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.
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Question: Deep neural networks use deeper and broader structures to achieve better performance and consequently, use increasingly more GPU memory as well. However, limited GPU memory restricts many potential designs of neural networks. In this paper, we propose a reinforcement learning based variable swapping and recomputation algorithm to reduce the memory cost, without sacrificing the accuracy of models. Variable swapping can transfer variables between CPU and GPU memory to reduce variables stored in GPU memory. Recomputation can trade time for space by removing some feature maps during forward propagation. Forward functions are executed once again to get the feature maps before reuse. However, how to automatically decide which variables to be swapped or recomputed remains a challenging problem. To address this issue, we propose to use a deep Q-network(DQN) to make plans. By combining variable swapping and recomputation, our results outperform several well-known benchmarks.

Answer: We propose a reinforcement learning based variable swapping and recomputation algorithm to reduce the memory cost.

Question: Recurrent Neural Networks architectures excel at processing sequences by
modelling dependencies over different timescales. The recently introduced
Recurrent Weighted Average (RWA) unit captures long term dependencies
far better than an LSTM on several challenging tasks. The RWA achieves
this by applying attention to each input and computing a weighted average
over the full history of its computations. Unfortunately, the RWA cannot
change the attention it has assigned to previous timesteps, and so struggles
with carrying out consecutive tasks or tasks with changing requirements.
We present the Recurrent Discounted Attention (RDA) unit that builds on
the RWA by additionally allowing the discounting of the past.
We empirically compare our model to RWA, LSTM and GRU units on
several challenging tasks. On tasks with a single output the RWA, RDA and
GRU units learn much quicker than the LSTM and with better performance.
On the multiple sequence copy task our RDA unit learns the task three
times as quickly as the LSTM or GRU units while the RWA fails to learn at
all. On the Wikipedia character prediction task the LSTM performs best
but it followed closely by our RDA unit. Overall our RDA unit performs
well and is sample efficient on a large variety of sequence tasks.

Answer: We introduce the Recurrent Discounted Unit which applies attention to any length sequence in linear time

Question: We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks.
We model the order stream as a stochastic process with finite history dependence, and employ a conditional Wasserstein GAN to capture history dependence of orders in a stock market.
We test our approach with actual market and synthetic data on a number of different statistics, and find the generated data to be close to real data.

Answer:
We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks.