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.

Example Input: Stochastic gradient descent (SGD) has been the dominant optimization method for training deep neural networks due to its many desirable properties. One of the more remarkable and least understood quality of SGD is that it generalizes relatively well
 on unseen data even when the neural network has millions of parameters. We hypothesize that in certain cases it is desirable to relax its intrinsic generalization properties and introduce an extension of SGD called deep gradient boosting (DGB). The key idea of DGB is that back-propagated gradients inferred using the chain rule can be viewed as pseudo-residual targets of a gradient boosting problem. Thus at each layer of a neural network the weight update is calculated by solving the corresponding boosting problem using a linear base learner. The resulting weight update formula can also be viewed as a normalization procedure of the data that arrives at each layer during the forward pass. When implemented as a separate input normalization layer (INN) the new architecture shows improved performance on image recognition tasks when compared to the same architecture without normalization layers. As opposed to batch normalization (BN), INN has no learnable parameters however it matches its performance on CIFAR10 and ImageNet classification tasks.
Example Output: What can we learn about training neural networks if we treat each layer as a gradient boosting problem?

Example Input: Temporal Difference Learning with function approximation is known to be unstable. Previous work like \citet{sutton2009fast} and \citet{sutton2009convergent} has presented alternative objectives that are stable to minimize. However, in practice, TD-learning with neural networks requires various tricks like using a target network that updates slowly \citep{mnih2015human}. In this work we propose a constraint on the TD update that minimizes change to the target values. This constraint can be applied to the gradients of any TD objective, and can be easily applied to nonlinear function approximation. We validate this update by applying our technique to deep Q-learning, and training without a target network. We also show that adding this constraint on Baird's counterexample keeps Q-learning from diverging.
Example Output: We show that adding a constraint to TD updates stabilizes learning and allows Deep Q-learning without a target network

Example Input: This paper fosters the idea that deep learning methods can be sided to classical
 visual odometry pipelines to improve their accuracy and to produce uncertainty
 models to their estimations. We show that the biases inherent to the visual odom-
 etry process can be faithfully learnt and compensated for, and that a learning ar-
 chitecture associated to a probabilistic loss function can jointly estimate a full
 covariance matrix of the residual errors, defining a heteroscedastic error model.
 Experiments on autonomous driving image sequences and micro aerial vehicles
 camera acquisitions assess the possibility to concurrently improve visual odome-
 try and estimate an error associated to its outputs.
Example Output:
This paper discusses different methods of pairing VO with deep learning and proposes a simultaneous prediction of corrections and uncertainty.