In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic framework which draws on both formal semantics and recent advances in machine learning. In particular, we separate predicates from the entities they refer to, allowing us to perform Bayesian inference based on logical forms. We describe an implementation of this framework using a combination of Restricted Boltzmann Machines and feedforward neural networks. Finally, we demonstrate the feasibility of this approach by training it on a parsed corpus and evaluating it on established similarity datasets.

A: Functional Distributional Semantics
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Q: Reactive (memoryless) policies are sufficient in completely observable Markov decision pro cesses (MDPs), but some kind of memory is usually necessary for optimal control of a par tially observable MDP. Policies with finite mem ory can be represented as finite-state automata. In this paper, we extend Baird and Moore's YAPS algorithm to the problem of learning gen eral finite-state automata. Because it performs stochastic gradient descent, this algorithm can be shown to converge to a locally optimal finite state controller. We provide the details of the algorithm and then consider the question of un der what conditions stochastic gradient descent will outperform exact gradient descent. We con clude with empirical results comparing the per formance of stochastic and exact gradient de scent, and showing the ability of our algorithm to extract the useful information contained in the sequence of past observations to compensate for the lack of observability at each time-step.

A: Learning Finite-State Controllers for Partially Observable Environments
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Q: We present Exponentiated Gradient LINUCB, an algorithm for contextual multi-armed bandits. This algorithm uses Exponentiated Gradient to find the optimal exploration of the LINUCB. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.

A:
Exponentiated Gradient LINUCB for Contextual Multi- Armed Bandits
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