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: Neural networks can converge faster with help from a smarter batch selection strategy. In this regard, we propose Ada-Boundary, a novel adaptive-batch selection algorithm that constructs an effective mini-batch according to the learning progress of the model.Our key idea is to present confusing samples what the true label is. Thus, the samples near the current decision boundary are considered as the most effective to expedite convergence. Taking advantage of our design, Ada-Boundary maintains its dominance in various degrees of training difficulty. We demonstrate the advantage of Ada-Boundary by extensive experiments using two convolutional neural networks for three benchmark data sets. The experiment results show that Ada-Boundary improves the training time by up to 31.7% compared with the state-of-the-art strategy and by up to 33.5% compared with the baseline strategy.

Output: We suggest a smart batch selection technique called Ada-Boundary.


Input: Consider Input: Protein classification is responsible for the biological sequence, we came up with an idea whichdeals with the classification of proteomics using deep learning algorithm. This algorithm focusesmainly to classify sequences of protein-vector which is used for the representation of proteomics.Selection  of  the  type  protein  representation  is  challenging  based  on  which  output  in  terms  ofaccuracy is depended on, The protein representation used here is n-gram i.e.  3-gram and Kerasembedding used for biological sequences like protein. In this paper we are working on the Proteinclassification to show the strength and representation of biological sequence of the proteins

Output: Protein Family Classification using Deep Learning


Input: Consider Input: Though deep neural networks have achieved the state of the art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples. To solve the problem, some regularization adversarial training methods, constraining the output label or logit, have been studied. In this paper, we propose a novel regularized adversarial training framework ATLPA,namely Adversarial Tolerant Logit Pairing with Attention. Instead of constraining a hard distribution (e.g., one-hot vectors or logit) in adversarial training, ATLPA uses Tolerant Logit which consists of confidence distribution on top-k classes and captures inter-class similarities at the image level. Specifically, in addition to minimizing the empirical loss, ATLPA encourages attention map for pairs of examples to be similar. When applied to clean examples and their adversarial counterparts, ATLPA improves accuracy on adversarial examples over adversarial training. We evaluate ATLPA with the state of the art algorithms, the experiment results show that our method outperforms these baselines with higher accuracy. Compared with previous work, our work is evaluated under highly challenging PGD attack: the maximum perturbation $\epsilon$ is 64 and 128 with 10 to 200 attack iterations.


Output: In this paper, we propose a novel regularized adversarial training framework ATLPA,namely Adversarial Tolerant Logit Pairing with Attention.