instruction:
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.
question:
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
answer:
Protein Family Classification using Deep Learning


question:
Music relies heavily on repetition to build structure and meaning.   Self-reference occurs on multiple timescales, from motifs to phrases to reusing of entire sections of music, such as in pieces with ABA structure.   The Transformer (Vaswani et al., 2017), a sequence model based on self-attention, has achieved compelling results in many generation tasks that require maintaining long-range coherence. This suggests that self-attention might also be well-suited to modeling music. In musical composition and performance, however, relative timing is critically important.   Existing approaches for representing relative positional information in the Transformer modulate attention based on pairwise distance (Shaw et al., 2018).   This is impractical for long sequences such as musical compositions since their memory complexity is quadratic in the sequence length.   We propose an algorithm that reduces the intermediate memory requirements to linear in the sequence length. This enables us to demonstrate that a Transformer with our modified relative attention mechanism can generate minute-long (thousands of steps) compositions with compelling structure, generate continuations that coherently elaborate on a given motif, and in a seq2seq setup generate accompaniments conditioned on melodies.    We evaluate the Transformer with our relative attention mechanism on two datasets, JSB Chorales and Piano-e-competition, and obtain state-of-the-art results on the latter.
answer:
We show the first successful use of Transformer in generating music that exhibits long-term structure. 


question:
Stochastic neural networks with discrete random variables are an important class of models for their expressivity and interpretability. Since direct differentiation and backpropagation is not possible, Monte Carlo gradient estimation techniques have been widely employed for training such models. Efficient stochastic gradient estimators, such Straight-Through and Gumbel-Softmax, work well for shallow models with one or two stochastic layers. Their performance, however, suffers with increasing model complexity.
 In this work we focus on stochastic networks with multiple layers of Boolean latent variables. To analyze such such networks, we employ the framework of harmonic analysis for Boolean functions.   We use it to derive an analytic formulation for the source of bias in the biased Straight-Through estimator. Based on the analysis we propose \emph{FouST}, a simple gradient estimation algorithm that relies on three simple bias reduction steps. Extensive experiments show that FouST performs favorably compared to state-of-the-art biased estimators, while being much faster than unbiased ones. To the best of our knowledge FouST is the first gradient estimator to train up very deep stochastic neural networks, with up to 80 deterministic and 11 stochastic layers.
answer:

We present a low-bias estimator for Boolean stochastic variable models with many stochastic layers.