Justin Solomon
24 papers · 2014–2025 · 8 conferences · across top CS/AI conferences
Achievements
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π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (18) π§ Keyword Pioneer π£ Hot Topic Early Bird π Conference Polyglot (8)
π
Interdisciplinary Bridge
π
Conference Polyglot
(8)
π
Academic Marathon
(11)
ποΈ
Keyword Collector
(83)
π
Century Club
(24)
π₯
Unstoppable
(8)
π
Trend Setter
β‘
Prolific Year
(6)
Conferences
ICML (8)
ICLR (6)
CVPR (3)
CORL (2)
NIPS (2)
ECCV (1)
JMLR (1)
UAI (1)
Top co-authors
Keywords
optimal transport
(4)
neural network
(2)
semi-supervised learning
(2)
3d object detection
(2)
wasserstein distance
(2)
representation learning
(2)
image segmentation
(1)
semantic segmentation
(1)
active learning
(1)
image generation
(1)
variational inference
(1)
bayesian inference
(1)
3d shape generation
(1)
remote sensing
(1)
multi-task learning
(1)
deep learning
(1)
implicit representation
(1)
point cloud
(1)
self-supervised learning
(1)
metric learning
(1)
Papers
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
ICML 2025
Score Distillation via Reparametrized DDIM
NIPS 2024
Nuclear Norm Regularization for Deep Learning
NIPS 2024
Asymmetry in Low-Rank Adapters of Foundation Models
ICML 2024
Slicing Mutual Information Generalization Bounds for Neural Networks
ICML 2024
Learning Proximal Operators to Discover Multiple Optima
ICLR 2023
Sampling with Mollified Interaction Energy Descent
ICLR 2023
Riemannian Metric Learning via Optimal Transport
ICLR 2023
Representation Learning for Object Detection from Unlabeled Point Cloud Sequences
CORL 2022
DeepCurrents: Learning Implicit Representations of Shapes With Boundaries
CVPR 2022
Incorporating Unlabeled Data into Distributionally Robust Learning
JMLR 2021
DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
CORL 2021
Improving approximate optimal transport distances using quantization
UAI 2021
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
ICLR 2021
Polygonal Building Extraction by Frame Field Learning
CVPR 2021
Learning Manifold Patch-Based Representations of Man-Made Shapes
ICLR 2021
Model Fusion with Kullback-Leibler Divergence
ICML 2020
Pillar-based Object Detection for Autonomous Driving
ECCV 2020
Deep Parametric Shape Predictions Using Distance Fields
CVPR 2020
Learning Embeddings into Entropic Wasserstein Spaces
ICLR 2019
Stochastic Wasserstein Barycenters
ICML 2018
Gromov-Wasserstein Averaging of Kernel and Distance Matrices
ICML 2016
Exponential Integration for Hamiltonian Monte Carlo
ICML 2015
Wasserstein Propagation for Semi-Supervised Learning
ICML 2014