**AdaRNN** is an adaptive [RNN](https://paperswithcode.com/methods/category/recurrent-neural-networks) that learns an adaptive model through two modules: [Temporal Distribution Characterization](https://paperswithcode.com/method/temporal-distribution-characterization) (TDC) and [Temporal Distribution Matching](https://paperswithcode.com/method/temporal-distribution-matching) (TDM) algorithms. Firstly, to better characterize the distribution information in time-series, TDC splits the training data into $K$ most diverse periods that have a large distribution gap inspired by the principle of maximum entropy. After that, a temporal distribution matching (TDM) algorithm is used to dynamically reduce distribution divergence using a [RNN](https://paperswithcode.com/methods/category/recurrent-neural-networks)-based model.

VL-T5 is a unified framework that learns different tasks in a single architecture with the same language modeling objective, i.e., multimodal conditional text generation. The model learns to generate labels in text based on the visual and textual inputs. In contrast to other existing methods, the framework unifies tasks as generating text labels conditioned on multimodal inputs. This allows the model to tackle vision-and-language tasks with unified text generation objective. The models use text prefixes to adapt to different tasks.

VL-T5

Unifying Vision-and-Language Tasks via Text Generation

AdaRNN

AdaRNN: Adaptive Learning and Forecasting of Time Series

To obtain excellent deep neural architectures, a series of techniques are carefully designed in EfficientNets. The giant formula for simultaneously enlarging the resolution, depth and width provides us a Rubik's cube for neural networks. So that we can find networks with high efficiency and excellent performance by twisting the three dimensions. This paper aims to explore the twisting rules for obtaining deep neural networks with minimum model sizes and computational costs. Different from the network enlarging, we observe that resolution and depth are more important than width for tiny networks. Therefore, the original method, i.e., the compound scaling in [EfficientNet](https://paperswithcode.com/method/efficientnet) is no longer suitable. To this end, we summarize a tiny formula for downsizing neural architectures through a series of smaller models derived from the EfficientNet-B0 with the FLOPs constraint. Experimental results on the ImageNet benchmark illustrate that our TinyNet performs much better than the smaller version of EfficientNets using the inversed giant formula. For instance, our TinyNet-E achieves a 59.9% Top-1 accuracy with only 24M FLOPs, which is about 1.9% higher than that of the previous best [MobileNetV3](https://paperswithcode.com/method/mobilenetv3) with similar computational cost.

Source	AdaRNN: Adaptive Learning and Forecasting of Time Series
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com