Search Results for author:
Found 9 papers, 7 papers with code
Self-Supervised Dataset Distillation for Transfer Learning
To achieve this, we also introduce the MSE between representations of the inner model and the self-supervised target model on the original full dataset for outer optimization.
Learning Transferable Adversarial Robust Representations via Multi-view Consistency
Despite the success on few-shot learning problems, most meta-learned models only focus on achieving good performance on clean examples and thus easily break down when given adversarially perturbed samples.
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.
MOG: Molecular Out-of-distribution Generation with Energy-based Models
To validate the ability to explore the chemical space beyond the known molecular distribution, we experiment with MOG to generate molecules of high absolute values of docking score, which is the affinity score based on a physical binding simulation between a target protein and a given molecule.
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
In this work, we propose StyleSpeech, a new TTS model which not only synthesizes high-quality speech but also effectively adapts to new speakers.
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning
Then, the learned model can be used for downstream few-shot classification tasks, where we obtain task-specific parameters by performing semi-supervised EM on the latent representations of the support and query set, and predict labels of the query set by computing aggregated posteriors.
Contrastive Learning with Adversarial Perturbations for Conditional Text Generation
In this work, we propose to mitigate the conditional text generation problem by contrasting positive pairs with negative pairs, such that the model is exposed to various valid or incorrect perturbations of the inputs, for improved generalization.
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
For transductive link prediction, we further propose a stochastic embedding layer to model uncertainty in the link prediction between unseen entities.
Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs
We validate our Information Maximizing Hierarchical Conditional Variational AutoEncoder (Info-HCVAE) on several benchmark datasets by evaluating the performance of the QA model (BERT-base) using only the generated QA pairs (QA-based evaluation) or by using both the generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models.
Ranked #1 on
Question Generation
on Natural Questions
