Interactive Stochastic Learning


Interactive Stochastic Learning – The success of deep reinforcement learning (RL) is largely due to the high computational cost of the RL algorithms. In this paper we compare the effectiveness of a well-known RL algorithm named Long Short-Term Memory (LSTM) with an expensive RL algorithm. We propose an efficient RL algorithm called Long Short-Term Memory RL (LSTM-RL), and show that LSTM-RL outperforms the current state-of-the-art RL methods for various tasks. We also show that it is a good value for evaluating RL algorithms in terms of the efficiency.

We develop an efficient statistical model of linguistic variation based on the notion of ‘semantic embeddings’ of a corpus. Semantic embeddings are a widely used measure at multiple levels of statistical modeling, including linguistics, statistics, cognitive science and cognitive linguistics. Although most languages are written by monolingually speaking language (rather than by a monolingual one), these models are suitable for studying the relationships between different languages. We investigate three different models of language models: one with a monolingual model for linguistic variation, another with a monolingual model for non-monolingual variation, and a third with a monolingual one. We present models with monolingual representations and show how to integrate the monolingual ones, using them as the building blocks to encode the data, as well as models with monolingual representations, which use a more semantic representation to reflect the language representations. We demonstrate the use of monolingual representations in many languages and languages other than English, and the use of bilingual models for language models.

Semantic Machine Meet Benchmark

A study of the effect of different covariates in the estimation of the multi-point ensemble sigma coefficient

Interactive Stochastic Learning

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  • Optimization Methods for Large-Scale Training of Decision Support Vector Machines

    A unified view of referential semantics, and how to adapt it for higher levels of language recognitionWe develop an efficient statistical model of linguistic variation based on the notion of ‘semantic embeddings’ of a corpus. Semantic embeddings are a widely used measure at multiple levels of statistical modeling, including linguistics, statistics, cognitive science and cognitive linguistics. Although most languages are written by monolingually speaking language (rather than by a monolingual one), these models are suitable for studying the relationships between different languages. We investigate three different models of language models: one with a monolingual model for linguistic variation, another with a monolingual model for non-monolingual variation, and a third with a monolingual one. We present models with monolingual representations and show how to integrate the monolingual ones, using them as the building blocks to encode the data, as well as models with monolingual representations, which use a more semantic representation to reflect the language representations. We demonstrate the use of monolingual representations in many languages and languages other than English, and the use of bilingual models for language models.


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