Tensor completionConvex optimizationRecently, the \\({ Tensor}~{ Nuclear}~{ Norm}~{ (TNN)}\\) regularization based on t-SVD has been widely used in various low tubal-rank tensor recovery tasks. However, these models usually require smooth change of data along the third dimension to ensure ...
Low-n-rank tensor recovery based on multi-linear augmented Lagrange multiplier method The problem of recovering data in multi-way arrays (i.e., tensors) arises in many fields such as image processing and computer vision, etc. In this paper, ... H Tan,B Cheng,J Feng,... - 《Neurocomp...
hand a well accepted theory of gravity, different modifications in the Einstein-Hilbert action of the GR have been proposed [18]. Recently the Starobinski–Bel–Robinson (SBR) modified theory of gravity has been proposed by adding quadratic terms, that involve the Bel–Robinson tensor and the ...
def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False, rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix=''): # Make sure only the first process in DDP process the dataset first, and the fol...
Shape-safety is currently supported up to rank-2 tensors, i.e. matrices. Example The following example shows how to derive higher-order partials of a function z of type ℝ²→ℝ: val z = x * (-sin(x * y) + y) * 4 // Infix notation val `∂z∕∂x` = d(z) / d(...
Recently, the tensor train (TT) rank has received much attention for tensor completion, due to its ability to explore the global low-rankness of tensors. However, existing methods still leave room for improvement, since the low-rankness itself is generally not sufficient to recover the underlyi...
tensors. // Note: If the tensor is sparse, you must specify this value. repeated uint64 shape = 3 [packed = true]; } // A sparse or dense rank-R tensor that stores data as 32-bit ints (int32). message Int32Tensor{// Each value in the vector. If keys is empty, this is ...
The real-world tensors often contain missing values, which makes tensor completion become a prerequisite to utilize them. Previous studies have shown that imposing a low-rank constraint on tensor completion produces impressive performances. In this paper, we argue that low-rank constraint, albeit ...
under which the data tensor may have a more significant low tensor Q-rank structure than that of low tubal-rank structure. Specifically, maximizing the variance of singular value distribution leads to Variance Maximization Tensor Q-Nuclear norm (VMTQN), while minimizing the value of nuclear norm ...
Tensor: tab_slice = slice(0, self.tab_incoming_dim) text_slice = slice( self.tab_incoming_dim, self.tab_incoming_dim + self.text_incoming_dim ) image_slice = slice( self.tab_incoming_dim + self.text_incoming_dim, self.tab_incoming_dim + self.text_incoming_dim + self.image_incoming...