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AI is analyzing your overall score…
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
dito
January 31, 2025 – January 31, 2025
Official PyTorch Implementation of "Diffusion Autoencoders are Scalable Image Tokenizers"
View Projectinfd
August 25, 2024 – November 8, 2024
Image Neural Field Diffusion Models, CVPR 2024 (Highlight)
View Projecttrans-inr
August 4, 2022 – August 13, 2022
Transformers as Meta-Learners for Implicit Neural Representations, in ECCV 2022
View Projectliif
December 16, 2020 – August 21, 2021
Learning Continuous Image Representation with Local Implicit Image Function, in CVPR 2021 (Oral)
View Projectfew-shot-meta-baseline
January 12, 2020 – October 10, 2021
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning, in ICCV 2021
View Projectprototypical-network-pytorch
July 7, 2018 – March 26, 2020
A re-implementation of "Prototypical Networks for Few-shot Learning"
View ProjectDGP
May 31, 2018 – June 22, 2019
Rethinking Knowledge Graph Propagation for Zero-Shot Learning, in CVPR 2019
View ProjectCultural Fit Analysis
The candidate's project history is heavily skewed towards academic research in computer vision and machine learning, primarily using Python. While this demonstrates strong technical capabilities in a specific domain, the breadth of technologies and project types for a general 'Backend Engineer' role is limited. There is no explicit experience in typical backend development areas like API design, database management, distributed systems, or cloud platforms. This suggests a potential mismatch with a standard backend engineering role, though the candidate's problem-solving skills from research could be transferable.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's project descriptions indicate a focus on research and technical implementation.