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.

Ex Input:
Inspired by neurophysiological discoveries of navigation cells in the mammalian
 brain, we introduce the first deep neural network architecture for modeling Egocentric
 Spatial Memory (ESM). It learns to estimate the pose of the agent and
 progressively construct top-down 2D global maps from egocentric views in a spatially
 extended environment. During the exploration, our proposed ESM network
 model updates belief of the global map based on local observations using a recurrent
 neural network. It also augments the local mapping with a novel external
 memory to encode and store latent representations of the visited places based on
 their corresponding locations in the egocentric coordinate. This enables the agents
 to perform loop closure and mapping correction. This work contributes in the
 following aspects: first, our proposed ESM network provides an accurate mapping
 ability which is vitally important for embodied agents to navigate to goal locations.
 In the experiments, we demonstrate the functionalities of the ESM network in
 random walks in complicated 3D mazes by comparing with several competitive
 baselines and state-of-the-art Simultaneous Localization and Mapping (SLAM)
 algorithms. Secondly, we faithfully hypothesize the functionality and the working
 mechanism of navigation cells in the brain. Comprehensive analysis of our model
 suggests the essential role of individual modules in our proposed architecture and
 demonstrates efficiency of communications among these modules. We hope this
 work would advance research in the collaboration and communications over both
 fields of computer science and computational neuroscience.

Ex Output:
first deep neural network for modeling Egocentric Spatial Memory inspired by neurophysiological discoveries of navigation cells in mammalian brain


Ex Input:
We introduce MTLAB, a new algorithm for learning multiple related tasks with strong theoretical guarantees. Its key idea is to perform learning sequentially over the data of all tasks, without interruptions or restarts at task boundaries. Predictors for individual tasks are derived from this process by an additional online-to-batch conversion step.

 By learning across task boundaries, MTLAB achieves a sublinear regret of true risks in the number of tasks. In the lifelong learning setting, this leads to an improved generalization bound that converges with the total number of samples across all observed tasks, instead of the number of examples per tasks or the number of tasks independently. At the same time, it is widely applicable: it can handle finite sets of tasks, as common in multi-task learning, as well as stochastic task sequences, as studied in lifelong learning.

Ex Output:
A new algorithm for online multi-task learning that learns without restarts at the task borders


Ex Input:
Knowledge Bases (KBs) are becoming increasingly large, sparse and probabilistic. These KBs are typically used to perform query inferences and rule mining. But their efficacy is only as high as their completeness. Efficiently utilizing incomplete KBs remains a major challenge as the current KB completion techniques either do not take into account the inherent uncertainty associated with each KB tuple or do not scale to large KBs.

 Probabilistic rule learning not only considers the probability of every KB tuple but also tackles the problem of KB completion in an explainable way. For any given probabilistic KB, it learns probabilistic first-order rules from its relations to identify interesting patterns. But, the current probabilistic rule learning techniques perform grounding to do probabilistic inference for evaluation of candidate rules. It does not scale well to large KBs as the time complexity of inference using grounding is exponential over the size of the KB. In this paper, we present SafeLearner -- a scalable solution to probabilistic KB completion that performs probabilistic rule learning using lifted probabilistic inference -- as faster approach instead of grounding. 

 We compared SafeLearner to the state-of-the-art probabilistic rule learner ProbFOIL+ and to its deterministic contemporary AMIE+ on standard probabilistic KBs of NELL (Never-Ending Language Learner) and Yago. Our results demonstrate that SafeLearner scales as good as AMIE+ when learning simple rules and is also significantly faster than ProbFOIL+.

Ex Output:

Probabilistic Rule Learning system using Lifted Inference