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.

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.
We show that adding a constraint to TD updates stabilizes learning and allows Deep Q-learning without a target network

Training methods for deep networks are primarily variants on stochastic gradient descent. Techniques that use (approximate) second-order information are rarely used because of the computational cost and noise associated with those approaches in deep learning contexts. However, in this paper, we show how feedforward deep networks exhibit a low-rank derivative structure. This low-rank structure makes it possible to use second-order information without needing approximations and without incurring a significantly greater computational cost than gradient descent. To demonstrate this capability, we implement Cubic Regularization (CR) on a feedforward deep network with stochastic gradient descent and two of its variants. There, we use CR to calculate learning rates on a per-iteration basis while training on the MNIST and CIFAR-10 datasets. CR proved particularly successful in escaping plateau regions of the objective function. We also found that this approach requires less problem-specific information (e.g. an optimal initial learning rate) than other first-order methods in order to perform well.
We show that deep learning network derivatives have a low-rank structure, and this structure allows us to use second-order derivative information to calculate learning rates adaptively and in a computationally feasible manner.

Training conditional generative latent-variable models is challenging in scenarios where the conditioning signal is very strong and the decoder is expressive enough to generate a plausible output given only the condition; the generative model tends to ignore the latent variable, suffering from posterior collapse. We find, and empirically show, that one of the major reasons behind posterior collapse is rooted in the way that generative models are conditioned, i.e., through concatenation of the latent variable and the condition . To mitigate this problem, we propose to explicitly make the latent variables depend on the condition by unifying the conditioning and latent variable sampling, thus coupling them so as to prevent the model from discarding the root of variations . To achieve this, we develop a conditional Variational Autoencoder architecture that learns a distribution not only of the latent variables, but also of the condition, the latter acting as prior on the former . Our experiments on the challenging tasks of conditional human motion prediction and image captioning demonstrate the effectiveness of our approach at avoiding posterior collapse . Video results of our approach are anonymously provided in http://bit.ly/iclr2020
We propose a conditional variational autoencoder framework that mitigates the posterior collapse in scenarios where the conditioning signal strong enough for an expressive decoder to generate a plausible output from it.