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.
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Question: Acoustic models using probabilistic linear discriminant analysis (PLDA) capture the correlations within feature vectors using subspaces which do not vastly expand the model. This allows high dimensional and correlated feature spaces to be used, without requiring the estimation of multiple high dimension covariance matrices. In this letter we extend the recently presented PLDA mixture model for speech recognition through a tied PLDA approach, which is better able to control the model size to avoid overfitting. We carried out experiments uisng the Switchboard corpus, with both mel frequency cepstral coefficient features and bottleneck feature derived from a deep neural network. Reductions in word error rate were obtained by using tied PLDA, compared with the PLDA mixture model, subspace Gaussian mixture models, and deep neural networks.

Answer: Tied Probabilistic Linear Discriminant Analysis for Speech Recognition


Question: Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classi­ cal notion of Value of Informationthe expected im­ provement in future decision quality arising from the tnformation acquired by exploration. Estimating this quantity requires an assessment of the agent's uncer­ tainty about its current value estimates for states. In this paper we investigate ways to represent and rea­ son about this uncertainty in algorithms where the sys­ tem attempts to learn a model of its environment. We explicitly represent uncertainty about the parameters of the model and build probability distributions over Q­ values based on these. These distributions are used to compute a myopic approximation to the value of infor­ mation for each action and hence to select the action that best balances exploration and exploitation.

Answer: Model based Bayesian Exploration


Question: In this paper, we analyze a generic algorithm scheme for sequential global optimization using Gaussian processes. The upper bounds we derive on the cumulative regret for this generic algorithm improve by an exponential factor the previously known bounds for algorithms like GP-UCB. We also introduce the novel Gaussian Process Mutual Information algorithm (GP-MI), which significantly improves further these upper bounds for the cumulative regret. We confirm the efficiency of this algorithm on synthetic and real tasks against the natural competitor, GP-UCB, and also the Expected Improvement heuristic. Preprint for the 31st International Conference on Machine Learning (ICML 2014) 1 ar X iv :1 31 1. 48 25 v3 [ st at .M L ] 8 J un 2 01 5 Erratum After the publication of our article, we found an error in the proof of Lemma 1 which invalidates the main theorem. It appears that the information given to the algorithm is not sufficient for the main theorem to hold true. The theoretical guarantees would remain valid in a setting where the algorithm observes the instantaneous regret instead of noisy samples of the unknown function. We describe in this page the mistake and its consequences. Let f : X → R be the unknown function to be optimized, which is a sample from a Gaussian process. Let’s fix x, x1, . . . , xT ∈ X and the observations yt = f(xt)+ t where the noise variables t are independent Gaussian noise N (0, σ). We define the instantaneous regret rt = f(x?)− f(xt) and, MT = T ∑

Answer:
Gaussian Process Optimization with Mutual Information