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Found 8 papers, 2 papers with code
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems.
Constructing Canonical Feynman Integrals with Intersection Theory
Canonical Feynman integrals are of great interest in the study of scattering amplitudes at the multi-loop level.
High Energy Physics - Theory High Energy Physics - Phenomenology
Odd-even layer-number effect and layer-dependent magnetic phase diagrams in MnBi2Te4
However, the layer-dependent magnetism of MnBi2Te4, which is fundamental and crucial for further exploration of quantum phenomena in this system, remains elusive.
Materials Science
PGLP: Customizable and Rigorous Location Privacy through Policy Graph
Third, we design a private location trace release framework that pipelines the detection of location exposure, policy graph repair, and private trajectory release with customizable and rigorous location privacy.
Cryptography and Security Computers and Society
Improving Generalization via Attribute Selection on Out-of-the-box Data
Unfortunately, previous attribute selection methods are conducted based on the seen data, and their selected attributes have poor generalization capability to the unseen data, which is unavailable in the training stage of ZSL tasks.
Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation
In most of existing attribute-based research, class-specific attributes (CSA), which are class-level annotations, are usually adopted due to its low annotation cost for each class instead of each individual image.
Target-Independent Active Learning via Distribution-Splitting
By discovering the connections between hypothesis and input distribution, we map the volume of version space into the number density and propose a target-independent distribution-splitting strategy with the following advantages: 1) provide theoretical guarantees on reducing label complexity and error rate as volume-splitting; 2) break the curse of initial hypothesis; 3) provide model guidance for a target-independent AL algorithm in real AL tasks.
Complementary Attributes: A New Clue to Zero-Shot Learning
Extensive experiments on five ZSL benchmark datasets and the large-scale ImageNet dataset demonstrate that the proposed complementary attributes and rank aggregation can significantly and robustly improve existing ZSL methods and achieve the state-of-the-art performance.
