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07/26/2023
Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?
Existing analyses of the expressive capacity of Transformer models have ...
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05/26/2023
Exploring Weight Balancing on Long-Tailed Recognition Problem
Recognition problems in long-tailed data, where the sample size per clas...
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06/02/2022
Excess risk analysis for epistemic uncertainty with application to variational inference
We analyze the epistemic uncertainty (EU) of supervised learning in Baye...
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05/15/2022
Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective
The lottery ticket hypothesis (LTH) has attracted attention because it c...
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04/29/2022
Goldilocks-curriculum Domain Randomization and Fractal Perlin Noise with Application to Sim2Real Pneumonia Lesion Detection
A computer-aided detection (CAD) system based on machine learning is exp...
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04/18/2022
Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey
Representation learning enables us to automatically extract generic feat...
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04/11/2022
Neural Lagrangian Schrödinger bridge
Population dynamics is the study of temporal and spatial variation in th...
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10/11/2021
A Closer Look at Prototype Classifier for Few-shot Image Classification
The prototypical network is a prototype classifier based on meta-learnin...
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08/31/2021
Disentanglement Analysis with Partial Information Decomposition
Given data generated from multiple factors of variation that cooperative...
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06/09/2021
Loss function based second-order Jensen inequality and its application to particle variational inference
Bayesian model averaging, obtained as the expectation of a likelihood fu...
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03/17/2021
Toward Neural-Network-Guided Program Synthesis and Verification
We propose a novel framework of program and invariant synthesis called n...
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02/24/2021
Abelian Neural Networks
We study the problem of modeling a binary operation that satisfies some ...
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02/13/2021
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
Instance discriminative self-supervised representation learning has been...
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02/01/2021
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
To cope with high annotation costs, training a classifier only from weak...
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11/23/2020
Stable Weight Decay Regularization
Weight decay is a popular regularization technique for training of deep ...
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11/12/2020
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting
Deep learning is often criticized by two serious issues which rarely exi...
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08/03/2020
Classification from Ambiguity Comparisons
Labeling data is an unavoidable pre-processing procedure for most machin...
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07/03/2020
Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain
Label disagreement between human experts is a common issue in the medica...
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06/29/2020
Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia
Adaptive Momentum Estimation (Adam), which combines Adaptive Learning Ra...
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06/15/2020
LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learning
The prototypical network (ProtoNet) is a few-shot learning framework tha...
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06/13/2020
γ-ABC: Outlier-Robust Approximate Bayesian Computation based on Robust Divergence Estimator
Making a reliable inference in complex models is an essential issue in s...
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06/11/2020
Similarity-based Classification: Connecting Similarity Learning to Binary Classification
In real-world classification problems, pairwise supervision (i.e., a pai...
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05/08/2020
Sequential Gallery for Interactive Visual Design Optimization
Visual design tasks often involve tuning many design parameters. For exa...
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03/10/2020
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time
The Gaussian process bandit is a problem in which we want to find a maxi...
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02/10/2020
Few-shot Domain Adaptation by Causal Mechanism Transfer
We study few-shot supervised domain adaptation (DA) for regression probl...
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02/10/2020
A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast
Stochastic optimization algorithms, such as Stochastic Gradient Descent ...
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11/20/2019
Bayesian interpretation of SGD as Ito process
The current interpretation of stochastic gradient descent (SGD) as a sto...
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07/24/2019
Classification from Triplet Comparison Data
Learning from triplet comparison data has been extensively studied in th...
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06/24/2019
Interactive Subspace Exploration on Generative Image Modelling
Generative image modeling techniques such as GAN demonstrate highly conv...
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05/28/2019
Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge
We propose an algorithm based on reinforcement learning for solving NP-h...
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05/02/2019
Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping
Deep neural networks (DNNs) have a high accuracy on image classification...
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04/26/2019
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization
Pairwise similarities and dissimilarities between data points might be e...
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03/22/2019
Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics
Imaging flow cytometry shows significant potential for increasing our un...
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03/14/2019
On Learning from Ghost Imaging without Imaging
Computational ghost imaging is an imaging technique with which an object...
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03/07/2019
On Transformations in Stochastic Gradient MCMC
Stochastic gradient Langevin dynamics (SGLD) is a widely used sampler fo...
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02/12/2019
PAC-Bayes Analysis of Sentence Representation
Learning sentence vectors from an unlabeled corpus has attracted attenti...
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02/04/2019
Online Multiclass Classification Based on Prediction Margin for Partial Feedback
We consider the problem of online multiclass classification with partial...
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02/01/2019
Multi-level Monte Carlo Variational Inference
In many statistics and machine learning frameworks, stochastic optimizat...
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01/31/2019
Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization
We consider the semi-supervised ordinal regression problem, where unlabe...
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01/15/2019
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PAC-Bayesian Analysis
The notion of flat minima has played a key role in the generalization pr...
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09/13/2018
Clipped Matrix Completion: a Remedy for Ceiling Effects
We consider the recovery of a low-rank matrix from its clipped observati...
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09/11/2018
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions
Data-agnostic quasi-imperceptible perturbations on inputs can severely d...
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09/11/2018
Unsupervised Domain Adaptation Based on Source-guided Discrepancy
Unsupervised domain adaptation is the problem setting where data generat...
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05/21/2018
Frank-Wolfe Stein Sampling
In Bayesian inference, the posterior distributions are difficult to obta...
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03/12/2018
Variational Inference for Gaussian Process with Panel Count Data
We present the first framework for Gaussian-process-modulated Poisson pr...
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02/13/2018
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
While crowdsourcing has become an important means to label data, crowdwo...
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02/12/2018
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
High sensitivity of neural networks against malicious perturbations on i...
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02/12/2018
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer
Learning using privileged information is an attractive problem setting t...
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10/18/2017
Variational Inference based on Robust Divergences
Robustness to outliers is a central issue in real-world machine learning...
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09/26/2017