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:
The family of temporal difference (TD) methods span a spectrum from computationally frugal linear methods like TD(λ) to data efficient least squares methods. Least square methods make the best use of available data directly computing the TD solution and thus do not require tuning a typically highly sensitive learning rate parameter, but require quadratic computation and storage. Recent algorithmic developments have yielded several sub-quadratic methods that use an approximation to the least squares TD solution, but incur bias. In this paper, we propose a new family of accelerated gradient TD (ATD) methods that (1) provide similar data efficiency benefits to least-squares methods, at a fraction of the computation and storage (2) significantly reduce parameter sensitivity compared to linear TD methods, and (3) are asymptotically unbiased. We illustrate these claims with a proof of convergence in expectation and experiments on several benchmark domains and a large-scale industrial energy allocation domain.
answer:
Accelerated Gradient Temporal Difference Learning


question:
Recurrent neural networks scale poorly due to the intrinsic difficulty in parallelizing their state computations. For instance, the forward pass computation of ht is blocked until the entire computation of ht−1 finishes, which is a major bottleneck for parallel computing. In this work, we propose an alternative RNN implementation by deliberately simplifying the state computation and exposing more parallelism. The proposed recurrent unit operates as fast as a convolutional layer and 5-10x faster than cuDNN-optimized LSTM. We demonstrate the unit’s effectiveness across a wide range of applications including classification, question answering, language modeling, translation and speech recognition. We open source our implementation in PyTorch and CNTK1.
answer:
Training RNNs as Fast as CNNs


question:
We introduce the “exponential linear unit” (ELU) which speeds up learning in<lb>deep neural networks and leads to higher classification accuracies. Like rectified<lb>linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs (PRe-<lb>LUs), ELUs also avoid a vanishing gradient via the identity for positive values.<lb>However ELUs have improved learning characteristics compared to the units with<lb>other activation functions. In contrast to ReLUs, ELUs have negative values which<lb>allows them to push mean unit activations closer to zero. Zero means speed up<lb>learning because they bring the gradient closer to the unit natural gradient. We<lb>show that the unit natural gradient differs from the normal gradient by a bias shift<lb>term, which is proportional to the mean activation of incoming units. Like batch<lb>normalization, ELUs push the mean towards zero, but with a significantly smaller<lb>computational footprint. While other activation functions like LReLUs and PRe-<lb>LUs also have negative values, they do not ensure a noise-robust deactivation state.<lb>ELUs saturate to a negative value with smaller inputs and thereby decrease the<lb>propagated variation and information. Therefore ELUs code the degree of pres-<lb>ence of particular phenomena in the input, while they do not quantitatively model<lb>the degree of their absence. Consequently, dependencies between ELUs are much<lb>easier to model and distinct concepts are less likely to interfere.<lb>We found that ELUs lead not only to faster learning, but also to better general-<lb>ization performance once networks have many layers (≥ 5). ELU networks were<lb>among top 10 reported CIFAR-10 results and yielded the best published result on<lb>CIFAR-100, without resorting to multi-view evaluation or model averaging. On<lb>ImageNet, ELU networks considerably speed up learning compared to a ReLU<lb>network with the same architecture, obtaining less than 10% classification error<lb>for a single crop, single model network.
answer:

EXPONENTIAL LINEAR UNITS (ELUS)