TASK DEFINITION: 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.
PROBLEM: Auto-encoders are perhaps the best-known non-probabilistic methods for representation learning. They are conceptually simple and easy to train. Recent theoretical work has shed light on their ability to capture manifold structure, and drawn connections to density modeling. This has motivated researchers to seek ways of auto-encoder scoring, which has furthered their use in classification. Gated auto-encoders (GAEs) are an interesting and flexible extension of auto-encoders which can learn transformations among different images or pixel covariances within images. However, they have been much less studied, theoretically or empirically. In this work, we apply a dynamical systems view to GAEs, deriving a scoring function, and drawing connections to Restricted Boltzmann Machines. On a set of deep learning benchmarks, we also demonstrate their effectiveness for single and multi-label classification.

SOLUTION: Scoring and Classifying with Gated Auto-encoders

PROBLEM: Our hypothesis is that by equipping certain agents in a multi-agent system controlling an intelligent building with automated decision support, two important factors will be in­ creased. The first is energy saving in the building. The second is customer value-how the people in the building experience the ef­ fects of the actions of the agents. We give evi­ dence for the truth of this hypothesis through experimental findings related to tools for arti­ ficial decision making. A number of assump­ tions related to agent control, through moni­ toring and delegation of tasks to other kinds of agents, of rooms at a test site are relaxed. Each assumption controls at least one un­ certainty that complicates considerably the procedures for selecting actions part of each such agent. We show that in realistic deci­ sion situations, room-controlling agents can make bounded rational decisions even under dynamic real-time constraints. This result can be, and has been, generalized to other domains with even harsher time constraints.

SOLUTION: Artificial Decision Making Under Uncertainty in Intelligent Buildings

PROBLEM: This paper emphasizes the significance to jointly exploit the problem structure and the parameter structure, in the context of deep modeling. As a specific and interesting example, we describe the deep double sparsity encoder (DDSE), which is inspired by the double sparsity model for dictionary learning. DDSE simultaneously sparsities the output features and the learned model parameters, under one unified framework. In addition to its intuitive model interpretation, DDSE also possesses compact model size and low complexity. Extensive simulations compare DDSE with several carefully-designed baselines, and verify the consistently superior performance of DDSE. We further apply DDSE to the novel application domain of brain encoding, with promising preliminary results achieved.

SOLUTION:
Deep Double Sparsity Encoder: Learning to Sparsify Not Only Features But Also Parameters