Search Results for author:
Found 30 papers, 16 papers with code
Neural Re-rankers for Evidence Retrieval in the FEVEROUS Task
Computational fact-checking has gained a lot of traction in the machine learning and natural language processing communities.
Unbiased Sliced Wasserstein Kernels for High-Quality Audio Captioning
However, the contrastive method ignores the temporal information when measuring similarity across acoustic and linguistic modalities, leading to inferior performance.
Lightspeed Geometric Dataset Distance via Sliced Optimal Transport
We introduce sliced optimal transport dataset distance (s-OTDD), a model-agnostic, embedding-agnostic approach for dataset comparison that requires no training, is robust to variations in the number of classes, and can handle disjoint label sets.
Summarizing Bayesian Nonparametric Mixture Posterior -- Sliced Optimal Transport Metrics for Gaussian Mixtures
The second, sliced mixture Wasserstein, leverages the linearity of Gaussian mixture measures for efficient projection.
Borrowing Strength in Distributionally Robust Optimization via Hierarchical Dirichlet Processes
This paper presents a novel optimization framework to address key challenges presented by modern machine learning applications: High dimensionality, distributional uncertainty, and data heterogeneity.
Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation
The Learning-to-match (LTM) framework proves to be an effective inverse optimal transport approach for learning the underlying ground metric between two sources of data, facilitating subsequent matching.
Towards Marginal Fairness Sliced Wasserstein Barycenter
The sliced Wasserstein barycenter (SWB) is a widely acknowledged method for efficiently generalizing the averaging operation within probability measure spaces.
Hierarchical Hybrid Sliced Wasserstein: A Scalable Metric for Heterogeneous Joint Distributions
By using HHRT, we extend the SW into Hierarchical Hybrid Sliced Wasserstein (H2SW) distance which is designed specifically for comparing heterogeneous joint distributions.
Integrating Efficient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learning
In the realm of computer vision and graphics, accurately establishing correspondences between geometric 3D shapes is pivotal for applications like object tracking, registration, texture transfer, and statistical shape analysis.
On Parameter Estimation in Deviated Gaussian Mixture of Experts
We consider the parameter estimation problem in the deviated Gaussian mixture of experts in which the data are generated from $(1 - \lambda^{\ast}) g_0(Y| X)+ \lambda^{\ast} \sum_{i = 1}^{k_{\ast}} p_{i}^{\ast} f(Y|(a_{i}^{\ast})^{\top}X+b_i^{\ast},\sigma_{i}^{\ast})$, where $X, Y$ are respectively a covariate vector and a response variable, $g_{0}(Y|X)$ is a known function, $\lambda^{\ast} \in [0, 1]$ is true but unknown mixing proportion, and $(p_{i}^{\ast}, a_{i}^{\ast}, b_{i}^{\ast}, \sigma_{i}^{\ast})$ for $1 \leq i \leq k^{\ast}$ are unknown parameters of the Gaussian mixture of experts.
Sliced Wasserstein with Random-Path Projecting Directions
From the RPD, we derive the random-path slicing distribution (RPSD) and two variants of sliced Wasserstein, i. e., the Random-Path Projection Sliced Wasserstein (RPSW) and the Importance Weighted Random-Path Projection Sliced Wasserstein (IWRPSW).
Quasi-Monte Carlo for 3D Sliced Wasserstein
Monte Carlo (MC) integration has been employed as the standard approximation method for the Sliced Wasserstein (SW) distance, whose analytical expression involves an intractable expectation.
Diffeomorphic Mesh Deformation via Efficient Optimal Transport for Cortical Surface Reconstruction
The metric is defined by sliced Wasserstein distance on meshes represented as probability measures that generalize the set-based approach.
Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts
Originally introduced as a neural network for ensemble learning, mixture of experts (MoE) has recently become a fundamental building block of highly successful modern deep neural networks for heterogeneous data analysis in several applications of machine learning and statistics.
Sliced Wasserstein Estimation with Control Variates
To bridge the literature on variance reduction and the literature on the SW distance, we propose computationally efficient control variates to reduce the variance of the empirical estimation of the SW distance.
Energy-Based Sliced Wasserstein Distance
The second approach is optimizing for the best distribution which belongs to a parametric family of distributions and can maximize the expected distance.
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction
Despite being efficient, Max-SW and its amortized version cannot guarantee metricity property due to the sub-optimality of the projected gradient ascent and the amortization gap.
Markovian Sliced Wasserstein Distances: Beyond Independent Projections
Sliced Wasserstein (SW) distance suffers from redundant projections due to independent uniform random projecting directions.
Fast Approximation of the Generalized Sliced-Wasserstein Distance
Generalized sliced Wasserstein distance is a variant of sliced Wasserstein distance that exploits the power of non-linear projection through a given defining function to better capture the complex structures of the probability distributions.
Hierarchical Sliced Wasserstein Distance
We explain the usage of these projections by introducing Hierarchical Radon Transform (HRT) which is constructed by applying Radon Transform variants recursively.
Transformer with Fourier Integral Attentions
Multi-head attention empowers the recent success of transformers, the state-of-the-art models that have achieved remarkable success in sequence modeling and beyond.
Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution
Finally, we demonstrate the favorable performance of CSW over the conventional sliced Wasserstein in comparing probability measures over images and in training deep generative modeling on images.
Amortized Projection Optimization for Sliced Wasserstein Generative Models
Seeking informative projecting directions has been an important task in utilizing sliced Wasserstein distance in applications.
On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks
To address this issue, we propose a novel model fusion framework, named CLAFusion, to fuse neural networks with a different number of layers, which we refer to as heterogeneous neural networks, via cross-layer alignment.
Improving Mini-batch Optimal Transport via Partial Transportation
Mini-batch optimal transport (m-OT) has been widely used recently to deal with the memory issue of OT in large-scale applications.
Structured Dropout Variational Inference for Bayesian Neural Networks
Approximate inference in Bayesian deep networks exhibits a dilemma of how to yield high fidelity posterior approximations while maintaining computational efficiency and scalability.
On Transportation of Mini-batches: A Hierarchical Approach
To address these problems, we propose a novel mini-batch scheme for optimal transport, named Batch of Mini-batches Optimal Transport (BoMb-OT), that finds the optimal coupling between mini-batches and it can be seen as an approximation to a well-defined distance on the space of probability measures.
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein
To improve the discrepancy and consequently the relational regularization, we propose a new relational discrepancy, named spherical sliced fused Gromov Wasserstein (SSFG), that can find an important area of projections characterized by a von Mises-Fisher distribution.
Distributional Sliced-Wasserstein and Applications to Generative Modeling
Sliced-Wasserstein distance (SW) and its variant, Max Sliced-Wasserstein distance (Max-SW), have been used widely in the recent years due to their fast computation and scalability even when the probability measures lie in a very high dimensional space.
EmbNum: Semantic labeling for numerical values with deep metric learning
Semantic labeling for numerical values is a task of assigning semantic labels to unknown numerical attributes.
