instruction:
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
question:
Cold-start and efficiency issues of the Top-k recommendation are critical to large-scale recommender systems. Previous hybrid recommendation methods are effective to deal with the cold-start issues by extracting real latent factors of cold-start items(users) from side information, but they still suffer low efficiency in online recommendation caused by the expensive similarity search in real latent space. This paper presents a collaborative generated hashing (CGH) to improve the efficiency by denoting users and items as binary codes, which applies to various settings: cold-start users, cold-start items and warm-start ones. Specifically, CGH is designed to learn hash functions of users and items through the Minimum Description Length (MDL) principle; thus, it can deal with various recommendation settings. In addition, CGH initiates a new marketing strategy through mining potential users by a generative step. To reconstruct effective users, the MDL principle is used to learn compact and informative binary codes from the content data. Extensive experiments on two public datasets show the advantages for recommendations in various settings over competing baselines and analyze the feasibility of the application in marketing.
answer:
It can generate effective hash codes for efficient cold-start recommendation and meanwhile provide a feasible marketing strategy.


question:
The ability to look multiple times through a series of pose-adjusted glimpses is fundamental to human vision. This critical faculty allows us to understand highly complex visual scenes. Short term memory plays an integral role in aggregating the information obtained from these glimpses and informing our interpretation of the scene. Computational models have attempted to address glimpsing and visual attention but have failed to incorporate the notion of memory. We introduce a novel, biologically inspired visual working memory architecture that we term the Hebb-Rosenblatt memory. We subsequently introduce a fully differentiable Short Term Attentive Working Memory model (STAWM) which uses transformational attention to learn a memory over each image it sees. The state of our Hebb-Rosenblatt memory is embedded in STAWM as the weights space of a layer. By projecting different queries through this layer we can obtain goal-oriented latent representations for tasks including classification and visual reconstruction. Our model obtains highly competitive classification performance on MNIST and CIFAR-10. As demonstrated through the CelebA dataset, to perform reconstruction the model learns to make a sequence of updates to a canvas which constitute a parts-based representation. Classification with the self supervised representation obtained from MNIST is shown to be in line with the state of the art models (none of which use a visual attention mechanism). Finally, we show that STAWM can be trained under the dual constraints of classification and reconstruction to provide an interpretable visual sketchpad which helps open the `black-box' of deep learning.
answer:
A biologically inspired working memory that can be integrated in recurrent visual attention models for state of the art performance


question:
Variational autoencoders (VAEs) have been successful at learning a low-dimensional manifold from high-dimensional data with complex dependencies. At their core, they consist of a powerful Bayesian probabilistic inference model, to capture the salient features of the data. In training, they exploit the power of variational inference, by optimizing a lower bound on the model evidence. The latent representation and the performance of VAEs are heavily influenced by the type of bound used as a cost function. Significant research work has been carried out into the development of tighter bounds than the original ELBO, to more accurately approximate the true log-likelihood. By leveraging the q-deformed logarithm in the traditional lower bounds, ELBO and IWAE, and the upper bound CUBO, we bring contributions to this direction of research. In this proof-of-concept study, we explore different ways of creating these q-deformed bounds that are tighter than the classical ones and we show improvements in the performance of such VAEs on the binarized MNIST dataset.

answer:

Using the q-deformed logarithm, we derive tighter bounds than IWAE, to train variational autoencoders.