C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra
Authors: Yuka Hashimoto, Zhao Wang, Tomoko Matsui
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our framework to practical problems such as density estimation and few-shot learning and show that our framework enables us to learn features of data even with a limited number of samples.Then, in Section 5, we discuss practical applications and show numerical results. |
| Researcher Affiliation | Collaboration | 1NTT Network Service Systems Laboratories, NTT Corporation, Tokyo, Japan 2Institute for Disaster Response Robotics, Waseda University, Tokyo, Japan 3Department of Statistical Modeling, the Institute of Statistical Mathematics, Tokyo, Japan. |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code of this paper is available at https://www.rd.ntt/e/ns/qos/ person/hashimoto/code_c_star_net.zip. |
| Open Datasets | Yes | We used the mini Image Net dataset (Vinyals et al., 2016), which is composed of 100 classes, each of which has 600 images, and considered the 5-way 1-shot task in the same manner as Subsection 4.2 in (Rusu et al., 2019). |
| Dataset Splits | No | No explicit mention of a validation set split or its details was found. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'masked autoregressive flow' and 'Adam' but does not specify their version numbers or other software dependencies with versions. |
| Experiment Setup | Yes | We set the learning rate so that it decays polynomially starting from 0.001 with decay rate 0.5.We set the hyperparameter λ in Eq. (4) as 0.3 and set the distribution D on Z as the uniform distribution on S9 i=1{z Z | z zi 0.05}. We set the learning rate so that it decays polynomially starting from 0.001 with decay rate 0.5.In this experiment, we set µ = 0.1.For a new task Tnew, we set the finite-dimensional subspace V of AN as N1 j=1 Span{vj,1, . . . , vj,l}N0. Here, l 10, vj,i(z) = e 10 zj,i pj(z) 2, zj,1 = Z(Tnew)64(j 1)+1:64j, and zj,i for i = 2, . . . , l are randomly drawn from the normal distribution with mean zj,1 and standard deviation 0.01. µ = 0.05 for Task 1 and µ = 0.1 for Task 2. |