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.

Example Input: In this paper, we explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly applying this approach to text is challenging–lexical features highly informative for one task maybe insignificant for another. Thus, rather than learning solely from words, our model also leverages their distributional signatures, which encode pertinent word occurrence patterns. Our model is trained within a meta-learning framework to map these signatures into attention scores, which are then used to weight the lexical representations of words. We demonstrate that our model consistently outperforms prototypical networks learned on lexical knowledge (Snell et al., 2017) in both few-shot text classification and relation classification by a significant margin across six benchmark datasets (19.96% on average in 1-shot classification).
Example Output: Meta-learning methods used for vision, directly applied to NLP, perform worse than nearest neighbors on new classes; we can do better with distributional signatures.

Example Input: We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. NLMs exploit the power of both neural networks---as function approximators, and logic programming---as a symbolic processor for objects with properties, relations, logic connectives, and quantifiers.   After being trained on small-scale tasks (such as sorting short arrays), NLMs can recover lifted rules, and generalize to large-scale tasks (such as sorting longer arrays). In our experiments, NLMs achieve perfect generalization in a number of tasks, from relational reasoning tasks on the family tree and general graphs, to decision making tasks including sorting arrays, finding shortest paths, and playing the blocks world. Most of these tasks are hard to accomplish for neural networks or inductive logic programming alone.
Example Output: We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning.

Example Input: We present the iterative two-pass decomposition flow to accelerate existing convolutional neural networks (CNNs).   The proposed rank selection algorithm can effectively determine the proper ranks of the target convolutional layers for the low rank approximation. Our two-pass CP-decomposition helps prevent from the instability problem. The iterative flow makes the decomposition of the deeper networks systematic. The experiment results shows that VGG16 can be accelerated with a 6.2x measured speedup while the accuracy drop remains only 1.2%.

Example Output:
We present the iterative two-pass CP decomposition flow to effectively accelerate existing convolutional neural networks (CNNs).