In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: Consider Input: In this work we use the recent advances in representation learning to propose a neural architecture for the problem of natural language inference. Our approach is aligned to mimic how a human does the natural language inference process given two statements. The model uses variants of Long Short Term Memory (LSTM), attention mechanism and composable neural networks, to carry out the task. Each part of our model can be mapped to a clear functionality humans do for carrying out the overall task of natural language inference. The model is end-to-end differentiable enabling training by stochastic gradient descent. On Stanford Natural Language Inference(SNLI) dataset, the proposed model achieves better accuracy numbers than all published models in literature.

Output: A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference


Input: Consider Input: We can program a Real-Time (RT) music improvisation system in C++ without a formal semantic or we can model it with process calculi such as the Non-deterministic Timed Concurrent Constraint (ntcc) calculus. “A Concurrent Constraints Factor Oracle (FO) model for Music Improvisation” (Ccfomi) is an improvisation model specified on ntcc. Since Ccfomi improvises non-deterministically, there is no control on choices and therefore little control over the sequence variation during the improvisation. To avoid this, we extended Ccfomi using the Probabilistic Non-deterministic Timed Concurrent Constraint calculus. Our extension to Ccfomi does not change the time and space complexity of building the FO, thus making our extension compatible with RT. However, there was not a ntcc interpreter capable of RT to execute Ccfomi. We developed Ntccrt –a RT capable interpreter for ntcc– and we executed Ccfomi on Ntccrt. In the future, we plan to extend Ntccrt to execute our extension to Ccfomi.

Output: Probabilistic Extension to the Concurrent Constraint Factor Oracle Model for Music Improvisation


Input: Consider Input: Exponential Linear Units (ELUs) are a useful rectifier for constructing deep learning architectures, as they may speed up and otherwise improve learning by virtue of not have vanishing gradients and by having mean activations near zero [1]. However, the ELU activation as parametrized in [1] is not continuously differentiable with respect to its input when the shape parameter α is not equal to 1. We present an alternative parametrization which is C continuous for all values of α, making the rectifier easier to reason about and making α easier to tune. This alternative parametrization has several other useful properties that the original parametrization of ELU does not: 1) its derivative with respect to x is bounded, 2) it contains both the linear transfer function and ReLU as special cases, and 3) it is scale-similar with respect to α. The Exponential Linear Unit as described in [1] is as follows: ELU(x, α) = { x if x ≥ 0 α(exp(x)− 1) otherwise (1) Where x is the input to the function, and α is a shape parameter. The derivative of this function with respect to x is: d dx ELU(x, α) = { 1 if x ≥ 0 α exp(x) otherwise (2) In Figures 1a and 1b we plot this activation and its derivative with respect to x for different values of α. We see that when α 6= 1, the activation’s derivative is discontinuous at x = 0. Additionally we see that large values of α can cause a large (“exploding”) gradient for small negative values of x, which may make training difficult. Our alternative parametrization of the ELU, which we dub “CELU”, is simply the ELU where the activation for negative values has been modified to ensure that the derivative at x = 0 for all values of α is 1: CELU(x, α) = { x if x ≥ 0 α ( exp ( x α ) − 1 ) otherwise (3) Note that ELU and CELU are identical when α = 1: ∀x ELU(x, 1) = CELU(x, 1) (4) The derivative of the activation with respect to x and α are as follows: d dx CELU(x, α) = { 1 if x ≥ 0 exp ( x α ) otherwise (5) d dα CELU(x, α) = { 0 if x ≥ 0 exp ( x α ) ( 1− x α ) − 1 otherwise Like in ELU, derivatives for CELU can be computed efficiently by precomputing exp ( x α ) and using it for the activation and its derivatives. Unlike ELU, CELU is scale-similar as a function of x and α: CELU(x, α) = 1 c CELU(cx, cα) (6) The CELU also converges to ReLU as α approaches 0 from the right and converges to a linear “no-op” activation as α approaches∞: lim α→0+ CELU(x, α) = max(0, x) (7) lim<lb>α→∞<lb>CELU(x, α) = x<lb>(8) This gives the CELU a nice interpretation as a way to in-<lb>terpolate between a ReLU and a linear function using α.<lb>Naturally, CELU can be slightly shifted in x and y such that<lb>it converges to any arbitrary shifted ReLU, in case negative<lb>activations are desirable even for small values of α.<lb>References<lb>[1] D. Clevert, T. Unterthiner, and S. Hochreiter. Fast and accu-<lb>rate deep network learning by exponential linear units (elus).<lb>CoRR, abs/1511.07289, 2015. 1<lb>ar<lb>X<lb>iv<lb>:1<lb>70<lb>4.<lb>07<lb>48<lb>3v<lb>1<lb>[<lb>cs<lb>.L<lb>G<lb>]<lb>2<lb>4<lb>A<lb>pr<lb>2<lb>01<lb>7


Output: Continuously Differentiable Exponential Linear Units