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.

Input: Consider Input: 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.

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


Input: Consider Input: Deep reinforcement learning has achieved great success in many previously difficult reinforcement learning tasks, yet recent studies show that deep RL agents are also unavoidably susceptible to adversarial perturbations, similar to deep neural networks in classification tasks. Prior works mostly focus on model-free adversarial attacks and agents with discrete actions. In this work, we study the problem of continuous control agents in deep RL with adversarial attacks and propose the first two-step algorithm based on learned model dynamics. Extensive experiments on various MuJoCo domains (Cartpole, Fish, Walker, Humanoid) demonstrate that our proposed framework is much more effective and efficient than model-free based attacks baselines in degrading agent performance as well as driving agents to unsafe states.

Output: We study the problem of continuous control agents in deep RL with adversarial attacks and proposed a two-step algorithm based on learned model dynamics. 


Input: Consider Input: Edge intelligence especially binary neural network (BNN) has attracted considerable attention of the artificial intelligence community recently. BNNs significantly reduce the computational cost, model size, and memory footprint.   However, there is still a performance gap between the successful full-precision neural network with ReLU activation and BNNs. We argue that the accuracy drop of BNNs is due to their geometry. 
 We analyze the behaviour of the full-precision neural network with ReLU activation and compare it with its binarized counterpart. This comparison suggests random bias initialization as a remedy to activation saturation in full-precision networks and  leads us towards an improved BNN training. Our numerical experiments confirm our geometric intuition.


Output: Improve saturating activations (sigmoid, tanh, htanh etc.) and Binarized Neural Network with Bias Initialization