Accuracy-Preserving CNN Pruning Method under Limited Data Availability

In October, 20 companies joined The Crunchbase Unicorn Board, contributing a total of $44.5 billion in value. This marks the highest valuation added for a new cohort in the past three years, with artificial intelligence (AI) leading the growth.

AI is revolutionizing various industries, but effective infrastructure is crucial for optimal performance. This ebook outlines strategies to enhance AI workloads, including optimizing infrastructure for applications like chatbots, utilizing dynamic batching and KV caching to reduce costs, and leveraging technologies like NVIDIA GPUs and Kubernetes for scalability.

Artificial intelligence (AI) is expected to become a significant topic in the upcoming 2026 midterms and the 2028 election, following its limited presence in the 2024 election. Concerns are rising regarding the financial investments in AI, potential federal bailouts, energy consumption, labor displacement, and the reliability of AI outputs. Politicians are increasingly voicing opposition to AI, indicating a growing political landscape surrounding this technology.

The CEO of Algebris Investments has advised investors to decrease their investments in leading technology firms, citing concerns about a potential significant correction in the AI sector. This bearish outlook reflects a growing skepticism regarding the sustainability of AI valuations amidst increasing market volatility.

The article introduces Convolutional Nearest Neighbors (ConvNN), a framework that unifies Convolutional Neural Networks (CNNs) and Transformers by viewing convolution and self-attention as neighbor selection and aggregation methods. ConvNN allows for a systematic exploration of the spectrum between these two architectures, serving as a drop-in replacement for convolutional and attention layers. The framework's effectiveness is validated through classification tasks on CIFAR-10 and CIFAR-100 datasets.

Bladder cancer is a prevalent malignancy with a high recurrence rate of up to 78%, necessitating precise post-operative monitoring. Multi-sequence contrast-enhanced MRI is commonly utilized for recurrence detection, but interpreting these scans is challenging due to post-surgical changes. This study introduces a curated multi-sequence, multi-modal MRI dataset designed for bladder cancer recurrence prediction and proposes H-CNN-ViT, a new model aimed at enhancing prediction accuracy in this critical area.

This article provides an in-depth exploration of object detection and semantic segmentation, merging theoretical foundations with practical applications. It reviews advancements in machine learning and deep learning, particularly focusing on convolutional neural networks (CNNs), YOLO architectures, and transformer-based approaches like DETR. The study also examines the integration of AI techniques and large language models to enhance object detection in complex environments, along with a comprehensive analysis of big data processing and model optimization.

The article discusses SparseST, a new approach in spatiotemporal data mining (STDM) that addresses the challenges of computational expense associated with Convolutional Long Short-Term Memory (ConvLSTM) models. These models, while effective in various applications such as transportation and healthcare, are often too resource-intensive for edge devices. The need for efficient AI solutions is emphasized, particularly in the context of edge computing, to maintain performance while reducing computational costs.