We introduce two novel tree search algorithms that use a policy to guide search.

Despite their impressive performance, Deep Neural Networks (DNNs) typically underperform Gradient Boosting Trees (GBTs) on many tabular-dataset learning tasks. W e propose that applying a different regularization coefficient to each weight might boost the performance of DNNs by allowing them t o make more use of the more relevant inputs. However, this will lead to an int ractable number of hyperparameters. Here, we introduce Regularization Learning Networks (RLNs), which overcome this challenge by introducing an efficient hy perparameter tuning scheme which minimizes a new Counterfactual Loss . Our results show that RLNs significantly improve DNNs on tabular datasets, and achieve comparable results to GBTs, with the best performance achieved with an ensemble that combines GBTs and RLNs. RLNs produce extremely sparse networks, elim inating up to 99 .

Neural Information Processing Systems http://nips.cc/

Neural Machine Translation (NMT) has achieved remarkable progress with the quick evolvement of model structures. In this paper, we propose the concept of layer-wise coordination for NMT, which explicitly coordinates the learning of hidden representations of the encoder and decoder together layer by layer, gradually from low level to high level.

The new AI-DADA tool lets you create a unique Swatch design using AI prompts. You can't make a custom MoonSwatch yet--but it's not entirely off the table. Cast your mind back to 2017. In those heady days before ChatGPT and DALL-E, and Zoom calls, Swatch launched a fancy online platform that let you, the watch-buying public, design your own Swatch watch . It was called Swatch x You, and it let you tweak Swatch's standard New Gent 41-mm model by selecting one of the (surprisingly limited) preset designs, which you could then move, zoom, and rotate to fit over the watch and strap.

We further implement models that incorporate these explanations into their training process and output them at test time.

The first two authors contributed equally to this work.

Using the enhanced mixture model, we can extract generalizable insights for a target model through a global approximation.

Neural Information Processing Systems http://nips.cc/

Using attention transfer, we provide Pareto curves/tables for distillation of residual networks with four benchmark datasets, indicating the memory versus accuracy payoff.