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Found 11 papers, 4 papers with code
Model Stitching: Looking For Functional Similarity Between Representations
Model stitching (Lenc & Vedaldi 2015) is a compelling methodology to compare different neural network representations, because it allows us to measure to what degree they may be interchanged.
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
In this work, we present Multi-Symmetry Ensembles (MSE), a framework for constructing diverse ensembles by capturing the multiplicity of hypotheses along symmetry axes, which explore the hypothesis space beyond stochastic perturbations of model weights and hyperparameters.
On the Importance of Calibration in Semi-supervised Learning
State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been highly successful in leveraging a mix of labeled and unlabeled data by combining techniques of consistency regularization and pseudo-labeling.
AI-Assisted Discovery of Quantitative and Formal Models in Social Science
In social science, formal and quantitative models, such as ones describing economic growth and collective action, are used to formulate mechanistic explanations, provide predictions, and uncover questions about observed phenomena.
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Deep learning techniques have been increasingly applied to the natural sciences, e. g., for property prediction and optimization or material discovery.
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations
In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge.
Discovering Dynamical Parameters by Interpreting Echo State Networks
Reservoir computing architectures known as echo state networks (ESNs) have been shown to have exceptional predictive capabilities when trained on chaotic systems.
WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices
We present a logarithmic-scale efficient convolutional neural network architecture for edge devices, named WaveletNet.
Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks
First, we demonstrate that in predicting the transmission from multilayer photonic film, the relative error rate is reduced by 46. 8% (26. 5%) when the source data comes from 10-layer (8-layer) films and the target data comes from 8-layer (10-layer) films.
Rotational Unit of Memory
We evaluate our model on synthetic memorization, question answering and language modeling tasks.
Ranked #5 on
Question Answering
on bAbi
(Accuracy (trained on 1k) metric)
Nanophotonic Particle Simulation and Inverse Design Using Artificial Neural Networks
We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles.
Computational Physics Applied Physics Optics
