Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization
Authors: Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We describe an experiment demonstrating the utility of Insta Hide for Gaussian images and comparing to the utility of another data augmentation scheme, Mix Up Zhang et al. (2018). We also informally report on our implementation of LEARNPUBLIC and its empirical efficacy. |
| Researcher Affiliation | Academia | Sitan Chen MIT EMAIL. Xiaoxiao Li Princeton Universtiy EMAIL Zhao Song Columbia University, Princeton University / IAS EMAIL. Danyang Zhuo Duke University EMAIL. |
| Pseudocode | Yes | Algorithm 1: LEARNPUBLIC({([pj]S, yj)}j [d]), Algorithm 2: GRAMEXTRACT({y X,wi}i [m], η), Algorithm 3: FINDFLORALSUBMATRIX(M, k, r), Algorithm 4: LEARNPRIVATEIMAGE({y X,wi}i [m]) |
| Open Source Code | No | The paper mentions 'Our implementation of LEARNPUBLIC and its empirical efficacy' but does not provide any link or explicit statement about the code being open-source. |
| Open Datasets | No | We generated random images x1, . . . , x1000 R10 from N(0, Id)... |
| Dataset Splits | Yes | In all of our experiments, we separate our original image data (before generating synthetic data) into two categories: 80% training data and 20% test data. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models or memory for the experimental setup. |
| Software Dependencies | No | The paper mentions software components like 'Res Net-18', 'NASNet', 'SGD optimizer', 'Mix Up', but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | We use a 4-layer neural network as a binary classifier, y = arg max(softmax(W4σ(W3σ(W2(σ(W1x + b1)) + b2) + b3) + b4)), where x R10, W1 R100 10, W2 R100 100, W3 R100 100, W4 R2 100, b1 R100, b2 R100, b3 R100, b4 R100. We initialize the entries of each Wl and bl to be i.i.d. draws from N(ul, 1), where ul is sampled from N(0, α) at the outset. We train the neural network for 100 epochs with cross-entropy loss and SGD optimizer with a learning rate of 0.01. |