Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CALTeC: Content-Adaptive Linear Tensor Completion for Collaborative
  Intelligence   In collaborative intelligence, an artificial intelligence (AI) model is
typically split between an edge device and the cloud. Feature tensors produced
by the edge sub-model are sent to the cloud via an imperfect communication
channel. At the cloud side, parts of the feature tensor may be missing due to
packet loss. In this paper we propose a method called Content-Adaptive Linear
Tensor Completion (CALTeC) to recover the missing feature data. The proposed
method is fast, data-adaptive, does not require pre-training, and produces
better results than existing methods for tensor data recovery in collaborative
intelligence.

35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph