research
∙
02/09/2023
HybrIK-Transformer
HybrIK relies on a combination of analytical inverse kinematics and deep...
share
research
∙
08/16/2022
SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows
Inverse Kinematics (IK) systems are often rigid with respect to their in...
research
∙
01/30/2022
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
Recent progress in neural forecasting accelerated improvements in the pe...
research
∙
01/18/2022
Motion Inbetweening via Deep Δ-Interpolator
We show that the task of synthesizing missing middle frames, commonly kn...
research
∙
09/20/2021
Neural forecasting at scale
We study the problem of efficiently scaling ensemble-based deep neural n...
research
∙
06/03/2021
ProtoRes: Proto-Residual Architecture for Deep Modeling of Human Pose
Our work focuses on the development of a learnable neural representation...
research
∙
12/31/2020
Adaptive filters for the moving target indicator system
Adaptive algorithms belong to an important class of algorithms used in r...
research
∙
10/02/2020
Optimization over Random and Gradient Probabilistic Pixel Sampling for Fast, Robust Multi-Resolution Image Registration
This paper presents an approach to fast image registration through proba...
research
∙
10/02/2020
Uncertainty driven probabilistic voxel selection for image registration
This paper presents a novel probabilistic voxel selection strategy for m...
research
∙
09/24/2020
N-BEATS neural network for mid-term electricity load forecasting
We address the mid-term electricity load forecasting (MTLF) problem. Thi...
research
∙
07/30/2020
FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting
Forecasting of multivariate time-series is an important problem that has...
research
∙
02/07/2020
Meta-learning framework with applications to zero-shot time-series forecasting
Can meta-learning discover generic ways of processing time-series (TS) f...
research
∙
01/26/2020
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Inputs
Significant progress has been made recently in developing few-shot objec...
research
∙
12/18/2019
One-Shot Weakly Supervised Video Object Segmentation
Conventional few-shot object segmentation methods learn object segmentat...
research
∙
05/24/2019
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
We focus on solving the univariate times series point forecasting proble...
research
∙
02/19/2019
Adaptive Cross-Modal Few-Shot Learning
Metric-based meta-learning techniques have successfully been applied to ...
research
∙
05/23/2018
