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Found 11 papers, 3 papers with code
Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States
This paper theoretically studies the implicit bias of policy gradient in terms of extrapolation to unseen initial states.
Overcoming Order in Autoregressive Graph Generation
Graph generation is a fundamental problem in various domains, including chemistry and social networks.
Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets
Overparameterization in deep learning typically refers to settings where a trained neural network (NN) has representational capacity to fit the training data in many ways, some of which generalize well, while others do not.
On the Implicit Bias of Gradient Descent for Temporal Extrapolation
When using recurrent neural networks (RNNs) it is common practice to apply trained models to sequences longer than those seen in training.
Regularizing Towards Permutation Invariance in Recurrent Models
We show that RNNs can be regularized towards permutation invariance, and that this can result in compact models, as compared to non-recurrent architectures.
The workweek is the best time to start a family -- A Study of GPT-2 Based Claim Generation
Argument generation is a challenging task whose research is timely considering its potential impact on social media and the dissemination of information.
A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis
To this end, we created a corpus of 30, 497 arguments carefully annotated for point-wise quality, released as part of this work.
Automatic Argument Quality Assessment - New Datasets and Methods
In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results.
Learning RNNs with Commutative State Transitions
Many machine learning tasks involve analysis of set valued inputs, and thus the learned functions are expected to be permutation invariant.
Automatic Argument Quality Assessment -- New Datasets and Methods
In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results.
Learning to combine Grammatical Error Corrections
We also present a spellchecker created for this task which outperforms standard spellcheckers tested on the task of spellchecking.
Ranked #7 on
Grammatical Error Correction
on BEA-2019 (test)
