7,350 research outputs found
Few-Shot Image Recognition by Predicting Parameters from Activations
In this paper, we are interested in the few-shot learning problem. In
particular, we focus on a challenging scenario where the number of categories
is large and the number of examples per novel category is very limited, e.g. 1,
2, or 3. Motivated by the close relationship between the parameters and the
activations in a neural network associated with the same category, we propose a
novel method that can adapt a pre-trained neural network to novel categories by
directly predicting the parameters from the activations. Zero training is
required in adaptation to novel categories, and fast inference is realized by a
single forward pass. We evaluate our method by doing few-shot image recognition
on the ImageNet dataset, which achieves the state-of-the-art classification
accuracy on novel categories by a significant margin while keeping comparable
performance on the large-scale categories. We also test our method on the
MiniImageNet dataset and it strongly outperforms the previous state-of-the-art
methods
A Possibility of Search for New Physics at LHCb
It is interesting to search for new physics beyond the standard model at
LHCb. We suggest that weak decays of doubly charmed baryon such as
to charmless final states would be a possible
signal for new physics. In this work, we consider two models, i.e. the
unparticle and as examples to study such possibilities. We also discuss
the cases for which have not been observed yet, but
one can expect to find them when LHCb begins running. Our numerical results
show that these two models cannot result in sufficiently large decay widths,
therefore if such modes are observed at LHCb, there must be a new physics other
than the unparticle or models.Comment: 7 pages, 3 figures, 1 table. More references and discussion adde
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