In this paper, we propose a new definition of data dependent tensor rank named tensor Q-rank by a learnable orthogonal matrix \\(\\mathbf {Q}\\), and further introduce a unified data dependent low rank tensor recovery model. According to the low rank hypothesis, we introduce two ...
However, these models usually require smooth change of data along the third dimension to ensure their low rank structures. In this paper, we propose a new definition of data dependent tensor rank named tensor Q-rank by a learnable orthogonal matrix Q, and further introduce a unified data ...
We introduce an explainable selection method of Q \mathbf{Q} , under which the data tensor may have a more significant low tensor Q-rank structure than that of low tubal-rank structure. We also provide a corresponding envelope of our rank function and apply it to the low rank tensor ...
Tensor对象由原始数据组成的多维的数组,Tensor的rank(秩)其实是表示数组的维数,如下所示的tensor例子: Rank 数学实例 Python 例子 0 标量(点) 666 1 向量(直线) [6,6] 2 矩阵(平面) [[6,6,6],[6,6,6]] 3 立体(图片) [[[6,6],[6,6]] , [[6,6],[6,6]]] n n阶 (立体+时间轴,可参...
tensorflow中不同类型的数据可以用不同维度(rank)的张量来表示。 标量为0维张量,向量为1维张量,矩阵为2维张量。 彩色图像有rgb三个通道,可以表示为3维张量。 视频还有时间维,可以表示为4维张量。 可以简单地总结为:有几层中括号,就是多少维的张量。 pytorch AI检测代码解析 scalar = torch.tensor(True) print...
Keywords strain rate tensor, vorticity tensor, Q-criterion, Hodge dual Gradient of a Vector Field | fvc::grad(u) The gradient of a velocity vector u returns a velocity gradient tensor (second rank tensor). ∇u≡=∂iuj ⎜∂u1/∂x1∂u1/∂x2∂u1/∂x3∂u2/∂x1∂...
Quantized Tensor Robust Principal Component Analysis (Q-TRPCA) aims to recover a low-rank tensor and a sparse tensor from noisy, quantized, and sparsely corrupted measurements. A nonconvex constrained maximum likelihood (ML) estim...
When the rank is constant, this is a quadratic number of observations even though the number of parameters in the model is linear. Here we show how to solve the (noisy) tensor completion problem with many fewer observations. Let [Math Processing Error]n1≤n2≤n3. We give an algorithm ...
local_rank=local_rank, torch_distributed_backend=backend, use_pynccl=False, use_custom_allreduce=False, ) _WORLD = init_world_group(ranks, local_rank, backend) else: assert _WORLD.world_size == torch.distributed.get_world_size(), ( "world group already initialized with a different world ...
3. 阶(Rank):张量的阶是指它的维度数,或称为秩。零阶张量是标量(没有方向,只有大小),一阶张量是向量,二阶张量可以视作矩阵,而更高阶的张量则是三维、四维乃至更高维度的数组。 4. 物理和工程应用:在物理中,张量用来描述如应力、应变、电磁场等各种场的性质,它们都是与坐标系选择无关的物理量。例如,应力...