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Found 9 papers, 3 papers with code
The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.
Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve
Variational autoencoders (VAEs) are powerful tools for learning latent representations of data used in a wide range of applications.
Neuro-Symbolic Entropy Regularization
We propose a loss, neuro-symbolic entropy regularization, that encourages the model to confidently predict a valid object.
Probabilistic Sufficient Explanations
Understanding the behavior of learned classifiers is an important task, and various black-box explanations, logical reasoning approaches, and model-specific methods have been proposed.
Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction
We study the problem of entity-relation extraction in the presence of symbolic domain knowledge.
Generalization in data-driven models of primary visual cortex
With this new readout we train our network on neural responses from mouse primary visual cortex (V1) and obtain a gain in performance of 7% compared to the previous state-of-the-art network.
Stochastic Optimization of Sorting Networks via Continuous Relaxations
Sorting input objects is an important step in many machine learning pipelines.
Large-Scale Deep Learning on the YFCC100M Dataset
We train our three-layer deep neural network on the Yahoo!
Joint Analysis of Time-Evolving Binary Matrices and Associated Documents
An objective of such analysis is to infer structure and inter-relationships underlying the matrices, here defined by latent features associated with each axis of the matrix.
