instruction:
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.
question:
Despite being so vital to success of Support Vector Machines, the principle of separating margin maximisation is not used in deep learning. We show that minimisation of margin variance and not maximisation of the margin is more suitable for improving generalisation in deep architectures. We propose the Halfway loss function that minimises the Normalised Margin Variance (NMV) at the output of a deep learning models and evaluate its performance against the Softmax Cross-Entropy loss on the MNIST, smallNORB and CIFAR-10 datasets.
answer:
Effects of the optimisation of the margin distribution on generalisation in deep architectures


question:
Encoding finite linear CSPs as Boolean formulas and solving them by using modern SAT solvers has proven to be highly effective, as exemplified by the award-winning sugar system. We here develop an alternative approach based on ASP. This allows us to use first-order encodings providing us with a high degree of flexibility for easy experimentation with different implementations. The resulting system aspartame re-uses parts of sugar for parsing and normalizing CSPs. The obtained set of facts is then combined with an ASP encoding that can be grounded and solved by off-the-shelf ASP systems. We establish the competitiveness of our approach by empirically contrasting aspartame and sugar.
answer:
Aspartame: Solving Constraint Satisfaction Problems with Answer Set Programming


question:
Several authors have recently developed risk-sensitive policy gradient methods<lb>that augment the standard expected cost minimization problem with a measure of<lb>variability in cost. These studies have focused on specific risk-measures, such as<lb>the variance or conditional value at risk (CVaR). In this work, we extend the pol-<lb>icy gradient method to the whole class of coherent risk measures, which is widely<lb>accepted in finance and operations research, among other fields. We consider<lb>both static and time-consistent dynamic risk measures. For static risk measures,<lb>our approach is in the spirit of policy gradient algorithms and combines a standard<lb>sampling approach with convex programming. For dynamic risk measures, our ap-<lb>proach is actor-critic style and involves explicit approximation of value function.<lb>Most importantly, our contribution presents a unified approach to risk-sensitive<lb>reinforcement learning that generalizes and extends previous results.
answer:

Policy Gradient for Coherent Risk Measures