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 ...
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...
fallback to Python session." ) args.use_py_session = True runner_cls = ModelRunner if args.use_py_session else ModelRunnerCpp runner_kwargs = dict(engine_dir=args.engine_dir, rank=runtime_rank, debug_mode=args.debug
下标q为曲线的阶），有节向量V=\{v_0,v_1,...,v_k\}，其中k=n+q+1。将q_i代入p中有：...
ThenAcan be written asA=U0⊗V0+l+u∑k=1Uk⊗Vk,(3.1)whereUkandVkare of sizep×pandq×qrespectively, and additionallyrankVk= 1,16k6l+u.(3.2)Proof.Introduce a down-shift matrixZpof orderp, it readsZp=01 0...1 0, Tensor-Train ranks for matrices and their inverse...
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 scalar = torch.tensor(True) print(scalar) print...
Tensor-cell2cell has only one rank parameter, while scTensor has rank parameters for the number of tensor orders (i.e. 3). This difference can be an advantage or a disadvantage; a small number of ranks reduces the computational time required to estimate the optimal ranks but it might make...
qscheme, float, int], Tuple[torch.qscheme, Tensor, Tensor, int]] if self.qscheme() == torch.per_tensor_affine: quantizer_params = self.qscheme(), self.q_scale(), self.q_zero_point() elif self.qscheme() in (torch.per_channel_affine, torch.per_channel_affine_float_qparams): ...
In the present paper, we will find the description of all additive mappings T: Mn(K) → Mn(K) such that [T(y), y] = T(y)y - yT(y) = 0 for all y ∈ Y, where Y = {y ∈ Mn(K) | y = zepq+weps or y = zepq+wesq, where p, q, s ∈ {1, . . . , n}, ...
把上面的数字一般化，每个方向分成q份，变成m次矩阵乘法，复杂度变成O(nlogqm)。考虑更大的q。