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Found 16 papers, 8 papers with code
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation
Large pretrained language models (LMs) like BERT have improved performance in many disparate natural language processing (NLP) tasks.
Variational Model Inversion Attacks
In this work, we provide a probabilistic interpretation of model inversion attacks, and formulate a variational objective that accounts for both diversity and accuracy.
Disentanglement and Generalization Under Correlation Shifts
However, often such correlations are not robust (e. g., they may change between domains, datasets, or applications) and we wish to avoid exploiting them.
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.
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks
For problems where global invertibility is necessary, such as applying normalizing flows on OOD data, we show the importance of designing stable INN building blocks.
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling
We estimate the Stein discrepancy between the data density $p(x)$ and the model density $q(x)$ defined by a vector function of the data.
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x|y).
Out-of-distribution Detection in Few-shot Classification
In this work, we propose tasks for out-of-distribution detection in the few-shot setting and establish benchmark datasets, based on four popular few-shot classification datasets.
On the Invertibility of Invertible Neural Networks
Guarantees in deep learning are hard to achieve due to the interplay of flexible modeling schemes and complex tasks.
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Centroid-based deep metric learning for speaker recognition
Speaker embedding models that utilize neural networks to map utterances to a space where distances reflect similarity between speakers have driven recent progress in the speaker recognition task.
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).
Neural Relational Inference for Interacting Systems
Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics.
Dualing GANs
We start from linear discriminators in which case conjugate duality provides a mechanism to reformulate the saddle point objective into a maximization problem, such that both the generator and the discriminator of this 'dualing GAN' act in concert.
