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.

Q: We develop a worst-case analysis of aggregation of binary classifier ensembles in a transductive setting, for a broad class of losses including but not limited to all convex surrogates. The result is a family of parameter-free ensemble aggregation algorithms, which are as efficient as linear learning and prediction for convex risk minimization but work without any relaxations whatsoever on many nonconvex losses like the 0-1 loss. The prediction algorithms take a familiar form, applying "link functions" to a generalized notion of ensemble margin, but without the assumptions typically made in margin-based learning all this structure follows from a minimax interpretation of loss minimization.

A: Minimax Binary Classifier Aggregation with General Losses
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Q: Besides spoken words, speech signals also carry information about speaker gender, age, and emotional state which can be used in a variety of speech analysis applications. In this paper, a divide and conquer strategy for ensemble classification has been proposed to recognize emotions in speech. Intrinsic hierarchy in emotions has been utilized to construct an emotions tree, which assisted in breaking down the emotion recognition task into smaller sub tasks. The proposed framework generates predictions in three phases. Firstly, emotions are detected in the input speech signal by classifying it as neutral or emotional. If the speech is classified as emotional, then in the second phase, it is further classified into positive and negative classes. Finally, individual positive or negative emotions are identified based on the outcomes of the previous stages. Several experiments have been performed on a widely used benchmark dataset. The proposed method was able to achieve improved recognition rates as compared to several other approaches.

A: Divide-and-Conquer based Ensemble to Spot Emotions in Speech using MFCC and Random Forest
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Q: 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 is not equal to 1. We present an alternative parametrization which is C continuous for all values of , making the rectifier easier to reason about and making 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 . The Exponential Linear Unit as described in [1] is as follows: ELU(x, ) = { x if x 0 (exp(x) 1) otherwise (1) Where x is the input to the function, and is a shape parameter. The derivative of this function with respect to x is: d dx ELU(x, ) = { 1 if x 0 exp(x) otherwise (2) In Figures 1a and 1b we plot this activation and its derivative with respect to x for different values of . We see that when 6= 1, the activation's derivative is discontinuous at x = 0. Additionally we see that large values of 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 is 1: CELU(x, ) = { x if x 0 ( exp ( x ) 1 ) otherwise (3) Note that ELU and CELU are identical when = 1: x ELU(x, 1) = CELU(x, 1) (4) The derivative of the activation with respect to x and are as follows: d dx CELU(x, ) = { 1 if x 0 exp ( x ) otherwise (5) d d CELU(x, ) = { 0 if x 0 exp ( x ) ( 1 x ) 1 otherwise Like in ELU, derivatives for CELU can be computed efficiently by precomputing exp ( x ) and using it for the activation and its derivatives. Unlike ELU, CELU is scale-similar as a function of x and : CELU(x, ) = 1 c CELU(cx, c) (6) The CELU also converges to ReLU as approaches 0 from the right and converges to a linear "no-op" activation as approaches: lim 0+ CELU(x, ) = max(0, x) (7) lim<lb><lb>CELU(x, ) = 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 .<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 .<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

A:
Continuously Differentiable Exponential Linear Units
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