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:
Graph networks have recently attracted considerable interest, and in particular in the context of semi-supervised learning. These methods typically work by generating node representations that are propagated throughout a given weighted graph.

 Here we argue that for semi-supervised learning, it is more natural to consider propagating labels in the graph instead. Towards this end, we propose a differentiable neural version of the classic Label Propagation (LP) algorithm. This formulation can be used for learning edge weights, unlike other methods where weights are set heuristically. Starting from a layer implementing a single iteration of LP, we proceed by adding several important non-linear steps that significantly enhance the label-propagating mechanism.

 Experiments in two distinct settings demonstrate the utility of our approach.

answer:
Neural net for graph-based semi-supervised learning; revisits the classics and propagates *labels* rather than feature representations


question:
Reinforcement learning algorithms rely on carefully engineered rewards from the environment that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is difficult and not scalable, motivating the need for developing reward functions that are intrinsic to the agent. 
 Curiosity is such intrinsic reward function which uses prediction error as a reward signal. In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. {\em without any extrinsic rewards}, across $54$ standard benchmark environments, including the Atari game suite. Our results show surprisingly good performance as well as a high degree of alignment between the intrinsic curiosity objective and the hand-designed extrinsic rewards of many games. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups. Game-play videos and code are at https://doubleblindsupplementary.github.io/large-curiosity/.
answer:
An agent trained only with curiosity, and no extrinsic reward, does surprisingly well on 54 popular environments, including the suite of Atari games, Mario etc.


question:
Deep neural models, such as convolutional and recurrent networks, achieve phenomenal results over spatial data such as images and text.
 However, when considering tabular data, gradient boosting of decision trees (GBDT) remains the method of choice.
 Aiming to bridge this gap, we propose \emph{deep neural forests} (DNF)
 --  a novel architecture that combines elements from decision trees as well as dense residual connections. 
 We present the results of extensive empirical study in which we examine the performance of GBDTs, DNFs and (deep) fully-connected networks. 
 These results indicate that DNFs achieve comparable results to GBDTs on tabular data, and open the door to end-to-end neural modeling of multi-modal data. To this end, we present a successful application of DNFs as part of a hybrid architecture for a multi-modal driving scene understanding classification task.
answer:

An architecture for tabular data, which emulates branches of decision trees and uses dense residual connectivity