instruction:
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.
question:
Whereas it is believed that techniques such as Adam, batch normalization and, more recently, SeLU nonlinearities ''solve'' the exploding gradient problem, we show that this is not the case and that in a range of popular MLP architectures, exploding gradients exist and that they limit the depth to which networks can be effectively trained, both in theory and in practice. We explain why exploding gradients occur and highlight the {\it collapsing domain problem}, which can arise in architectures that avoid exploding gradients.

ResNets have significantly lower gradients and thus can circumvent the exploding gradient problem, enabling the effective training of much deeper networks, which we show is a consequence of a surprising mathematical property. By noticing that {\it any neural network is a residual network}, we devise the {\it residual trick}, which reveals that introducing skip connections simplifies the network mathematically, and that this simplicity may be the major cause for their success.
answer:
We show that in contras to popular wisdom, the exploding gradient problem has not been solved and that it limits the depth to which MLPs can be effectively trained. We show why gradients explode and how ResNet handles them.

question:
While many recent advances in deep reinforcement learning rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsupervised data to learn environment dynamics. One prospect is to pursue hybrid approaches, as in AlphaGo, which combines Monte-Carlo Tree Search (MCTS)a model-based methodwith deep-Q networks (DQNs)a model-free method. MCTS requires generating rollouts, which is computationally expensive. In this paper, we propose to simulate roll-outs, exploiting the latest breakthroughs in image-to-image transduction, namely Pix2Pix GANs, to predict the dynamics of the environment. Our proposed algorithm, generative adversarial tree search (GATS), simulates rollouts up to a specified depth using both a GAN- based dynamics model and a reward predictor. GATS employs MCTS for planning over the simulated samples and uses DQN to estimate the Q-function at the leaf states. Our theoretical analysis establishes some favorable properties of GATS vis-a-vis the bias-variance trade-off and empirical results show that on 5 popular Atari games, the dynamics and reward predictors converge quickly to accurate solutions. However, GATS fails to outperform DQNs in 4 out of 5 games. Notably, in these experiments, MCTS has only short rollouts (up to tree depth 4), while previous successes of MCTS have involved tree depth in the hundreds. We present a hypothesis for why tree search with short rollouts can fail even given perfect modeling.
answer:
Surprising negative results on Model Based + Model deep RL

question:
Mainstream captioning models often follow a sequential structure to generate cap-
tions, leading to issues such as introduction of irrelevant semantics, lack of diversity
in the generated captions, and inadequate generalization performance. In this paper,
we present an alternative paradigm for image captioning, which factorizes the
captioning procedure into two stages: (1) extracting an explicit semantic represen-
tation from the given image; and (2) constructing the caption based on a recursive
compositional procedure in a bottom-up manner. Compared to conventional ones,
our paradigm better preserves the semantic content through an explicit factorization
of semantics and syntax. By using the compositional generation procedure, caption
construction follows a recursive structure, which naturally fits the properties of
human language. Moreover, the proposed compositional procedure requires less
data to train, generalizes better, and yields more diverse captions.
answer:
a hierarchical and compositional way to generate captions