531 research outputs found

    Few-Shot Image Recognition by Predicting Parameters from Activations

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    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

    Observation of Strong Coulomb Blockade in Resistively Isolated Tunnel Junctions

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    We report measurements of the Coulomb-blockade current in resistively isolated (R_{Isol} >> h/e^{2}) tunnel junctions for the temperature range 60mK WereportmeasurementsoftheCoulomb−blockadecurrentinresistivelyisolated(We report measurements of the Coulomb-blockade current in resistively isolated (R_{Isol}\gg h/e^{2})$ tunnel junctions for the temperature range 60mK < T < 230mK where the charging energy E_{c} is much greater than the thermal energy. A zero-bias resistance R_{0} of up to 10^{4}R_{T} (the tunnel resistance of the bare junction) is obtained. For eV << E_{c}, the I-V curves for a given R_{Isol} scale as a function of V/T, with I \propto V^{\alpha (R_{Isol})} over a range of V. The data agree well with numerical calculations of the tunneling rate that include environmental effects.Comment: 13 pages, 3 eps figure

    Demonstration of Geometric Landau-Zener Interferometry in a Superconducting Qubit

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    Geometric quantum manipulation and Landau-Zener interferometry have been separately explored in many quantum systems. In this Letter, we combine these two approaches to study the dynamics of a superconducting phase qubit. We experimentally demonstrate Landau-Zener interferometry based on the pure geometric phases in this solid-state qubit. We observe the interference caused by a pure geometric phase accumulated in the evolution between two consecutive Landau-Zener transitions, while the dynamical phase is canceled out by a spin-echo pulse. The full controllability of the qubit state as a function of the intrinsically robust geometric phase provides a promising approach for quantum state manipulation.Comment: 5 pages + 3 pages supplemental Materia
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