3 citations · 3 across the 6 of their papers we have counts for
7 papers
Kandinsky 6.0 Video: Foundation Models for Synchronized Video and Audio Generation
Team Kandinsky, Julia Agafonova, Bulat Akhmatov +85
We present Kandinsky 6.0 Video, a family of foundation diffusion models for synchronized text-to-audio-video generation, comprising Kandinsky 6.0 Video Lite (3B parameters) and Kan…
Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation
Vladimir Arkhipkin, Vladimir Korviakov, Nikolai Gerasimenko +22
This report introduces Kandinsky 5.0, a family of state-of-the-art foundation models for high-resolution image and 10-second video synthesis. The framework comprises three core lin…
NABLA: Neighborhood Adaptive Block-Level Attention
Dmitrii Mikhailov, Aleksey Letunovskiy, Maria Kovaleva +6
Recent progress in transformer-based architectures has demonstrated remarkable success in video generation tasks. However, the quadratic complexity of full attention mechanisms rem…
NeoNeXt: Novel neural network operator and architecture based on the patch-wise matrix multiplications
Vladimir Korviakov, Denis Koposov
Most of the computer vision architectures nowadays are built upon the well-known foundation operations: fully-connected layers, convolutions and multi-head self-attention blocks. I…
Neural Tangent Kernel: A Survey
Eugene Golikov, Eduard Pokonechnyy, Vladimir Korviakov
A seminal work [Jacot et al., 2018] demonstrated that training a neural network under specific parameterization is equivalent to performing a particular kernel method as width goes…
ISyNet: Convolutional Neural Networks design for AI accelerator
Alexey Letunovskiy, Vladimir Korviakov, Vladimir Polovnikov +3
In recent years Deep Learning reached significant results in many practical problems, such as computer vision, natural language processing, speech recognition and many others. For…
Tensor Yard: One-Shot Algorithm of Hardware-Friendly Tensor-Train Decomposition for Convolutional Neural Networks
Anuar Taskynov, Vladimir Korviakov, Ivan Mazurenko +1
Nowadays Deep Learning became widely used in many economic, technical and scientific areas of human interest. It is clear that efficiency of solutions based on Deep Neural Networks…