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Found 38 papers, 11 papers with code
Bridging the Gap Between Variational Inference and Wasserstein Gradient Flows
Variational inference is a technique that approximates a target distribution by optimizing within the parameter space of variational families.
MoEmo Vision Transformer: Integrating Cross-Attention and Movement Vectors in 3D Pose Estimation for HRI Emotion Detection
In the current effort, we introduce 1) MoEmo (Motion to Emotion), a cross-attention vision transformer (ViT) for human emotion detection within robotics systems based on 3D human pose estimations across various contexts, and 2) a data set that offers full-body videos of human movement and corresponding emotion labels based on human gestures and environmental contexts.
Approximate Stein Classes for Truncated Density Estimation
Estimating truncated density models is difficult, as these models have intractable normalising constants and hard to satisfy boundary conditions.
Minimizing $f$-Divergences by Interpolating Velocity Fields
To perform such movements we need to calculate the corresponding velocity fields which include a density ratio function between these two distributions.
Label-Free Multi-Domain Machine Translation with Stage-wise Training
Our model is composed of three parts: a backbone model, a domain discriminator taking responsibility to discriminate data from different domains, and a set of experts that transfer the decoded features from generic to specific.
Density Ratio Estimation and Neyman Pearson Classification with Missing Data
We then adapt an important downstream application of DRE, Neyman-Pearson (NP) classification, to this MNAR setting.
MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows
The conventional understanding of adversarial training in generative adversarial networks (GANs) is that the discriminator is trained to estimate a divergence, and the generator learns to minimize this divergence.
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
We introduce Sequential Neural Posterior Score Estimation (SNPSE) and Sequential Neural Likelihood Score Estimation (SNLSE), two new score-based methods for Bayesian inference in simulator-based models.
Sliced Wasserstein Variational Inference
For example, it is not a proper metric, i. e., it is non-symmetric and does not preserve the triangle inequality.
Score Matching for Truncated Density Estimation on a Manifold
When observations are truncated, we are limited to an incomplete picture of our dataset.
Variational Likelihood-Free Gradient Descent
In many scientific applications, we do not have explicit access to the likelihood function.
Estimating the Arc Length of the Optimal ROC Curve and Lower Bounding the Maximal AUC
In this paper, we show the arc length of the optimal ROC curve is an $f$-divergence.
HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval
Video-Text Retrieval has been a hot research topic with the growth of multimedia data on the internet.
Continual Density Ratio Estimation in an Online Setting
To the best of our knowledge, there is no existing method that can evaluate generative models in continual learning without storing samples from the original distribution.
Spin-orbit driven ferromagnetism at half moiré filling in magic-angle twisted bilayer graphene
Strong electron correlation and spin-orbit coupling (SOC) provide two non-trivial threads to condensed matter physics.
Mesoscale and Nanoscale Physics Materials Science Strongly Correlated Electrons
Tunable Doping of Rhenium and Vanadium into Transition Metal Dichalcogenides for Two-Dimensional Electronics
Alternatively, using highly conductive doped TMDCs will have a profound impact on the contact engineering of 2D electronics.
Materials Science
Viscoelasticity enables self-organization of bacterial active matter in space and time
Active matter consists of units that generate mechanical work by consuming energy.
Soft Condensed Matter Biological Physics Fluid Dynamics
A General Class of Transfer Learning Regression without Implementation Cost
We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression.
Posterior Ratio Estimation of Latent Variables
Density Ratio Estimation has attracted attention from the machine learning community due to its ability to compare the underlying distributions of two datasets.
Model Reuse with Reduced Kernel Mean Embedding Specification
Given a publicly available pool of machine learning models constructed for various tasks, when a user plans to build a model for her own machine learning application, is it possible to build upon models in the pool such that the previous efforts on these existing models can be reused rather than starting from scratch?
Estimating Density Models with Truncation Boundaries using Score Matching
In this paper, we study parameter estimation for truncated probability densities using SM.
Continual Density Ratio Estimation (CDRE): A new method for evaluating generative models in continual learning
We propose a new method Continual Density Ratio Estimation (CDRE), which can estimate density ratios between a target distribution of real samples and a distribution of samples generated by a model while the model is changing over time and the data of the target distribution is not available after a certain time point.
Personalized Dialogue Generation with Diversified Traits
In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues.
Fisher Efficient Inference of Intractable Models
For example, the asymptotic variance of MLE solution attains equality of the asymptotic Cram{\'e}r-Rao lower bound (efficiency bound), which is the minimum possible variance for an unbiased estimator.
Trimmed Density Ratio Estimation
Density ratio estimation is a vital tool in both machine learning and statistical community.
Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation
We propose an inlier-based outlier detection method capable of both identifying the outliers and explaining why they are outliers, by identifying the outlier-specific features.
Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI).
Ranked #2 on
Hand Gesture Recognition
on ChaLearn val
Learning Sparse Structural Changes in High-dimensional Markov Networks: A Review on Methodologies and Theories
Recent years have seen an increasing popularity of learning the sparse \emph{changes} in Markov Networks.
Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks
This paper addresses the problem of continuous gesture recognition from sequences of depth maps using convolutional neutral networks (ConvNets).
Planogram Compliance Checking Based on Detection of Recurring Patterns
Product layout is extracted from an input image by means of unsupervised recurring pattern detection and matched via graph matching with the expected product layout specified by a planogram to measure the level of compliance.
Creating Simplified 3D Models with High Quality Textures
This paper presents an extension to the KinectFusion algorithm which allows creating simplified 3D models with high quality RGB textures.
Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective
Transfer learning assumes classifiers of similar tasks share certain parameter structures.
Modeling Relation Paths for Representation Learning of Knowledge Bases
Representation learning of knowledge bases (KBs) aims to embed both entities and relations into a low-dimensional space.
Structure Learning of Partitioned Markov Networks
We learn the structure of a Markov Network between two groups of random variables from joint observations.
Support Consistency of Direct Sparse-Change Learning in Markov Networks
We study the problem of learning sparse structure changes between two Markov networks $P$ and $Q$.
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
We propose a new method for detecting changes in Markov network structure between two sets of samples.
Density-Difference Estimation
A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference.
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
The objective of change-point detection is to discover abrupt property changes lying behind time-series data.
