Volume 2

By Servicio Geológico Colombiano Sgc. 2019

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

Citations in the corpus

Story Ending Prediction by Transferable BERT (2019, background): “5 [Sharma et al., 2018] is a recently released revised version in order to overcome the human-authorship biases [Schwartz et al.”
Recent Advances in Imitation Learning from Observation (2019, background): “Considering the problem of imitation learning using state-only demonstrations is not new [Ijspeert et al. , 2002; Bentivegna et al. , 2002].”
What to Do Next: Modeling User Behaviors by Time-LSTM (2017, methodology): “To achieve this, we adapt the dynamic updated model in [Mikolov et al., 2010] to our task as follows.”
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review (2020, background): “[Miller et al., 2003] proposed a taxonomybased approach, which classified objects into categories that should be grasped by each canonical grasp.”
Generative Replay-based Continual Zero-Shot Learning (2021, unknown): “Then at testing time, predict a semantic vector for unseen class samples and assign a class based on the nearest neighbor algorithm [Socher et al., 2013; Mishra et al., 2018; Verma and Rai, 2017; Wang et al., 2016].”
Self-Supervised Tuning for Few-Shot Segmentation (2020, background): “2, a Siamese network fe [Koch et al., 2015] is first proposed to extract the features of input query and support images [Zhang et al., 2012].”
The infinitesimal generator of the stochastic Burgers equation (2018, background): “In recent years, new solution theories like regularity structures [Hai14, FH14] or paracontrolled distributions [GIP15, GP17] were developed for singular SPDEs, see [Gub18] for an up-to-date and fairly exhaustive review.”
The Minds of Many: Opponent Modeling in a Stochastic Game (2017, methodology): “In AI, opponent modelling is used for decision making by exploiting sub-optimal opponents [Ganzfried and Sandholm, 2011].”
Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations (2018, background): “, 2016], autonomous driving [Bojarski and others, 2016], and even predicting census data [Gebru et al., 2017].”
Sharing Experience in Multitask Reinforcement Learning (2019, background): “There are several works [Schulman et al., 2015; Kakade and Langford, 2002] presenting theoretical constraint on policy-update that guarantees monotonic convergence.”