Search Results for author:
Found 14 papers, 7 papers with code
What makes math problems hard for reinforcement learning: a case study
Using a long-standing conjecture from combinatorial group theory, we explore, from multiple perspectives, the challenges of finding rare instances carrying disproportionately high rewards.
PAGE: Parametric Generative Explainer for Graph Neural Network
Due to the dimensionality reduction of features in the latent space of the auto-encoder, it becomes easier to extract causal features leading to the model's output, which can be easily employed to generate explanations.
Boosting Generalization with Adaptive Style Techniques for Fingerprint Liveness Detection
We introduce a high-performance fingerprint liveness feature extraction technique that secured first place in LivDet 2023 Fingerprint Representation Challenge.
Wasserstein Distortion: Unifying Fidelity and Realism
We introduce a distortion measure for images, Wasserstein distortion, that simultaneously generalizes pixel-level fidelity on the one hand and realism or perceptual quality on the other.
D-Separation for Causal Self-Explanation
Instead of attempting to rectify the issues of the MMI criterion, we propose a novel criterion to uncover the causal rationale, termed the Minimum Conditional Dependence (MCD) criterion, which is grounded on our finding that the non-causal features and the target label are \emph{d-separated} by the causal rationale.
AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness
Online advertisements are important elements in e-commerce sites, social media platforms, and search engines.
Decoupled Rationalization with Asymmetric Learning Rates: A Flexible Lipschitz Restraint
However, such a cooperative game may incur the degeneration problem where the predictor overfits to the uninformative pieces generated by a not yet well-trained generator and in turn, leads the generator to converge to a sub-optimal model that tends to select senseless pieces.
MGR: Multi-generator Based Rationalization
Rationalization is to employ a generator and a predictor to construct a self-explaining NLP model in which the generator selects a subset of human-intelligible pieces of the input text to the following predictor.
Alternating Direction Method of Multiplier-Based Distributed Planning Model for Natural Gas, Electricity Network, and Regional Integrated Energy Systems
Simulation results illustrate that a distributed planning model is more sensitive to individual load differences, which is precisely the defect of the joint planning model.
AutoDebias: Learning to Debias for Recommendation
This provides a valuable opportunity to develop a universal solution for debiasing, e. g., by learning the debiasing parameters from data.
From Three Dimensional Manifolds to Modular Tensor Categories
We find modular tensor categories that realize candidate modular data constructed from Seifert fibered spaces and torus bundles over the circle that reveal many subtleties in the program.
Quantum Algebra Mathematical Physics Geometric Topology Mathematical Physics Quantum Physics 18M20, 57K16, 58J28
Scene Text Detection with Scribble Lines
It is a general labeling method for texts with various shapes and requires low labeling costs.
Predicting conversions in display advertising based on URL embeddings
Online display advertising is growing rapidly in recent years thanks to the automation of the ad buying process.
FedOCR: Communication-Efficient Federated Learning for Scene Text Recognition
In this paper, we study how to make use of decentralized datasets for training a robust scene text recognizer while keeping them stay on local devices.
