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Found 8 papers, 8 papers with code
The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric
We show how perceptual embeddings of the visual system can be constructed at inference-time with no training data or deep neural network features.
Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs
We present a one-shot method for compressing large labeled graphs called Random Edge Coding.
Action Matching: Learning Stochastic Dynamics from Samples
Learning the continuous dynamics of a system from snapshots of its temporal marginals is a problem which appears throughout natural sciences and machine learning, including in quantum systems, single-cell biological data, and generative modeling.
Compressing Multisets with Large Alphabets using Bits-Back Coding
Current methods which compress multisets at an optimal rate have computational complexity that scales linearly with alphabet size, making them too slow to be practical in many real-world settings.
Regularized Classification-Aware Quantization
Our method performs well on unseen data, and is faster than previous methods proportional to a quadratic term of the dataset size.
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Naively applied, our schemes would require more initial bits than the standard bits-back coder, but we show how to drastically reduce this additional cost with couplings in the latent space.
Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes
ICD coding from electronic clinical records is a manual, time-consuming and expensive process.
Ward2ICU: A Vital Signs Dataset of Inpatients from the General Ward
We present a proxy dataset of vital signs with class labels indicating patient transitions from the ward to intensive care units called Ward2ICU.
