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.

Ex Input:
Claims from the fields of network neuroscience and connectomics suggest that topological models of the brain involving complex networks are of particular use and interest. The field of deep neural networks has mostly left inspiration from these claims out. In this paper, we propose three architectures and use each of them to explore the intersection of network neuroscience and deep learning in an attempt to bridge the gap between the two fields. Using the teachings from network neuroscience and connectomics, we show improvements over the ResNet architecture, we show a possible connection between early training and the spectral properties of the network, and we show the trainability of a DNN based on the neuronal network of C.Elegans.

Ex Output:
We explore the intersection of network neurosciences and deep learning. 


Ex Input:
Pre-trained deep neural network language models such as ELMo, GPT, BERT and XLNet have recently achieved state-of-the-art performance on a variety of language understanding tasks. However, their size makes them impractical for a number of scenarios, especially on mobile and edge devices. In particular, the input word embedding matrix accounts for a significant proportion of the model's memory footprint, due to the large input vocabulary and embedding dimensions. Knowledge distillation techniques have had success at compressing large neural network models, but they are ineffective at yielding student models with vocabularies different from the original teacher models. We introduce a novel knowledge distillation technique for training a student model with a significantly smaller vocabulary as well as lower embedding and hidden state dimensions. Specifically, we employ a dual-training mechanism that trains the teacher and student models simultaneously to obtain optimal word embeddings for the student vocabulary. We combine this approach with learning shared projection matrices that transfer layer-wise knowledge from the teacher model to the student model. Our method is able to compress the BERT-BASE model by more than 60x, with only a minor drop in downstream task metrics, resulting in a language model with a footprint of under 7MB. Experimental results also demonstrate higher compression efficiency and accuracy when compared with other state-of-the-art compression techniques.

Ex Output:
We present novel distillation techniques that enable training student models with different vocabularies and compress BERT by 60x with minor performance drop.


Ex Input:
Prepositions are among the most frequent words. Good prepositional representation  is of great syntactic and semantic interest  in computational linguistics. Existing methods on preposition representation either treat prepositions as content words (e.g., word2vec and GloVe) or depend heavily on external linguistic resources including syntactic parsing, training task and dataset-specific representations. In this paper we use word-triple counts (one of the words is a preposition) to  capture the preposition's interaction with its head and children. Prepositional  embeddings are derived via tensor decompositions on a large unlabeled corpus.   We reveal a new geometry involving Hadamard products and empirically demonstrate its utility in paraphrasing of phrasal verbs. Furthermore, our prepositional  embeddings are used as simple features to two challenging downstream tasks: preposition selection and prepositional attachment disambiguation. We achieve comparable to or better results than state of the art on  multiple standardized datasets.  

Ex Output:

This work is about tensor-based method for preposition representation training.