Search Results for author:
Found 10 papers, 0 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.
One-Shot Domain Incremental Learning
In DIL, we assume that samples on new domains are observed over time.
Deep generative model super-resolves spatially correlated multiregional climate data
Additionally, we present the outcomes of another variant of the deep generative model-based downscaling approach in which the low-resolution precipitation field is substituted with the pressure field, referred to as $\psi$SRGAN (Precipitation Source Inaccessible SRGAN).
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.
Learning low-dimensional manifolds under the L0-norm constraint for unsupervised outlier detection
To address this issue, we propose a novel reconstruction-based method: “L0-norm constrained autoencoders (L0-AE).” L0-AE uses autoencoders to learn low-dimensional manifolds that capture the nonlinear features of the data and uses a novel optimization algorithm that can decompose the data under the L0-norm constraints on the error matrix.
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.
