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Found 10 papers, 6 papers with code
Guarantee Regions for Local Explanations
Interpretability methods that utilise local surrogate models (e. g. LIME) are very good at describing the behaviour of the predictive model at a point of interest, but they are not guaranteed to extrapolate to the local region surrounding the point.
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations
In this work, we survey properties defined in interpretable machine learning papers, synthesize them based on what they actually measure, and describe the trade-offs between different formulations of these properties.
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
In this paper we introduce Uncertainty Baselines: high-quality implementations of standard and state-of-the-art deep learning methods on a variety of tasks.
Training independent subnetworks for robust prediction
Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over the original network.
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding
Variational Autoencoders (VAEs) have seen widespread use in learned image compression.
Refining the variational posterior through iterative optimization
Variational inference (VI) is a popular approach for approximate Bayesian inference that is particularly promising for highly parameterized models such as deep neural networks.
Compression without Quantization
Standard compression algorithms work by mapping an image to discrete code using an encoder from which the original image can be reconstructed through a decoder.
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements.
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
The current state-of-the-art inference method, Variational Inference (VI), employs a Gaussian approximation to the posterior distribution.
Deep Gaussian Processes with Decoupled Inducing Inputs
Deep Gaussian Processes (DGP) are hierarchical generalizations of Gaussian Processes (GP) that have proven to work effectively on a multiple supervised regression tasks.
