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08/21/2022
Dataset Condensation with Latent Space Knowledge Factorization and Sharing
In this paper, we introduce a novel approach for systematically solving ...
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03/05/2022
Meta Mirror Descent: Optimiser Learning for Fast Convergence
Optimisers are an essential component for training machine learning mode...
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10/12/2021
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty
Numerous recent works utilize bi-Lipschitz regularization of neural netw...
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10/06/2021
Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning
Multilingual models jointly pretrained on multiple languages have achiev...
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10/06/2021
Online Hyperparameter Meta-Learning with Hypergradient Distillation
Many gradient-based meta-learning methods assume a set of parameters tha...
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02/14/2021
Large-Scale Meta-Learning with Continual Trajectory Shifting
Meta-learning of shared initialization parameters has shown to be highly...
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06/13/2020
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
Regularization and transfer learning are two popular techniques to enhan...
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04/06/2020
Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs
In realistic settings, a speaker recognition system needs to identify a ...
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02/27/2020
Transductive Few-shot Learning with Meta-Learned Confidence
We propose a novel transductive inference framework for metric-based met...
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08/05/2019
Learning to Generalize to Unseen Tasks with Bilevel Optimization
Recent metric-based meta-learning approaches, which learn a metric space...
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05/30/2019
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
While tasks could come with varying number of instances in realistic set...
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05/30/2019
Meta Dropout: Learning to Perturb Features for Generalization
A machine learning model that generalizes well should obtain low errors ...
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05/28/2018
Adaptive Network Sparsification via Dependent Variational Beta-Bernoulli Dropout
While variational dropout approaches have been shown to be effective for...
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05/24/2018
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Attention mechanism is effective in both focusing the deep learning mode...
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12/21/2017
DropMax: Adaptive Stochastic Softmax
We propose DropMax, a stochastic version of softmax classifier which at ...
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08/01/2017