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.

Q: We develop a probabilistic latent-variable model to discover semantic framestypes of events and their participantsfrom corpora. We present a Dirichlet-multinomial model in which frames are latent categories that explain the linking of verb-subject-object triples, given document-level sparsity. We analyze what the model learns, and compare it to FrameNet, noting it learns some novel and interesting frames. This document also contains a discussion of inference issues, including concentration parameter learning; and a small-scale error analysis of syntactic parsing accuracy. Note: this work was originally posted online October 2012 as part of CMU MLD's Data Analysis Project requirement. This version has no new experiments or results, but has added some discussion of new related work.

A: Learning Frames from Text with an Unsupervised Latent Variable Model
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Q: An emerging way to deal with highdimensional non-euclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This work aims to elevate the notion of joint harmonic analysis to a full-fledged framework denoted as Time-Vertex Signal Processing, that links together the time-domain signal processing techniques with the new tools of graph signal processing. This entails three main contributions: (a) We provide a formal motivation for harmonic time-vertex analysis as an analysis tool for the state evolution of simple Partial Differential Equations on graphs. (b) We improve the accuracy of joint filtering operators by up-to two orders of magnitude. (c) Using our joint filters, we construct time-vertex dictionaries analyzing the different scales and the local time-frequency content of a signal. The utility of our tools is illustrated in numerous applications and datasets, such as dynamic mesh denoising and classification, still-video inpainting, and source localization in seismic events. Our results suggest that joint analysis of time-vertex signals can bring benefits to regression and learning.

A: A Time-Vertex Signal Processing Framework Scalable processing and meaningful representations for time-series on graphs
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Q: The commonly used Q-learning algorithm combined with function approximation induces systematic overestimations of state-action values. These systematic errors might cause instability, poor performance and sometimes divergence of learning. In this work, we present the AVERAGED TARGET DQN (ADQN) algorithm, an adaptation to the DQN class of algorithms which uses a weighted average over past learned networks to reduce generalization noise variance. As a consequence, this leads to reduced overestimations, more stable learning process and improved performance. Additionally, we analyze ADQN variance reduction along trajectories and demonstrate the performance of ADQN on a toy Gridworld problem, as well as on several of the Atari 2600 games from the Arcade Learning Environment.

A:
Deep Reinforcement Learning with Averaged Target DQN
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