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03/28/2023
Variational Distribution Learning for Unsupervised Text-to-Image Generation
We propose a text-to-image generation algorithm based on deep neural net...
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11/23/2022
Generalizable Implicit Neural Representations via Instance Pattern Composers
Despite recent advances in implicit neural representations (INRs), it re...
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06/09/2022
Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer
Although autoregressive models have achieved promising results on image ...
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03/03/2022
Autoregressive Image Generation using Residual Quantization
For autoregressive (AR) modeling of high-resolution images, vector quant...
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11/29/2021
Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity
DETR is the first end-to-end object detector using a transformer encoder...
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07/13/2021
Automated Learning Rate Scheduler for Large-batch Training
Large-batch training has been essential in leveraging large-scale datase...
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06/11/2021
Hybrid Generative-Contrastive Representation Learning
Unsupervised representation learning has recently received lots of inter...
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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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05/23/2019
Bayesian Optimization over Sets
We propose a Bayesian optimization method over sets, to minimize a black...
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04/11/2019
MxML: Mixture of Meta-Learners for Few-Shot Classification
A meta-model is trained on a distribution of similar tasks such that it ...
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02/25/2019
ORACLE: Order Robust Adaptive Continual LEarning
The order of the tasks a continual learning model encounters may have la...
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06/05/2018
Mixed Effect Composite RNN-GP: A Personalized and Reliable Prediction Model for Healthcare
We present a personalized and reliable prediction model for healthcare, ...
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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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05/25/2018
Transductive Propagation Network for Few-shot Learning
Few-shot learning aims to build a learner that quickly generalizes to no...
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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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10/17/2017
Learning to Warm-Start Bayesian Hyperparameter Optimization
Hyperparameter optimization undergoes extensive evaluations of validatio...
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06/03/2015
