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.

While extremely successful in several applications, especially with low-level representations; sparse, noisy samples and structured domains (with multiple objects and interactions) are some of the open challenges in most deep models. Column Networks, a deep architecture, can succinctly capture such domain structure and interactions, but may still be prone to sub-optimal learning from sparse and noisy samples. Inspired by the success of human-advice guided learning in AI, especially in data-scarce domains, we propose Knowledge-augmented Column Networks that leverage human advice/knowledge for better learning with noisy/sparse samples. Our experiments demonstrate how our approach leads to either superior overall performance or faster convergence.
Guiding relation-aware deep models towards better learning with human knowledge.

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.
We present a novel algorithm for solving reinforcement learning and bandit structured prediction problems with very sparse loss feedback.

The question why deep learning algorithms generalize so well has attracted increasing
 research interest. However, most of the well-established approaches,
 such as hypothesis capacity, stability or sparseness, have not provided complete
 explanations (Zhang et al., 2016; Kawaguchi et al., 2017). In this work, we focus
 on the robustness approach (Xu & Mannor, 2012), i.e., if the error of a hypothesis
 will not change much due to perturbations of its training examples, then it
 will also generalize well. As most deep learning algorithms are stochastic (e.g.,
 Stochastic Gradient Descent, Dropout, and Bayes-by-backprop), we revisit the robustness
 arguments of Xu & Mannor, and introduce a new approach – ensemble
 robustness – that concerns the robustness of a population of hypotheses. Through
 the lens of ensemble robustness, we reveal that a stochastic learning algorithm can
 generalize well as long as its sensitiveness to adversarial perturbations is bounded
 in average over training examples. Moreover, an algorithm may be sensitive to
 some adversarial examples (Goodfellow et al., 2015) but still generalize well. To
 support our claims, we provide extensive simulations for different deep learning
 algorithms and different network architectures exhibiting a strong correlation between
 ensemble robustness and the ability to generalize.

Explaining the generalization of stochastic deep learning algorithms, theoretically and empirically, via ensemble robustness