We propose interpretable deep Gaussian Processes (GPs) that combine the expressiveness of deep Neural Networks (NNs) with quantified uncertainty of deep GPs. Our approach is based on approximating deep GP as a GP, which allows explicit, analytic forms for compositions of a wide variety of kernels. Consequently, our approach admits interpretation as both NNs with specified activation functions and as a variational approximation to deep GPs. We provide general recipes for deriving the effective kernels for deep GPs of two, three, or infinitely many layers, composed of homogeneous or heterogeneous kernels. Results illustrate the expressiveness of our effective kernels through samples from the prior and inference on simulated data and demonstrate advantages of interpretability by analysis of analytic forms, drawing relations and equivalences across kernels, and a priori identification of non-pathological regimes of hyperparameter space.
Williams @cite_18 was first to derive analytic kernels of one-layer neural networks, using sigmoidal and Gaussian activation functions. Later, Cho and Saul @cite_14 extended analytic results to polynomial activation functions and further made extension to deep models. Daniely al @cite_20 extended these results for homogeneous deep networks with 2nd hermite, step, and exponential activation functions and Poole al @cite_5 to @math activation functions. Our approach differs by focusing on deep GPs for Bayesian inference, and allowing heterogeneous kernels.
@cite_5 @cite_18 @cite_14 @cite_20
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We combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in generic, deep neural networks with random weights. Our results reveal an order-to-chaos expressivity phase transition, with networks in the chaotic phase computing nonlinear functions whose global curvature grows exponentially with depth but not width. We prove this generic class of deep random functions cannot be efficiently computed by any shallow network, going beyond prior work restricted to the analysis of single functions. Moreover, we formalize and quantitatively demonstrate the long conjectured idea that deep networks can disentangle highly curved manifolds in input space into flat manifolds in hidden space. Our theoretical analysis of the expressive power of deep networks broadly applies to arbitrary nonlinearities, and provides a quantitative underpinning for previously abstract notions about the geometry of deep functions. We introduce a new family of positive-definite kernel functions that mimic the computation in large, multilayer neural nets. These kernel functions can be used in shallow architectures, such as support vector machines (SVMs), or in deep kernel-based architectures that we call multilayer kernel machines (MKMs). We evaluate SVMs and MKMs with these kernel functions on problems designed to illustrate the advantages of deep architectures. On several problems, we obtain better results than previous, leading benchmarks from both SVMs with Gaussian kernels as well as deep belief nets. We develop a general duality between neural networks and compositional kernel Hilbert spaces. We introduce the notion of a computation skeleton, an acyclic graph that succinctly describes both a family of neural networks and a kernel space. Random neural networks are generated from a skeleton through node replication followed by sampling from a normal distribution to assign weights. The kernel space consists of functions that arise by compositions, averaging, and non-linear transformations governed by the skeleton's graph topology and activation functions. We prove that random networks induce representations which approximate the kernel space. In particular, it follows that random weight initialization often yields a favorable starting point for optimization despite the worst-case intractability of training neural networks.