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: In this paper we present an interface between a symbolic planner and a geometric task planner, which is different to a standard trajectory planner in that the former is able to perform geometric reasoning on abstract entities—tasks. We believe that this approach facilitates a more principled interface to symbolic planning, while also leaving more room for the geometric planner to make independent decisions. We show how the two planners could be interfaced, and how their planning and backtracking could be interleaved. We also provide insights for a methodology for using the combined system, and experimental results to use as a benchmark with future extensions to both the combined system, as well as to the geometric task planner.

SOLUTION: Towards Combining HTN Planning and Geometric Task Planning

PROBLEM: Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks (CNN) are able to extract higher level features that are invariant to local spectral and temporal variations. Recurrent neural networks (RNNs) are powerful in learning the longer term temporal context in the audio signals. CNNs and RNNs as classifiers have recently shown improved performances over established methods in various sound recognition tasks. We combine these two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it on a polyphonic sound event detection task. We compare the performance of the proposed CRNN method with CNN, RNN, and other established methods, and observe a considerable improvement for four different datasets consisting of everyday sound events.

SOLUTION: Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection

PROBLEM: Automatic translation from natural language descriptions into programs is a long-<lb>standing challenging problem. In this work, we consider a simple yet impor-<lb>tant sub-problem: translation from textual descriptions to If-Then programs. We<lb>devise a novel neural network architecture for this task which we train end-to-<lb>end. Specifically, we introduce Latent Attention, which computes multiplicative<lb>weights for the words in the description in a two-stage process with the goal of<lb>better leveraging the natural language structures that indicate the relevant parts for<lb>predicting program elements. Our architecture reduces the error rate by 28.57%<lb>compared to prior art [3]. We also propose a one-shot learning scenario of If-Then<lb>program synthesis and simulate it with our existing dataset. We demonstrate a<lb>variation on the training procedure for this scenario that outperforms the original<lb>procedure, significantly closing the gap to the model trained with all data.

SOLUTION:
Latent Attention For If-Then Program Synthesis