**GibbsNet**

Directed latent variable models that formulate the joint distribution as $p(x,z) = p(z) p(x \mid z)$ have the advantage of fast and exact sampling. However, these models have the weakness of needing to specify $p(z)$, often with a simple fixed prior that limits the expressiveness of the model. Undirected latent variable models discard the requirement that $p(z)$ be specified with a prior, yet sampling from them generally requires an iterative procedure such as blocked Gibbs-sampling that may require many steps to draw samples from the joint distribution $p(x, z)$. We propose a novel approach to learning the joint distribution between the data and a latent code which uses an adversarially learned iterative procedure to gradually refine the joint distribution, $p(x, z)$, to better match with the data distribution on each step. GibbsNet is the best of both worlds both in theory and in practice. Achieving the speed and simplicity of a directed latent variable model, it is guaranteed (assuming the adversarial game reaches the virtual training criteria global minimum) to produce samples from $p(x, z)$ with only a few sampling iterations. Achieving the expressiveness and flexibility of an undirected latent variable model, GibbsNet does away with the need for an explicit $p(z)$ and has the ability to do attribute prediction, class-conditional generation, and joint image-attribute modeling in a single model which is not trained for any of these specific tasks. We show empirically that GibbsNet is able to learn a more complex $p(z)$ and show that this leads to improved inpainting and iterative refinement of $p(x, z)$ for dozens of steps and stable generation without collapse for thousands of steps, despite being trained on only a few steps. … **Dpush**

Herein this paper is presented a novel invention – called Dpush – that enables truly scalable spam resistant uncensorable automatically encrypted and inherently authenticated messaging; thus restoring our ability to exert our right to private communication, and thus a step forward in restoring an uncorrupted democracy. Using a novel combination of a distributed hash table (DHT) and a proof of work (POW), combined in a way that can only be called a synergy, the emergent property of a scalable and spam resistant unsolicited messaging protocol elegantly emerges. Notable is that the receiver does not need to be online at the time the message is sent. This invention is already implemented and operating within the package that is called MORPHiS – which is a Sybil resistant enhanced Kademlia DHT implementation combined with an already functioning implementation of Dpush, as well as a polished HTTP Dmail interface to send and receive such messages today. … **Segmental Recurrent Neural Network (SRNN)**

We introduce segmental recurrent neural networks (SRNNs) which define, given an input sequence, a joint probability distribution over segmentations of the input and labelings of the segments. Representations of the input segments (i.e., contiguous subsequences of the input) are computed by encoding their constituent tokens using bidirectional recurrent neural nets, and these ‘segment embeddings’ are used to define compatibility scores with output labels. These local compatibility scores are integrated using a global semi-Markov conditional random field. Both fully supervised training – in which segment boundaries and labels are observed – as well as partially supervised training – in which segment boundaries are latent – are straightforward. Experiments on handwriting recognition and joint Chinese word segmentation/POS tagging show that, compared to models that do not explicitly represent segments such as BIO tagging schemes and connectionist temporal classification (CTC), SRNNs obtain substantially higher accuracies. …

# If you did not already know

**30**
*Saturday*
Dec 2017

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