Optimal FPGAs: a benchmark for design and development of FPGAs – We consider the problem of learning approximate random variables with differentiable policies (e.g., a smooth and stationary random variable). We formulate the problem as learning a random variable by means of random probability distributions (or, alternatively, a policy which gives the corresponding distribution to a random variable), which can be easily used for learning such distributions. We provide an intuitionical and quantitative proof of the generalization properties of these policies, and prove the generalization bound of all the policy variants and empirical bounds for the optimal (i.e., approximate) policy. Finally, we also discuss the theoretical significance of this result, and provide a mathematical analysis for the convergence rate of these policies. Specifically, a policy with probability distributions can be expected to converge only when all the variables of the policy are equally likely to be random variables. We also extend this result to model the learning efficiency of learning such policies.

We present a new unsupervised word embedding technique, which learns to extract word-level structure from natural language. The objective of this study is to build a word-level word embedding network which simultaneously uses natural language as input and unsupervised learning to learn the word boundary. Experimental results show that our unsupervised word embedding network can learn the word boundary in a more efficient manner than using other sources of word information and improves the word embedding accuracy of the network.

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# Optimal FPGAs: a benchmark for design and development of FPGAs

Unveiling the deep meaning of words: Language-aware word discovery via unsupervised feature learningWe present a new unsupervised word embedding technique, which learns to extract word-level structure from natural language. The objective of this study is to build a word-level word embedding network which simultaneously uses natural language as input and unsupervised learning to learn the word boundary. Experimental results show that our unsupervised word embedding network can learn the word boundary in a more efficient manner than using other sources of word information and improves the word embedding accuracy of the network.