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Found 11 papers, 1 papers with code
Enhancing Contextual Word Representations Using Embedding of Neighboring Entities in Knowledge Graphs
Pre-trained language models (PLMs) such as BERT and RoBERTa have dramatically improved the performance of various natural language processing tasks.
Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex
We propose an accuracy-preserving calibration method using the Concrete distribution as the probabilistic model on the probability simplex.
How many views does your deep neural network use for prediction?
The generalization ability of Deep Neural Networks (DNNs) is still not fully understood, despite numerous theoretical and empirical analyses.
StyleDiff: Attribute Comparison Between Unlabeled Datasets in Latent Disentangled Space
One major challenge in machine learning applications is coping with mismatches between the datasets used in the development and those obtained in real-world applications.
Partial Wasserstein Covering
We consider a general task called partial Wasserstein covering with the goal of providing information on what patterns are not being taken into account in a dataset (e. g., dataset used during development) compared with another dataset(e. g., dataset obtained from actual applications).
Variational Monocular Depth Estimation for Reliability Prediction
Self-supervised learning for monocular depth estimation is widely investigated as an alternative to supervised learning approach, that requires a lot of ground truths.
Neural Time Warping For Multiple Sequence Alignment
Multiple sequences alignment (MSA) is a traditional and challenging task for time-series analyses.
PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation
We propose a novel objective for penalizing geometric inconsistencies to improve the depth and pose estimation performance of monocular camera images.
Flow-based Image-to-Image Translation with Feature Disentanglement
Learning non-deterministic dynamics and intrinsic factors from images obtained through physical experiments is at the intersection of machine learning and material science.
Neural Edit Operations for Biological Sequences
The evolution of biological sequences, such as proteins or DNAs, is driven by the three basic edit operations: substitution, insertion, and deletion.
Causal Patterns: Extraction of multiple causal relationships by Mixture of Probabilistic Partial Canonical Correlation Analysis
In this paper, we propose a mixture of probabilistic partial canonical correlation analysis (MPPCCA) that extracts the Causal Patterns from two multivariate time series.
