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Found 14 papers, 3 papers with code
A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenes
Three-Dimensional (3D) dense captioning is an emerging vision-language bridging task that aims to generate multiple detailed and accurate descriptions for 3D scenes.
SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
However, there has been little work that studies sparse basis recovery in the Federated Learning (FL) setting, where the client data's differential privacy (DP) must also be simultaneously protected.
Code-Based English Models Surprising Performance on Chinese QA Pair Extraction Task
In previous studies, code-based models have consistently outperformed text-based models in reasoning-intensive scenarios.
Insurance Contract for High Renewable Energy Integration
A proper insurance design needs to resolve the following two challenges: (i) users' reliability preference is private information; and (ii) the insurance design is tightly coupled with the renewable energy investment decision.
On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model
Our upper bound reveals that, between the two hidden-layers, the test error descends faster with respect to the number of neurons in the second hidden-layer (the one closer to the output) than with respect to that in the first hidden-layer (the one closer to the input).
SeedGNN: Graph Neural Networks for Supervised Seeded Graph Matching
However, most previous GNNs for this task use a semi-supervised approach, which requires a large number of seeds and cannot learn knowledge that is transferable to unseen graphs.
Time-of-use Pricing for Energy Storage Investment
Such a pricing scheme provides users with incentives to invest in behind-the-meter energy storage and to shift peak load towards low-price intervals.
Student-Teacher Learning from Clean Inputs to Noisy Inputs
Feature-based student-teacher learning, a training method that encourages the student's hidden features to mimic those of the teacher network, is empirically successful in transferring the knowledge from a pre-trained teacher network to the student network.
On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models
Specifically, for a class of learnable functions, we provide a new upper bound of the generalization error that approaches a small limiting value, even when the number of neurons $p$ approaches infinity.
The Power of $D$-hops in Matching Power-Law Graphs
Under the Chung-Lu random graph model with $n$ vertices, max degree $\Theta(\sqrt{n})$, and the power-law exponent $2<\beta<3$, we show that as soon as $D> \frac{4-\beta}{3-\beta}$, by optimally choosing the first slice, with high probability our algorithm can correctly match a constant fraction of the true pairs without any error, provided with only $\Omega((\log n)^{4-\beta})$ initial seeds.
Contract-based Time-of-use Pricing for Energy Storage Investment
We also show that the proposed contracts can reduce the system social cost by over 30%, compared with no storage investment benchmark.
Systems and Control Systems and Control
Graph Matching with Partially-Correct Seeds
We establish non-asymptotic performance guarantees of perfect matching for both $1$-hop and $2$-hop algorithms, showing that our new $2$-hop algorithm requires substantially fewer correct seeds than the $1$-hop algorithm when graphs are sparse.
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree
Under a sparse true linear regression model with $p$ i. i. d.
Virtual Energy Storage Sharing and Capacity Allocation
In our simulation results, the proposed storage virtualization model can reduce the physical energy storage investment of the aggregator by 54. 3% and reduce the users' total costs by 34. 7%, compared to the case where users acquire their own physical storage.
