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Found 16 papers, 8 papers with code
3D Molecular Geometry Analysis with 2D Graphs
Here, we propose a novel deep learning framework to predict 3D geometries from molecular graphs.
Probably Approximate Shapley Fairness with Applications in Machine Learning
We observe that the fairness guarantees of exact SVs are too restrictive for SV estimates.
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games
We show that for any two players, under some regularity conditions, their difference in Shapley value converges in probability to the difference in Shapley value of a limiting game whose characteristic function is proportional to the log-determinant of the joint Fisher information.
AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI
Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards
This paper presents a novel collaborative generative modeling (CGM) framework that incentivizes collaboration among self-interested parties to contribute data to a pool for training a generative model (e. g., GAN), from which synthetic data are drawn and distributed to the parties as rewards commensurate to their contributions.
Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning
In this paper, we adopt federated learning as a gradient-based formalization of collaborative machine learning, propose a novel cosine gradient Shapley value to evaluate the agents’ uploaded model parameter updates/gradients, and design theoretically guaranteed fair rewards in the form of better model performance.
Validation Free and Replication Robust Volume-based Data Valuation
We observe that the diversity of the data points is an inherent property of the dataset that is independent of validation.
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods.
Ranked #1 on
3D Geometry Prediction
on Molecule3D val
Group Contrastive Self-Supervised Learning on Graphs
Our framework embeds the given graph into multiple subspaces, of which each representation is prompted to encode specific characteristics of graphs.
A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning
In this paper, we propose a novel Robust and Fair Federated Learning (RFFL) framework to achieve collaborative fairness and adversarial robustness simultaneously via a reputation mechanism.
Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping
Grouping has been commonly used in deep metric learning for computing diverse features.
Searching k-Optimal Goals for an Orienteering Problem on a Specialized Graph with Budget Constraints
We propose a novel non-randomized anytime orienteering algorithm for finding k-optimal goals that maximize reward on a specialized graph with budget constraints.
Collaborative Fairness in Federated Learning
In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability.
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection
This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristic-based subgoal selection, experience replay (Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning (Bengio et al. 2009; Zaremba and Sutskever 2014).
Deep Asymmetric Metric Learning via Rich Relationship Mining
The asymmetric structure enables the two data streams to interlace each other, which allows for the informative comparison between new data pairs over iterations.
