This article provides a roundup of notable time-series forecasting papers published between 2023 and 2024. It highlights five influential papers, including a case study from the online fashion industry, a review on forecasting reconciliation, and new deep learning models like TSMixer and CARD. The article emphasizes advancements in forecasting models, handling challenges in retail forecasting, and improvements in hierarchical forecasting methods.
SenseCraft AI is a free, web-based platform designed for beginners, focusing on a no-code approach and application-orientation to simplify and accelerate the creation of AI applications.
Researchers from the University of California San Diego have developed a mathematical formula that explains how neural networks learn and detect relevant patterns in data, providing insight into the mechanisms behind neural network learning and enabling improvements in machine learning efficiency.
Discussion on the challenges and promises of deep learning for outlier detection in various data modalities, including image and tabular data, with a focus on self-supervised learning techniques.
A detailed explanation of the Transformer model, a key architecture in modern deep learning for tasks like neural machine translation, focusing on components like self-attention, encoder and decoder stacks, positional encoding, and training.
David Ferrucci, the founder and CEO of Elemental Cognition, is among those pioneering 'neurosymbolic AI' approaches as a way to overcome the limitations of today's deep learning-based generative AI technology.
BEAL is a deep active learning method that uses Bayesian deep learning with dropout to infer the model’s posterior predictive distribution and introduces an expected confidence-based acquisition function to select uncertain samples. Experiments show that BEAL outperforms other active learning methods, requiring fewer labeled samples for efficient training.
Pete Warden shares his experience and knowledge about the memory layout of the Raspberry Pi Pico board, specifically the RP2040 microcontroller. He encountered baffling bugs while updating TensorFlow Lite Micro and traced them to poor understanding of the memory layout. The article provides detailed insights into the physical and RAM layouts, stack behavior, and potential pitfalls.
A detailed overview of the architecture, Python implementation, and future of autoencoders, focusing on their use in feature extraction and dimension reduction in unsupervised learning.
Researchers have mapped the complete neural connectome of a fruit fly, detailing all 139,255 nerve cells and their connections. This advance offers insights into how the brain processes information.
