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.

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However, the undirected setting is often insufficient: only a causal (i.e., directed) network provides

This paper concerns, more specifically, the inconsistency of separating sets used to remove dispensable edges, iteratively, based on conditional independence tests.

Wepresent MubyNet -afeed-forward,multitask, bottom upsystem fortheintegrated localization, as well as 3d pose and shape estimation, of multiple people inmonocular images.

Recovering the skeletal shape of an animal from a monocular video is a longstanding challenge.

'Sharkansas' contains entire fossilized skeletons dating back 320 million years. Breakthroughs, discoveries, and DIY tips sent six days a week. Arkansas is hundreds of miles from the Gulf of Mexico, but it's home to countless sharks . A trove of the fossilized predator's remains are embedded within the Fayetteville Shale --a roughly 350-million-year-old geological formation in the state's northwestern corner. Because a shark's cartilage skeleton decomposes so quickly, they usually only leave teeth behind when they die.