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:
Machine learned large-scale retrieval systems require a large amount of training data representing query-item relevance. However, collecting users' explicit feedback is costly. In this paper, we propose to leverage user logs and implicit feedback as auxiliary objectives to improve relevance modeling in retrieval systems. Specifically, we adopt a two-tower neural net architecture to model query-item relevance given both collaborative and content information. By introducing auxiliary tasks trained with much richer implicit user feedback data, we improve the quality and resolution for the learned representations of queries and items. Applying these learned representations to an industrial retrieval system has delivered significant improvements.
answer:
We propose a novel two-tower shared-bottom model architecture for transferring knowledge from rich implicit feedbacks to predict relevance for large-scale retrieval systems.


question:
We consider reinforcement learning and bandit structured prediction problems with very sparse loss feedback: only at the end of an episode. We introduce a novel algorithm, RESIDUAL LOSS PREDICTION (RESLOPE), that solves such problems by automatically learning an internal representation of a denser reward function. RESLOPE operates as a reduction to contextual bandits, using its learned loss representation to solve the credit assignment problem, and a contextual bandit oracle to trade-off exploration and exploitation. RESLOPE enjoys a no-regret reduction-style theoretical guarantee and outperforms state of the art reinforcement learning algorithms in both MDP environments and bandit structured prediction settings.
answer:
We present a novel algorithm for solving reinforcement learning and bandit structured prediction problems with very sparse loss feedback.


question:
Adversaries in neural networks have drawn much attention since their first debut. 
 While most existing methods aim at deceiving image classification models into misclassification or crafting attacks for specific object instances in the object setection tasks, we focus on creating universal adversaries to fool object detectors and hide objects from the detectors. 
 The adversaries we examine are universal in three ways: 
 (1) They are not specific for specific object instances; 
 (2) They are image-independent; 
 (3) They can further transfer to different unknown models. 
 To achieve this, we propose two novel techniques to improve the transferability of the adversaries: \textit{piling-up} and \textit{monochromatization}. 
 Both techniques prove to simplify the patterns of generated adversaries, and ultimately result in higher transferability.
answer:

We focus on creating universal adversaries to fool object detectors and hide objects from the detectors.