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Found 8 papers, 4 papers with code
Disentangling Multi-view Representations Beyond Inductive Bias
In this paper, we propose a novel multi-view representation disentangling method that aims to go beyond inductive biases, ensuring both interpretability and generalizability of the resulting representations.
Online Dynamic Acknowledgement with Learned Predictions
This elegant model studies the trade-off between acknowledgement cost and waiting experienced by requests.
A Clustering-guided Contrastive Fusion for Multi-view Representation Learning
To this end, we utilize a deep fusion network to fuse view-specific representations into the view-common representation, extracting high-level semantics for obtaining robust representation.
Single-Stage Is Enough: Multi-Person Absolute 3D Pose Estimation
The existing multi-person absolute 3D pose estimation methods are mainly based on two-stage paradigm, i. e., top-down or bottom-up, leading to redundant pipelines with high computation cost.
Learning-Augmented Algorithms for Online Steiner Tree
Steiner tree is known to have strong lower bounds in the online setting and any algorithm's worst-case guarantee is far from desirable.
Finite generation for valuations computing stability thresholds and applications to K-stability
We prove that on any log Fano pair of dimension $n$ whose stability threshold is less than $\frac{n+1}{n}$, any valuation computing the stability threshold has a finitely generated associated graded ring.
Algebraic Geometry Differential Geometry
Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing
Instance robustness ensures that the prediction is robust to modest changes in the problem input, where the measure of the change may be problem specific.
An Improved Neural Segmentation Method Based on U-NET
Compared with U-NET and SegNet, the improved U-NET network has fewer training parameters, shorter training time and get a great improvement in segmentation effect.
