In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[EX Q]: We extend the Consensus Network framework to Transductive Consensus Network (TCN), a semi-supervised multi-modal classification framework, and identify its two mechanisms: consensus and classification. By putting forward three variants as ablation studies, we show both mechanisms should be functioning together. Overall, TCNs outperform or align with the best benchmark algorithms when only 20 to 200 labeled data points are available.
[EX A]: A semi-supervised multi-modal classification framework, TCN, that outperforms various benchmarks.

[EX Q]: Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep learning ensembles have provided state-of-the-art results in uncertainty estimation. In this work, we focus on in-domain uncertainty for image classification. We explore the standards for its quantification and point out pitfalls of existing metrics. Avoiding these pitfalls, we perform a broad study of different ensembling techniques. To provide more insight in the broad comparison, we introduce the deep ensemble equivalent (DEE) and show that many sophisticated ensembling techniques are equivalent to an ensemble of very few independently trained networks in terms of the test log-likelihood.
[EX A]: We highlight the problems with common metrics of in-domain uncertainty and perform a broad study of modern ensembling techniques.

[EX Q]: We give a new algorithm for learning a two-layer neural network under a very general class of input distributions. Assuming there is a ground-truth two-layer network 
 y = A \sigma(Wx) + \xi,
 where A, W are weight matrices, \xi represents noise, and the number of neurons in the hidden layer is no larger than the input or output,  our algorithm is guaranteed to recover the parameters A, W of the ground-truth network. The only requirement on the input x is that it is symmetric, which still allows highly complicated and structured input. 

 Our algorithm is based on the method-of-moments framework and extends several results in tensor decompositions. We use spectral algorithms to avoid the complicated non-convex optimization in learning neural networks. Experiments show that our algorithm can robustly learn the ground-truth neural network with a small number of samples for many symmetric input distributions.
[EX A]:
We give an algorithm for learning a two-layer neural network with symmetric input distribution.