Sequence-to-sequence models have been applied to the conversation response generation problem where the source sequence is the conversation history and the target sequence is the response.Unlike translation, conversation responding is inherently creative.The generation of long, informative, coherent, and diverse responses remains a hard task.In this work, we focus on the single turn setting.We add self-attention to the decoder to maintain coherence in longer responses, and we propose a practical approach, called the glimpse-model, for scaling to large datasets.We introduce a stochastic beam-search algorithm with segment-by-segment reranking which lets us inject diversity earlier in the generation process.We trained on a combined data set of over 2.3B conversation messages mined from the web.In human evaluation studies, our method produces longer responses overall, with a higher proportion rated as acceptable and excellent as length increases, compared to baseline sequenceto-sequence models with explicit lengthpromotion.A back-off strategy produces better responses overall, in the full spectrum of lengths.

Image annotation is a typical area where there are multiple types of attributes associated with each individual image. In order to achieve better performance, it is important to develop effective modeling by utilizing prior knowledge. In this article, we extend the graph regularization approaches to a more general case where the regularization is imposed on the factorized variational distributions, instead of posterior distributions implicitly involved in EM-like algorithms. In this way, the problem modeling can be more flexible, and we can choose any factor in the problem domain to impose graph regularization wherever there are similarity constraints among the instances. We formulate the problem formally and show its geometrical background in manifold learning. We also design two practically effective algorithms and analyze their properties such as the convergence. Finally, we apply our approach to image annotation and show the performance improvement of our algorithm.

Gaussian Mixture Models (GMMs) are among the most statistically mature methods for clustering. Each cluster is represented by a Gaussian distribution. The clustering process thereby turns to estimate the parameters of the Gaussian mixture, usually by the Expectation-Maximization algorithm. In this paper, we consider the case where the probability distribution that generates the data is supported on a submanifold of the ambient space. It is natural to assume that if two points are close in the intrinsic geometry of the probability distribution, then their conditional probability distributions are similar. Specifically, we introduce a regularized probabilistic model based on manifold structure for data clustering, called Laplacian regularized Gaussian Mixture Model (LapGMM). The data manifold is modeled by a nearest neighbor graph, and the graph structure is incorporated in the maximum likelihood objective function. As a result, the obtained conditional probability distribution varies smoothly along the geodesics of the data manifold. Experimental results on real data sets demonstrate the effectiveness of the proposed approach.

This article presents a global optimization approach to reconstruct surfaces from a single document image. Instead of assuming globally developable in previous works which restricted the surface to be cylindrical, conical, etc, we use a free form parametric model which is simple yet expressive enough in the reconstruction task. We then apply developable constraints locally on sample points from extracted feature curves. And in order to further achieve better stability, the developable constraint we put on it is not so strong. Instead of isometric or conformal constraints used frequently in surface parametrization tasks, we use orthogonality. We show that even this is enough to reconstruct a wide class of document surfaces even with an uncalibrated camera.

We propose a novel technique for semi-supervised image annotation which introduces a harmonic regularizer based on the graph Laplacian of the data into the probabilistic semantic model for learning latent topics of the images. By using a probabilistic semantic model, we connect visual features and textual annotations of images by their latent topics. Meanwhile, we incorporate the manifold assumption into the model to say that the probabilities of latent topics of images are drawn from a manifold, so that for images sharing similar visual features or the same annotations, their probability distribution of latent topics should also be similar. We create a nearest neighbor graph to model the manifold and propose a regularized EM algorithm to simultaneously learn a generative model and assign probability density of latent topics to images discriminatively. In this way, databases with very few labeled images can be annotated better than previous works.