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Found 12 papers, 1 papers with code
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning
When estimating CATE using high-dimensional data, there have been many variable selection methods and neural network approaches based on representation learning, while these methods do not provide a way to verify whether the subset of variables after dimensionality reduction or the learned representations still satisfy the unconfoundedness assumption during the estimation process, which can lead to ineffective estimates of the treatment effects.
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Another promising line of research involves the adoption of modularity maximization, a popular and effective measure for community detection, as the guiding principle for clustering tasks.
Clean-image Backdoor Attacks
To gather a significant quantity of annotated training data for high-performance image classification models, numerous companies opt to enlist third-party providers to label their unlabeled data.
Self-supervision meets kernel graph neural models: From architecture to augmentations
Recently, the idea of designing neural models on graphs using the theory of graph kernels has emerged as a more transparent as well as sometimes more expressive alternative to MPNNs known as kernel graph neural networks (KGNNs).
Non-Line-of-Sight Imaging With Signal Superresolution Network
This paper proposes a general learning-based pipeline for increasing imaging quality with only a few scanning points.
Differentially Private Learning with Per-Sample Adaptive Clipping
As one way to implement privacy-preserving AI, differentially private learning is a framework that enables AI models to use differential privacy (DP).
Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization
In this work, we propose a signal-surface collaborative regularization (SSCR) framework that provides noise-robust reconstructions with a minimal number of measurements.
Non-line-of-sight imaging with arbitrary illumination and detection pattern
Non-line-of-sight (NLOS) imaging aims at reconstructing targets obscured from the direct line of sight.
Protecting Split Learning by Potential Energy Loss
In this paper, we focus on the privacy leakage from the forward embeddings of split learning.
SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing
Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as computer vision, natural language processing, etc.
Coherent ray-wave structured light based on (helical) Ince-Gaussian modes
The topological evolution of classic eigenmodes including Hermite-Laguerre-Gaussian and (helical) InceGaussian modes is exploited to construct coherent state modes, which unifies the representations of travelingwave (TW) and standing-wave (SW) ray-wave structured light for the first time and realizes the TW-SW unified ray-wave geometric beam with topology of raytrajectories splitting effect, breaking the boundary of TW and SW structured light.
Optics
A One-Shot Learning Framework for Assessment of Fibrillar Collagen from Second Harmonic Generation Images of an Infarcted Myocardium
Myocardial infarction (MI) is a scientific term that refers to heart attack.
