Boris N. Oreshkin | DeepAI

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Mennatullah Siam
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Grzegorz Dudek
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∙ 02/09/2023

HybrIK-Transformer

HybrIK relies on a combination of analytical inverse kinematics and deep...

0 Boris N. Oreshkin, et al. ∙

∙ 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...

8 Vikram Voleti, et al. ∙

∙ 01/30/2022

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

Recent progress in neural forecasting accelerated improvements in the pe...

2 Cristian Challu, et al. ∙

∙ 01/18/2022

Motion Inbetweening via Deep Δ-Interpolator

We show that the task of synthesizing missing middle frames, commonly kn...

10 Boris N. Oreshkin, et al. ∙

∙ 09/20/2021

Neural forecasting at scale

We study the problem of efficiently scaling ensemble-based deep neural n...

0 Philippe Chatigny, et al. ∙

∙ 06/03/2021

ProtoRes: Proto-Residual Architecture for Deep Modeling of Human Pose

Our work focuses on the development of a learnable neural representation...

0 Boris N. Oreshkin, et al. ∙

∙ 12/31/2020

Adaptive filters for the moving target indicator system

Adaptive algorithms belong to an important class of algorithms used in r...

0 Boris N. Oreshkin, et al. ∙

∙ 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...

0 Boris N. Oreshkin, et al. ∙

∙ 10/02/2020

Uncertainty driven probabilistic voxel selection for image registration

This paper presents a novel probabilistic voxel selection strategy for m...

0 Boris N. Oreshkin, et al. ∙

∙ 09/24/2020

N-BEATS neural network for mid-term electricity load forecasting

We address the mid-term electricity load forecasting (MTLF) problem. Thi...

0 Boris N. Oreshkin, et al. ∙

∙ 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...

0 Boris N. Oreshkin, et al. ∙

∙ 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...

63 Boris N. Oreshkin, et al. ∙

∙ 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...

9 Mennatullah Siam, et al. ∙

∙ 12/18/2019

One-Shot Weakly Supervised Video Object Segmentation

Conventional few-shot object segmentation methods learn object segmentat...

0 Mennatullah Siam, et al. ∙

∙ 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...

0 Boris N. Oreshkin, et al. ∙

∙ 02/19/2019

Adaptive Cross-Modal Few-Shot Learning

Metric-based meta-learning techniques have successfully been applied to ...

0 Chen Xing, et al. ∙

∙ 05/23/2018

TADAM: Task dependent adaptive metric for improved few-shot learning

Few-shot learning has become essential for producing models that general...

0 Boris N. Oreshkin, et al. ∙

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