Understanding GRU in Neural Networks
Learn about Gated Recurrent Units (GRU) in neural networks, their role in sequential data processing, and applications in machine learning.
Learn about Gated Recurrent Units (GRU) in neural networks, their role in sequential data processing, and applications in machine learning.
GEAR: hybrid KV cache compression combining 4-bit quantization, low-rank residual approximation, and sparse corrections to cut peak memory and boost inference throughput
LSG-SLAM: a stereo visual SLAM using 3D Gaussian splatting for large-scale outdoor reconstruction, improving tracking stability and mapping quality.
LLM-driven system automates embodied intelligence skill generation for robotic arms, converting natural-language tasks into MuJoCo scenes, actions, and reward code.
MAX78000 AI microcontroller with low-power CNN accelerator, Arm Cortex?M4 with FPU, 442 KB weight SRAM and 512 KB flash, optimized for edge inference.
Survey of deep learning approaches for radar target detection, comparing two-stage and single-stage detectors (Faster R-CNN, YOLOv5), preprocessing, and deployment results.
Survey of deep metric learning: formulations, sample selection and metric loss functions (contrastive, triplet, N?pair), architectures and applications in vision, audio, and text.
Overview of convolutional neural networks: principles like padding, stride, pooling and filters, edge detection fundamentals, architecture patterns and a Keras MNIST implementation.
Survey of deep learning applications in AI: image recognition, NLP, speech, recommendation systems, autonomous driving, healthcare, cybersecurity, and VR.
Review of monocular ranging algorithms and imaging geometry for forward collision warning, covering camera pose, lane-width distance estimation and accuracy metrics.