Search Results for author:
Found 13 papers, 5 papers with code
Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactions
Through the integration of classical computation and quantum simulation, this hybrid framework paves way for utilizing large-scale quantum simulations as priors in deep generative models.
Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions
We further propose a novel adaptive mapping to estimate the effective inverse temperature in quantum annealers.
Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching
Stereo matching methods based on iterative optimization, like RAFT-Stereo and IGEV-Stereo, have evolved into a cornerstone in the field of stereo matching.
FlowDA: Unsupervised Domain Adaptive Framework for Optical Flow Estimation
This paper introduces FlowDA, an unsupervised domain adaptive (UDA) framework for optical flow estimation.
Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume
The full 4D cost volume in Recurrent All-Pairs Field Transforms (RAFT) or global matching by Transformer achieves impressive performance for optical flow estimation.
CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models
The Large Hadron Collider's high luminosity era presents major computational challenges in the analysis of collision events.
MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo Matching
This architecture mitigates the multi-peak distribution problem in matching through the multi-peak lookup strategy, and integrates the coarse-to-fine concept into the iterative framework via the cascade search range.
AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation
In Neural Machine Translation (NMT), data augmentation methods such as back-translation have proven their effectiveness in improving translation performance.
Supervised Contrastive Learning with Structure Inference for Graph Classification
In this paper, we propose a novel graph neural network based on supervised contrastive learning with structure inference for graph classification.
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services.
Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report
Image super-resolution is one of the most popular computer vision problems with many important applications to mobile devices.
Bilingual Terminology Extraction from Comparable E-Commerce Corpora
Bilingual terminologies are important machine translation resources in the field of e-commerce, which are usually either manually translated or automatically extracted from parallel data.
Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary.
