ANDHRA module splits the neural network into multiple “branches” (representing different paths) that process the same input signal in parallel, each corresponding to different possible outcomes.

CNN-TS: A Deep Convolutional Neural Network for Time Series Classification

This method leverages deep convolutional neural networks to classify time series data with intermediate targets. It is designed to improve accuracy in time series analysis.

CNN-TS

A Deep Convolutional Neural Network for Time Series Classification with Intermediate Targets

ANDHRA Module

ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel Realities

We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices. At its core, we introduce the Universal Inverted Bottleneck (UIB) search block, a unified and flexible structure that merges Inverted Bottleneck (IB), ConvNext, Feed Forward Network (FFN), and a novel Extra Depthwise (ExtraDW) variant. Alongside UIB, we present Mobile MQA, an attention block tailored for mobile accelerators, delivering a significant 39% speedup. An optimized neural architecture search (NAS) recipe is also introduced which improves MNv4 search effectiveness. The integration of UIB, Mobile MQA and the refined NAS recipe results in a new suite of MNv4 models that are mostly Pareto optimal across mobile CPUs, DSPs, GPUs, as well as specialized accelerators like Apple Neural Engine and Google Pixel EdgeTPU - a characteristic not found in any other models tested. Finally, to further boost accuracy, we introduce a novel distillation technique. Enhanced by this technique, our MNv4-Hybrid-Large model delivers 87% ImageNet-1K accuracy, with a Pixel 8 EdgeTPU runtime of just 3.8ms.

Source	ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel Realities
Year
Data Source	Papers with Code archive (CC BY-SA)