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Found 9 papers, 5 papers with code
Challenging Common Paradigms in Multi-Task Learning
Second, the notion of gradient conflicts has often been phrased as a specific problem in MTL.
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles
These innovations lead to more interpretable models of neural population activity that train well and perform better even on mixtures of complex latent manifolds.
Score-Based Generative Classifiers
In this work, we investigate score-based generative models as classifiers for natural images.
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world.
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
We construct an unsupervised learning model that achieves nonlinear disentanglement of underlying factors of variation in naturalistic videos.
Ranked #1 on
Disentanglement
on Natural Sprites
A simple way to make neural networks robust against diverse image corruptions
The human visual system is remarkably robust against a wide range of naturally occurring variations and corruptions like rain or snow.
Towards the first adversarially robust neural network model on MNIST
Despite much effort, deep neural networks remain highly susceptible to tiny input perturbations and even for MNIST, one of the most common toy datasets in computer vision, no neural network model exists for which adversarial perturbations are large and make semantic sense to humans.
Learned Watershed: End-to-End Learning of Seeded Segmentation
Learned boundary maps are known to outperform hand- crafted ones as a basis for the watershed algorithm.
Comparative Study of Deep Learning Software Frameworks
The study is performed on several types of deep learning architectures and we evaluate the performance of the above frameworks when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia Titan X) settings.
