What is the current state of research on generative AI?4 answersThe current state of research on generative artificial intelligence (AI) is rapidly evolving and holds significant potential across various domains. Generative AI is forecasted to grow substantially, with the market expected to reach $6.5 billion by 2026. In the context of intelligent vehicles, generative AI technologies are poised to transform user interactions, offering immersive and personalized in-car experiences. Furthermore, the application of generative AI extends to fields like wound management, where it aids in understanding complexity, summarizing data, and automating research processes. Additionally, Generative AI algorithms are progressing swiftly in generating creative output, presenting opportunities for AI-based support in design disciplines. Overall, research on generative AI is diverse, spanning industries like healthcare, automotive, wound management, and design, showcasing its broad applicability and potential for innovation.
What is adaptive chess board?5 answersAn adaptive chess board is a modified version of a traditional chess board that allows for gameplay with two, three, or four players simultaneously. The board is divided into four colored sections, each with its own set of chess pieces. The sections are marked with alternating black and white squares along the borders and colored and white squares in the interior. The objective of the game is to checkmate the opponent's king. The game incorporates strategy blinders and gold stars at the corners of each section. The pawns have modified movement rules, allowing them to move one square at a time in any direction and capture diagonally. This adaptation of the chess board provides a unique and dynamic gameplay experience for multiple players.
What are the latest techniques for quantizing ViT models?5 answersQuantization techniques for Vision Transformer (ViT) models have been explored to improve efficiency during training and inference. One approach is knowledge-distillation-based variation-aware quantization, which uses multi-crop knowledge distillation to accelerate and stabilize training and alleviate the influence of parameter variations during quantization-aware training. Another technique is mixed-format sub-8bit quantization, which optimizes the numerical format of each matrix multiplication in ViTs to reduce post-training quantization (PTQ) error and achieve state-of-the-art accuracy. Binarization methods have also been proposed, such as BinaryViT, which uses gradient regularization to reduce weight oscillation and an activation shift module to reduce information distortion caused by binarization, achieving usable accuracy levels for fully binarized ViTs. Additionally, a two-scaled post-training quantization scheme for ViTs, called TSPTQ-ViT, leverages bit sparsity and outlier-aware scaling factors to reduce quantization loss and improve classification accuracy.
Which neural network architectures are less affected by quantization?5 answersNeural network architectures that are less affected by quantization include ResNet50, Mobilenet-v1, MobileNet-v2, and MNAS. These architectures have been shown to achieve superior quantization accuracy with smaller FLOPs and parameter sizes. Additionally, the use of trained scale factors for discretization thresholds and mutual rescaling of depth-wise separable convolution and convolution layers can optimize the training with quantization procedure, maintaining high accuracy even with a reduced training dataset size. Another method involves power exponent quantization-based compression, which reduces the influence of quantization on target detection accuracy while preserving the value range of parameters. Variational Network Quantization is another approach that prepares neural networks for pruning and few-bit quantization, resulting in small to negligible loss of task accuracy.
Is the convnext architecture heavily affected by quantization?5 answersThe convnext architecture is affected by quantization. Different studies have explored the impact of quantization on neural network architectures. Wu et al. propose a differentiable neural architecture search framework that quantizes different layers with different bit-widths, achieving state-of-the-art compression while outperforming baseline models. Yun and Wong introduce a progressive depth factorization strategy for efficient CNN architecture exploration under quantization constraints, enabling insights on efficiency-accuracy tradeoffs. Djahanshahi et al. demonstrate that weight quantization has a reduced effect on a special hardware architecture with distributed neurons. Wu et al. propose a low-precision floating-point quantization oriented processor, Phoenix, to address accuracy loss and hardware inefficiency caused by quantization. Peter et al. use neural architecture search to optimize CNNs for keyword spotting in limited resource environments, achieving high accuracy with reduced memory consumption through weight quantization.
What is the background of adaptive architecture?5 answersAdaptive architecture refers to buildings that are designed to adapt to their inhabitants and environments. This concept has a long history, with examples of adaptive buildings emerging during the modernist period, such as Rietveld’s Schroder house, Gaudi’s Casa Batllo, and Chareau's Maison de Verre. Early work in adaptive architecture involved manual adaptivity, sometimes with motor assistance. However, modern buildings have combined manual adaptivity with varying degrees of automation, resulting in buildings that respond to and influence the behavior of people, as well as the internal and external climate. The field of adaptive architecture aims to explore more meaningful and direct interactions between occupants and their environments, moving beyond traditional building management/automation systems. This shift towards adaptive architecture requires a dynamic approach to building performance and the interactions between occupants, buildings, and engineering appliances.