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Found 15 papers, 7 papers with code
Causal Scene BERT: Improving object detection by searching for challenging groups of data
Our main contribution is a pseudo-automatic method to discover such groups in foresight by performing causal interventions on simulated scenes.
Causal Scene BERT: Improving object detection by searching for challenging groups
We verify that the prioritized groups found via intervention are challenging for the object detector and show that retraining with data collected from these groups helps inordinately compared to adding more IID data.
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
Linear interpolation between initial neural network parameters and converged parameters after training with stochastic gradient descent (SGD) typically leads to a monotonic decrease in the training objective.
Exploring representation learning for flexible few-shot tasks
In this work, we consider a realistic setting where the relationship between examples can change from episode to episode depending on the task context, which is not given to the learner.
Few-Shot Attribute Learning
Compared to standard few-shot learning of semantic classes, in which novel classes may be defined by attributes that were relevant at training time, learning new attributes imposes a stiffer challenge.
Theoretical bounds on estimation error for meta-learning
Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly.
Regularized linear autoencoders recover the principal components, eventually
Our understanding of learning input-output relationships with neural nets has improved rapidly in recent years, but little is known about the convergence of the underlying representations, even in the simple case of linear autoencoders (LAEs).
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
Posterior collapse in Variational Autoencoders (VAEs) arises when the variational posterior distribution closely matches the prior for a subset of latent variables.
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
Our BCOP parameterization allows us to train large convolutional networks with provable Lipschitz bounds.
Lookahead Optimizer: k steps forward, 1 step back
The vast majority of successful deep neural networks are trained using variants of stochastic gradient descent (SGD) algorithms.
Understanding Posterior Collapse in Generative Latent Variable Models
Posterior collapse in Variational Autoencoders (VAEs) arises when the variational distribution closely matches the uninformative prior for a subset of latent variables.
Sorting out Lipschitz function approximation
We identify a necessary property for such an architecture: each of the layers must preserve the gradient norm during backpropagation.
Distilling the Posterior in Bayesian Neural Networks
We propose a framework, Adversarial Posterior Distillation, to distill the SGLD samples using a Generative Adversarial Network (GAN).
Adversarial Distillation of Bayesian Neural Network Posteriors
We propose a framework, Adversarial Posterior Distillation, to distill the SGLD samples using a Generative Adversarial Network (GAN).
Aggregated Momentum: Stability Through Passive Damping
Momentum is a simple and widely used trick which allows gradient-based optimizers to pick up speed along low curvature directions.
