribution of ρ is given by Lemma 1, we note that
– E ( ρ ) =
due to the symmetry of the distribution.
– V ar ( ρ ) =
E ( ρ 2 ) =
1 n because 1 = E (cid:16) (cid:80) i x 2 i (cid:80) j x
[Equation]
j (cid:17) =
(cid:80) i E (cid:16)
x 2 i (cid:80) j x 2 j (cid:17) = n ∗ E ( ρ 2 ) • Angles between a vector and the binarized version of that vector, η = v · θ ( v ) ||
v ||·|| θ ( v ) ||
= (cid:80) i | v i | √ (cid:80) v 2 i ∗√ n – E ( η ) = √ n √ π ∗ Γ
( n/ 2) Γ(( n + 1) / 2) lim
n →∞ E ( η ) =
(cid:114)π First, we note E ( η ) = √ nE ( | ρ | ) .
Then E ( | ρ | ) = (cid:82) 1 0 dρ ρ 2 √ π Γ( n/ 2) Γ(( n − 1) / 2) (1 − ρ 2 ) n − 3 2 = 2 √
π ∗ 1 n −Γ( n/ 2) Γ(( n − 1) / 2) (substitute u = ρ 2 and use Γ( x + 1) =
x Γ( x ) ).
Lemma two gives the n → ∞ limit.
[Equation] [Equation]
Thus we have the normal scaling as in the central limit theorem of the large n variance.
We can calculate this explicitly following the approach of 5 .
As E ( η ) has been calculated, it sufﬁces to calculate E ( η 2 ) .
Expanding out η 2 , E ( η 2 )
= 1 n + ( n − 1)
∗ E ( | v 1 v 2 | v 21 + ...v 2 n ) .
Below we show that E ( | v 1 v 2 | v 21 + ...v 2 n ) = 2 πn .
Thus the variance is:
[Equation]
Using Lemma 2 to expand out the last term, we get [ √ n √ π
( n/ 2) − 1 / 2 (1 − 1 / (4 n ) +
O ( n − 2 ))] 2 = 2 π (1 − 1 / (2 n ) + O ( n − 2 )) .
Plugging this in gives the desired result.
Going back to the calculation of that expectation, change variables to v 1 = r cos θ , v 2 =
r sin θ , z 2 = v 23 + ... + v 2 n .
The integration over the volume element dv 3 . . .
dv n is rewritten as dzdA n − 3 where dA n denotes the surface element of a n sphere.
Since the integrand only depends on the magnitude, z , (cid:82) dA n − 3 =
z n − 3 ∗ S n − 3 where
S n = 2 π ( n +1) / 2 Γ( n +12 ) denotes the surface area of a unit n -sphere.
Then
E
[Equation]
Then substitute r = p cos φ , z = p sin φ where φ ∈ [0 , π/ 2]
[Equation]
The ﬁrst integral is 2 n ( n − 2) using u = sin 2 φ .
The second integral is 2 ( n − 2) / 2 Γ( n/ 2) using u = p 2 / 2 and the deﬁnition of the gamma function.
Simplifying, the result isπ ∗ n .
Thus the angle between a vector and a binarized version of that vector converges to arccos (cid:113) 2 π ≈ 37 ◦ , which is a very small angle in high dimensions.
5. A N E XPLICIT E XAMPLE OF L EARNING D YNAMICS
In this subsection, we look at the learning dynamics for the BNN training algorithm in a simple case and gain some insight about the learning algorithm.
Consider the case of regression where the target output, y , is predicted with a binary linear predictor with x as the input.
Using a squared error loss,
L =
( y − ˆ y ) 2 =
( y − w b x ) 2 =
( y − θ ( w c ) x ) 2 .
(In this notation, x is a column vector.)
Taking the derivative of this loss with respect to the continuous weights and using the rule for back propagating through the binarize function gives ∆ w c ∼ −
[Equation]
Finally, averaging over the training data gives
[Equation]
It is worthwhile to compare this equation the corresponding equation from typical linear regression: ∆ w c ∼ C yx − w c · C xx .
For simplicity, consider the case where C xx is the identity matrix.
In this case, all of the components of w become independent: δw = (cid:15) ∗ ( α − θ ( w )) where (cid:15) is the learning rate and α is the entry of C yx corresponding to a particular element, w .
Compared to regular linear regression, it is clear that the stable point of these equations is when w = α .
Since the weight is
[Figure]
binarized, that equation cannot be satisﬁed.
However, it can be shown ( (cid:63) ) that in this special case of binary weight linear regression, E ( θ ( w c ))
=
α .
Intuitively, if we consider a high dimensional vector and the ﬂuctuations of each component are likely to be out of phase, then w b · x ≈ w c · x is going to be correct in expectation with a variance that scales as 1 n .
During the actual learning process, we anneal the learning rate to a very small number, so the particular state of a ﬂuctuating component of the vector is frozen in.
Relatedly, the equation C yx ≈ wC xx is easier to satisfy in high dimensions, whereas in low dimensions, it is only satisﬁed in expectation.
Proof for ( (cid:63) ) : Suppose that | α | ≤ 1 .
The basic idea of these dynamics is that steps of size proportional to (cid:15) are taken whose direction depends on whether w > 0 or w < 0 .
In particular, if w > 0 , then the step is − (cid:15) · | 1 − α | and if w < 0 , the step is (cid:15) · ( α + 1) .
It is evident that after a sufﬁcient burn-in period, | w | ≤ (cid:15) ∗ max( | 1 − α | , 1+ α ) ≤ 2 (cid:15) .
Suppose w > 0 occurs with fraction p and w < 0 occurs with fraction 1 − p .
In order for w to be in equilibrium, oscillating about zero, these steps balance out on average: p (1 − α ) =
(1 − p )
(1 + α ) → p = (1 + α ) / 2 .
Then the expected value of θ ( w ) is 1 ∗ p
+ ( − 1) ∗ (1 − p ) =
α .
When | α | > 1 , the dynamics diverge because α − θ ( w ) will always have the same sign.
This divergence demonstrates the importance of some normalization technique such as batch normalization or attempting to represent w with a constant times a binary matrix.
5. D OT P RODUCT C ORRELATIONS A FTER A CTIVATION P ERMUTATION
Suppose that A = w · a and B = v · a where w, v are weight vectors and a is the vector of activations.
What is the correlation, r , between A and B ?
Assuming that E ( a ) = 0 , E ( A )
=
E ( B ) = 0 .
Then
E ( AB ) =
(cid:80) i,j w
i
v j E ( a i a j ) =
w T Cv where C i,j = E ( a i a j ) .
Setting v = w and w = v gives
E ( A 2 ) =
w T Cw and v T Cv .
Thus r = w T Cv √ ( w T Cw )( v T Cv ) .
In the case where the activations are randomly permuted, C is proportional to the identity matrix, and thus the correlation between A and B is the cosine angle between u and v .
5. A NGLE P LOT
[Figure]
5. T ERNARY N EURAL N ETWORKS
Moving beyond binarization, recent work has shown how to train neural networks where the activations are quantized to three (or more) values (e.g. Hubara et al.
Indeed, ternarization may be a more natural quantization method than binarization for neural network weights because one can express a positive association ( +1 ), a negative association ( − 1 ), or no association ( 0 ) between two features in a neural network.
We show that the analysis used on BNNs holds for ternary neural networks.
The quantization function: ter a ( x )
{+= 
+}
if x > a , 0 if | x | < a , and − 1 if x < − a is used in place of the binarize function with the same straight-through estimator for the gradient.
Call a the ternarization threshold.
The exact value of a is only important at initialization because the scaling constant of the batch normalization layer allows the network to adapt the standard deviation of the pre-nonlinearity activations to the value of a .
The network architecture from the previous experiments was used to classify images in CIFAR-10 and a was chosen to be equal to 0 .
In practice, roughly 10 percent of the ternarized weights were zero (Fig. 7a) and 2 percent of the activations were zero.
Thus the network learning process did not ignore the possibility of using zero weights.
However, more work is needed to effectively use the zero value for the activations.
The empirical distribution of angles between the continuous vectors and their ternarized counterparts is highly peaked at the value predicted by the theory (Fig. 7b).
Random vectors are chosen from a standard normal distribution of dimension d .
As in the case of binarization, the ternarized version of a vector is close in angle to the original vector in high dimensions (Fig. 7c).
These vectors are quantized using ter a for different values of a .
The peak angle varies substantially as a function of a (Fig. 7d).
Note that for a = 0 , the ternarization function collapses into the binarization function.
The empirical value of a is the ratio of the empirical threshold to the empirical standard deviation of the continuous weights.
Thus a ≈ 0 .
18 ≈ 0 .
11 for the higher layers.
Remarkably, the theoretical prediction for the peak angle as a function of a matches closely with the empirical result (Fig. 7b).
Finally, the dot product proportionality property is also shown to hold for ternary neural networks (Fig. 7e).
Thus the continuous weights found using the TNN training algorithm approximate the continuous weights that one would get if the network were trained with continuous weights and regular backpropagation.
[Figure]