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Found 12 papers, 3 papers with code
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data.
Dataset Distillation Meets Provable Subset Selection
Deep learning has grown tremendously over recent years, yielding state-of-the-art results in various fields.
A Unified Approach to Coreset Learning
Our approach offers a new definition of coreset, which is a natural relaxation of the standard definition and aims at approximating the \emph{average} loss of the original data over the queries.
Fuzzy Commitments Offer Insufficient Protection to Biometric Templates Produced by Deep Learning
Even in the "hardest" settings (in which we take a reconstructed image from one system and use it in a different system, with different feature extraction process) the reconstructed image offers 50 to 120 times higher success rates than the system's FAR.
Data-Independent Structured Pruning of Neural Networks via Coresets
Our method is based on the coreset framework and it approximates the output of a layer of neurons/filters by a coreset of neurons/filters in the previous layer and discards the rest.
LSHR-Net: a hardware-friendly solution for high-resolution computational imaging using a mixed-weights neural network
However, to date, there have been two major drawbacks: (1) the high-precision real-valued sensing patterns proposed in the majority of existing works can prove problematic when used with computational imaging hardware such as a digital micromirror sampling device and (2) the network structures for image reconstruction involve intensive computation, which is also not suitable for hardware deployment.
Data-Independent Neural Pruning via Coresets
We propose the first efficient, data-independent neural pruning algorithm with a provable trade-off between its compression rate and the approximation error for any future test sample.
Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning
Our model features three novel components: First is a feed-forward embedding that takes random class support samples (after a customary CNN embedding) and transfers them to a better class representation in terms of a classification problem.
Dynamic Spectrum Matching with One-shot Learning
The effectiveness of the classification using CNNs drops rapidly when only a small number of spectra per substance are available for training (which is a typical situation in real applications).
Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution
Machine learning methods have found many applications in Raman spectroscopy, especially for the identification of chemical species.
Latent Hinge-Minimax Risk Minimization for Inference from a Small Number of Training Samples
Deep Learning (DL) methods show very good performance when trained on large, balanced data sets.
HoneyFaces: Increasing the Security and Privacy of Authentication Using Synthetic Facial Images
We implemented the HoneyFaces system and tested it with a password file composed of 270 real users.
