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: This paper puts forward a new text to tensor representation that relies on information compression techniques to assign shorter codes to the most frequently used characters. This representation is language-independent with no need of pretraining and produces an encoding with no information loss. It provides an adequate description of the morphology of text, as it is able to represent prefixes, declensions, and inflections with similar vectors and are able to represent even unseen words on the training dataset. Similarly, as it is compact yet sparse, is ideal for speed up training times using tensor processing libraries. As part of this paper, we show that this technique is especially effective when coupled with convolutional neural networks (CNNs) for text classification at character-level. We apply two variants of CNN coupled with it. Experimental results show that it drastically reduces the number of parameters to be optimized, resulting in competitive classification accuracy values in only a fraction of the time spent by one-hot encoding representations, thus enabling training in commodity hardware.

Answer: Using Compressing tecniques to Encoding of Words is a possibility for faster training of CNN and dimensionality reduction of representation


Question: A typical experiment to study cognitive function is to train animals to perform tasks, while the researcher records the electrical activity of the animals neurons. The main obstacle faced, when using this type of electrophysiological experiment to uncover the circuit mechanisms underlying complex behaviors, is our incomplete access to relevant circuits in the brain. One promising approach is to model neural circuits using an artificial neural network (ANN), which can provide complete access to the “neural circuits” responsible for a behavior. More recently, reinforcement learning models have been adopted to understand the functions of cortico-basal ganglia circuits as reward-based learning has been found in mammalian brain. In this paper, we propose a Biologically-plausible Actor-Critic with Episodic Memory (B-ACEM) framework to model a prefrontal cortex-basal ganglia-hippocampus (PFC-BG) circuit, which is verified to capture the behavioral findings from a well-known perceptual decision-making task, i.e., random dots motion discrimination. This B-ACEM framework links neural computation to behaviors, on which we can explore how episodic memory should be considered to govern future decision. Experiments are conducted using different settings of the episodic memory and results show that all patterns of episodic memories can speed up learning. In particular, salient events are prioritized to propagate reward information and guide decisions. Our B-ACEM framework and the built-on experiments give inspirations to both designs for more standard decision-making models in biological system and a more biologically-plausible ANN.

Answer: Fast learning via episodic memory verified by a biologically plausible framework for prefrontal cortex-basal ganglia-hippocampus (PFC-BG) circuit


Question: In this paper, we propose a novel regularization method, RotationOut, for neural networks. 
 Different from Dropout that handles each neuron/channel independently, RotationOut regards its input layer as an entire vector and introduces regularization by randomly rotating the vector. 
 RotationOut can also be used in convolutional layers and recurrent layers with a small modification.
 We further use a noise analysis method to interpret the difference between RotationOut and Dropout in co-adaptation reduction. 
 Using this method, we also show how to use RotationOut/Dropout together with Batch Normalization. 
 Extensive experiments in vision and language tasks are conducted to show the effectiveness of the proposed method. 
 Codes will be available.

Answer:
We propose a regularization method for neural network and a noise analysis method