TASK DEFINITION: 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.
PROBLEM: High performance of deep learning models typically comes at cost of considerable model size and computation time. These factors limit applicability for deployment on memory and battery constraint devices such as mobile phones or embedded systems. In this work we propose a novel pruning technique that eliminates entire filters and neurons according to their relative L1-norm as compared to the rest of the network, yielding more compression and decreased redundancy in the parameters. The resulting network is non-sparse, however, much more compact and requires no special infrastructure for its deployment. We prove the viability of our method by achieving 97.4%, 47.8% and 53% compression of LeNet-5, ResNet-56 and ResNet-110 respectively, exceeding state-of-the-art compression results reported on ResNet without losing any performance compared to the baseline. Our approach does not only exhibit good performance, but is also easy to implement on many architectures.

SOLUTION: We propose a novel structured class-blind pruning technique to produce highly compressed neural networks.

PROBLEM: This paper presents preliminary ideas of our work for auto- mated learning of Hierarchical Goal Networks in nondeter- ministic domains. We are currently implementing the ideas expressed in this paper.

SOLUTION: Learning HGNs, ND domains

PROBLEM: We propose the dense RNN, which has the fully connections from each hidden state to multiple preceding hidden states of all layers directly. As the density of the connection increases, the number of paths through which the gradient flows can be increased. It increases the magnitude of gradients, which help to prevent the vanishing gradient problem in time. Larger gradients, however, can also cause exploding gradient problem. To complement the trade-off between two problems, we propose an attention gate, which controls the amounts of gradient flows. We describe the relation between the attention gate and the gradient flows by approximation. The experiment on the language modeling using Penn Treebank corpus shows dense connections with the attention gate improve the model’s performance.

SOLUTION:
Dense RNN that has fully connections from each hidden state to multiple preceding hidden states of all layers directly.