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Found 21 papers, 13 papers with code
Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss
In this multilingual approach, a typical setup is to use pairs of (image and English-text) and translation pairs.
Simulation-Free Training of Neural ODEs on Paired Data
In this work, we investigate a method for simulation-free training of Neural Ordinary Differential Equations (NODEs) for learning deterministic mappings between paired data.
Variational Distribution Learning for Unsupervised Text-to-Image Generation
We propose a text-to-image generation algorithm based on deep neural networks when text captions for images are unavailable during training.
Generalizable Implicit Neural Representations via Instance Pattern Composers
Despite recent advances in implicit neural representations (INRs), it remains challenging for a coordinate-based multi-layer perceptron (MLP) of INRs to learn a common representation across data instances and generalize it for unseen instances.
Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer
After code stacks in the sequence are randomly masked, Contextual RQ-Transformer is trained to infill the masked code stacks based on the unmasked contexts of the image.
Ranked #1 on
Text-to-Image Generation
on Conceptual Captions
Autoregressive Image Generation using Residual Quantization
However, we postulate that previous VQ cannot shorten the code sequence and generate high-fidelity images together in terms of the rate-distortion trade-off.
Ranked #2 on
Text-to-Image Generation
on Conceptual Captions
Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity
Deformable DETR uses the multiscale feature to ameliorate performance, however, the number of encoder tokens increases by 20x compared to DETR, and the computation cost of the encoder attention remains a bottleneck.
Automated Learning Rate Scheduler for Large-batch Training
Large-batch training has been essential in leveraging large-scale datasets and models in deep learning.
Hybrid Generative-Contrastive Representation Learning
On the other hand, the generative pre-training directly estimates the data distribution, so the representations tend to be robust but not optimal for discriminative tasks.
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.
Bayesian Optimization with Approximate Set Kernels
We propose a practical Bayesian optimization method over sets, to minimize a black-box function that takes a set as a single input.
MxML: Mixture of Meta-Learners for Few-Shot Classification
A meta-model is trained on a distribution of similar tasks such that it learns an algorithm that can quickly adapt to a novel task with only a handful of labeled examples.
Scalable and Order-robust Continual Learning with Additive Parameter Decomposition
First, a continual learning model should effectively handle catastrophic forgetting and be efficient to train even with a large number of tasks.
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.
Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare
We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention.
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.
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.
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 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.
Learning to Warm-Start Bayesian Hyperparameter Optimization
A simple alternative of manual search is random/grid search on a space of hyperparameters, which still undergoes extensive evaluations of validation errors in order to find its best configuration.
Bilinear Random Projections for Locality-Sensitive Binary Codes
In this paper we analyze a bilinear random projection method where feature matrices are transformed to binary codes by two smaller random projection matrices.
