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Found 23 papers, 12 papers with code
Meta Variance Transfer: Learning to Augment from the Others
Humans have the ability to robustly recognize objects with various factors of variations such as nonrigid transformation, background noise, and change in lighting conditions.
Delta-AI: Local objectives for amortized inference in sparse graphical models
We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call $\Delta$-amortized inference ($\Delta$-AI).
Dataset Condensation with Latent Space Knowledge Factorization and Sharing
In this paper, we introduce a novel approach for systematically solving dataset condensation problem in an efficient manner by exploiting the regularity in a given dataset.
Meta Mirror Descent: Optimiser Learning for Fast Convergence
The resulting framework, termed Meta Mirror Descent (MetaMD), learns to accelerate optimisation speed.
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty
Numerous recent works utilize bi-Lipschitz regularization of neural network layers to preserve relative distances between data instances in the feature spaces of each layer.
Online Hyperparameter Meta-Learning with Hypergradient Distillation
Many gradient-based meta-learning methods assume a set of parameters that do not participate in inner-optimization, which can be considered as hyperparameters.
Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning
Thanks to the gradients aligned between tasks by our method, the model becomes less vulnerable to negative transfer and catastrophic forgetting.
Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty
Numerous recent works utilize bi-Lipschitz regularization of neural network layers to preserve relative distances between data instances in the feature spaces of each layer.
Large-Scale Meta-Learning with Continual Trajectory Shifting
Meta-learning of shared initialization parameters has shown to be highly effective in solving few-shot learning tasks.
Meta-Learned Confidence for Transductive Few-shot Learning
A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples, or confidence-weighted average of all the query samples.
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
As MetaPerturb is a set-function trained over diverse distributions across layers and tasks, it can generalize to heterogeneous tasks and architectures.
Meta Dropout: Learning to Perturb Latent Features for Generalization
Specifically, we meta-learn a noise generator which outputs a multiplicative noise distribution for latent features, to obtain low errors on the test instances in an input-dependent manner.
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Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs
By combining these two learning schemes, our model outperforms existing state-of-the-art speaker verification models learned with a standard supervised learning framework on short utterance (1-2 seconds) on the VoxCeleb datasets.
Meta-Learned Confidence for Few-shot Learning
To tackle this issue, we propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries such that they improve the model's transductive inference performance on unseen tasks.
Learning to Generalize to Unseen Tasks with Bilevel Optimization
To tackle this issue, we propose a simple yet effective meta-learning framework for metricbased approaches, which we refer to as learning to generalize (L2G), that explicitly constrains the learning on a sampled classification task to reduce the classification error on a randomly sampled unseen classification task with a bilevel optimization scheme.
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that the number of instances per task and class is fixed.
Meta Dropout: Learning to Perturb Features for Generalization
Specifically, we meta-learn a noise generator which outputs a multiplicative noise distribution for latent features, to obtain low errors on the test instances in an input-dependent manner.
ADAPTIVE NETWORK SPARSIFICATION VIA DEPENDENT VARIATIONAL BETA-BERNOULLI DROPOUT
With such input-independent dropout, each neuron is evolved to be generic across inputs, which makes it difficult to sparsify networks without accuracy loss.
Adaptive Network Sparsification with Dependent Variational Beta-Bernoulli Dropout
With such input-independent dropout, each neuron is evolved to be generic across inputs, which makes it difficult to sparsify networks without accuracy loss.
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Attention mechanism is effective in both focusing the deep learning models on relevant features and interpreting them.
DropMax: Adaptive Stochastic Softmax
Moreover, the learning of dropout probabilities for non-target classes on each instance allows the classifier to focus more on classification against the most confusing classes.
DropMax: Adaptive Variational Softmax
Moreover, the learning of dropout rates for non-target classes on each instance allows the classifier to focus more on classification against the most confusing classes.
Deep Asymmetric Multi-task Feature Learning
We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process.
