Search Results for author:
Found 23 papers, 5 papers with code
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances.
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Our primary contribution is the {\em MT-net}, which enables the meta-learner to learn on each layer's activation space a subspace that the task-specific learner performs gradient descent on.
Neural Complexity Measures
While various complexity measures for deep neural networks exist, specifying an appropriate measure capable of predicting and explaining generalization in deep networks has proven challenging.
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure
We consider a non-projective class of inhomogeneous random graph models with interpretable parameters and a number of interesting asymptotic properties.
Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization
We propose a practical Bayesian optimization method using Gaussian process regression, of which the marginal likelihood is maximized where the number of model selection steps is guided by a pre-defined threshold.
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.
Bayesian inference on random simple graphs with power law degree distributions
The BFRY random variables are well approximated by gamma random variables in a variational Bayesian inference routine, which we apply to several network datasets for which power law degree distributions are a natural assumption.
Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework
We propose a Bayesian evidence framework to facilitate transfer learning from pre-trained deep convolutional neural networks (CNNs).
Gaussian Copula Variational Autoencoders for Mixed Data
To this end, we employ Gaussian copula to model the local dependency in mixed categorical and continuous data, leading to {\em Gaussian copula variational autoencoder} (GCVAE).
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models
Normalized random measures (NRMs) provide a broad class of discrete random measures that are often used as priors for Bayesian nonparametric models.
Bayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility
Bayesian hierarchical clustering (BHC) is an agglomerative clustering method, where a probabilistic model is defined and its marginal likelihoods are evaluated to decide which clusters to merge.
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.
Convex Optimization for Binary Classifier Aggregation in Multiclass Problems
Multiclass problems are often decomposed into multiple binary problems that are solved by individual binary classifiers whose results are integrated into a final answer.
Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models
Bayesian nonparametric methods based on the Dirichlet process (DP), gamma process and beta process, have proven effective in capturing aspects of various datasets arising in machine learning.
Clustering sequence sets for motif discovery
Most of existing methods for DNA motif discovery consider only a single set of sequences to find an over-represented motif.
On Local Optimizers of Acquisition Functions in Bayesian Optimization
In practice, however, local optimizers of an acquisition function are also used, since searching for the global optimizer is often a non-trivial or time-consuming task.
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.
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.
Discrete Infomax Codes for Supervised Representation Learning
In this paper we present a model that produces Discrete InfoMax Codes (DIMCO); we learn a probabilistic encoder that yields k-way d-dimensional codes associated with input data.
Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning
Since a positive bag contains both positive and negative instances, it is often required to detect positive instances (key instances) when a set of instances is categorized as a positive bag.
Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytopes
The main idea is to use a random mapping which embeds the combinatorial space into a convex polytope in a continuous space, on which all essential process is performed to determine a solution to the black-box optimization in the combinatorial space.
Discrete InfoMax Codes for Meta-Learning
This paper analyzes how generalization works in meta-learning.
On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization
Sequential model-based optimization sequentially selects a candidate point by constructing a surrogate model with the history of evaluations, to solve a black-box optimization problem.
