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Found 25 papers, 5 papers with code
TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System
In this paper, we propose TrendSim, an LLM-based multi-agent system to simulate trending topics in social media under poisoning attacks.
OpenAI-o1 AB Testing: Does the o1 model really do good reasoning in math problem solving?
The Orion-1 model by OpenAI is claimed to have more robust logical reasoning capabilities than previous large language models.
Seesaw Experimentation: A/B Tests with Spillovers
This paper examines how spillover effects in A/B testing can impede organizational progress and develops strategies for mitigating these challenges.
AI Persuasion, Bayesian Attribution, and Career Concerns of Doctors
AI's interpretability influences how doctors attribute these sources and their willingness to change their minds.
A Random-patch based Defense Strategy Against Physical Attacks for Face Recognition Systems
In this paper, we propose a random-patch based defense strategy to robustly detect physical attacks for Face Recognition System (FRS).
Allegro-Legato: Scalable, Fast, and Robust Neural-Network Quantum Molecular Dynamics via Sharpness-Aware Minimization
Specifically, Allegro-Legato exhibits much weaker dependence of timei-to-failure on the problem size, $t_{\textrm{failure}} \propto N^{-0. 14}$ ($N$ is the number of atoms) compared to the SOTA Allegro model $\left(t_{\textrm{failure}} \propto N^{-0. 29}\right)$, i. e., systematically delayed time-to-failure, thus allowing much larger and longer NNQMD simulations without failure.
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative Learning
Specifically, there are an object detection task (consisting of an instance-classification task and a localization task) and an image-classification task in our framework, responsible for utilizing the two types of supervision.
Equalized Focal Loss for Dense Long-Tailed Object Detection
The conventional focal loss balances the training process with the same modulating factor for all categories, thus failing to handle the long-tailed problem.
Dynamic Selection in Algorithmic Decision-making
This paper identifies and addresses dynamic selection problems in online learning algorithms with endogenous data.
PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning
The application of deep learning to medical image segmentation has been hampered due to the lack of abundant pixel-level annotated data.
Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity
We model this as a Markov decision process and show that the dynamic interaction between data generation and data analysis leads to a new type of bias -- reinforcement bias -- that exacerbates the endogeneity problem in standard data analysis.
Deblurring Processor for Motion-Blurred Faces Based on Generative Adversarial Networks
Then we conducted a motion blur image generation experiment on some general facial data set, and used the pairs of blurred and sharp face image data to perform the training and testing experiments of the processor GAN, and gave some visual displays.
An Alternative Practice of Tropical Convolution to Traditional Convolutional Neural Networks
Convolutional neural networks (CNNs) have been used in many machine learning fields.
Min-Max-Plus Neural Networks
We present a new model of neural networks called Min-Max-Plus Neural Networks (MMP-NNs) based on operations in tropical arithmetic.
L-SNet: from Region Localization to Scale Invariant Medical Image Segmentation
Coarse-to-fine models and cascade segmentation architectures are widely adopted to solve the problem of large scale variations in medical image segmentation.
Cross-Modal Self-Attention Distillation for Prostate Cancer Segmentation
In this paper, we develop a cross-modal self-attention distillation network by fully exploiting the encoded information of the intermediate layers from different modalities, and the extracted attention maps of different modalities enable the model to transfer the significant spatial information with more details.
Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data
In this paper we develop a data-driven smoothing technique for high-dimensional and non-linear panel data models.
Shape-Enforcing Operators for Point and Interval Estimators
A common problem in econometrics, statistics, and machine learning is to estimate and make inference on functions that satisfy shape restrictions.
Estimation and Inference of Treatment Effects with $L_2$-Boosting in High-Dimensional Settings
In this paper, we provide results for valid inference after post- or orthogonal $L_2$-Boosting is used for variable selection.
$L_2$Boosting for Economic Applications
In the recent years more and more high-dimensional data sets, where the number of parameters $p$ is high compared to the number of observations $n$ or even larger, are available for applied researchers.
Detecting "Smart" Spammers On Social Network: A Topic Model Approach
Spammer detection on social network is a challenging problem.
High-Dimensional $L_2$Boosting: Rate of Convergence
Finally, we present simulation studies and applications to illustrate the relevance of our theoretical results and to provide insights into the practical aspects of boosting.
The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages
They are as convenient and easy to report in practice as the conventional average partial effects.
Methodology Econometrics
Actionness-Assisted Recognition of Actions
We elicit from a fundamental definition of action low-level attributes that can reveal agency and intentionality.
