Search Results for author:
Found 34 papers, 9 papers with code
Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Models
In this paper, we deeply explore the mechanisms employed by Transformer-based language models in factual recall tasks.
Unsigned Orthogonal Distance Fields: An Accurate Neural Implicit Representation for Diverse 3D Shapes
However, common distance field based implicit representations, specifically signed distance field (SDF) for watertight shapes or unsigned distance field (UDF) for arbitrary shapes, routinely suffer from degradation of reconstruction accuracy when converting to explicit surface points and meshes.
High-Dimensional Tail Index Regression: with An Application to Text Analyses of Viral Posts in Social Media
To conduct inference, we propose to debias the regularized estimate, and establish the asymptotic normality of the debiased estimator.
Is it Possible to Edit Large Language Models Robustly?
Q3: Which knowledge features are correlated with the performance and robustness of editing?
Inference in Auctions with Many Bidders Using Transaction Prices
Given the abundance of bidders in each auction, we propose an asymptotic framework in which the number of bidders diverges while the number of auctions remains fixed.
SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory Prediction
Analyzing and forecasting trajectories of agents like pedestrians and cars in complex scenes has become more and more significant in many intelligent systems and applications.
On the Inconsistency of Cluster-Robust Inference and How Subsampling Can Fix It
Conventional methods of cluster-robust inference are inconsistent in the presence of unignorably large clusters.
New Adversarial Image Detection Based on Sentiment Analysis
Deep Neural Networks (DNNs) are vulnerable to adversarial examples, while adversarial attack models, e. g., DeepFool, are on the rise and outrunning adversarial example detection techniques.
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on deep neural network-based classification models.
Extreme Changes in Changes
This paper proposes a new CIC estimator to accurately estimate treatment effects at extreme quantiles.
Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method
In light of these negative results about the existing CR methods, we propose a weighted CR (WCR) method as a simple fix.
Double Graphs Regularized Multi-view Subspace Clustering
In this paper, we propose a novel Double Graphs Regularized Multi-view Subspace Clustering (DGRMSC) method, which aims to harness both global and local structural information of multi-view data in a unified framework.
Global Weighted Tensor Nuclear Norm for Tensor Robust Principal Component Analysis
Tensor Robust Principal Component Analysis (TRPCA), which aims to recover a low-rank tensor corrupted by sparse noise, has attracted much attention in many real applications.
Dispersed Pixel Perturbation-based Imperceptible Backdoor Trigger for Image Classifier Models
In this paper, we propose a new backdoor trigger, which is easy to generate, imperceptible, and highly effective.
Capital and Labor Income Pareto Exponents in the United States, 1916-2019
Accurately estimating income Pareto exponents is challenging due to limitations in data availability and the applicability of statistical methods.
Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data
Administrative data are often easier to access as tabulated summaries than in the original format due to confidentiality concerns.
Non-Existent Moments of Earnings Growth
Our empirical analysis reveals that population kurtosis, skewness, and variance often do not exist for the conditional distribution of earnings growth.
Error-based Knockoffs Inference for Controlled Feature Selection
Recently, the scheme of model-X knockoffs was proposed as a promising solution to address controlled feature selection under high-dimensional finite-sample settings.
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters
Specifically, in order to ensure both the training efficiency and the training accuracy, we design a novel hybrid training algorithm, where the embedding layer and the dense neural network are handled by different synchronization mechanisms; then we build a system called Persia (short for parallel recommendation training system with hybrid acceleration) to support this hybrid training algorithm.
Three-dimensional Cooperative Localization of Commercial-Off-The-Shelf Sensors
We propose an efficient approach to solve the corresponding permutation combinatorial optimization problem, which integrates continuous space cooperative localization and permutation space likelihood ascent search.
Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese
Although pre-trained models (PLMs) have achieved remarkable improvements in a wide range of NLP tasks, they are expensive in terms of time and resources.
Audio Injection Adversarial Example Attack
We study the problem of audio adversarial example attacks with sparse perturbations.
Analyzing Adversarial Robustness of Deep Neural Networks in Pixel Space: a Semantic Perspective
Previous works study the adversarial robustness of image classifiers on image level and use all the pixel information in an image indiscriminately, lacking of exploration of regions with different semantic meanings in the pixel space of an image.
Fixed-k Tail Regression: New Evidence on Tax and Wealth Inequality from Forbes 400
We develop a novel fixed-k tail regression method that accommodates the unique feature in the Forbes 400 data that observations are truncated from below at the 400th largest order statistic.
Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction
Specifically, we divide the AOPE task into aspect term extraction (ATE) and aspect-specified opinion extraction (ASOE) subtasks; we first extract all the candidate aspect terms and then the corresponding opinion words given the aspect term.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Semantic Segmentation by Improved Generative Adversarial Networks
Besides, the adversarial loss aggressively encourages the output image to be close to the distribution of the ground truth.
Data-Free Adversarial Perturbations for Practical Black-Box Attack
Adversarial examples often exhibit black-box attacking transferability, which allows that adversarial examples crafted for one model can fool another model.
6D Object Pose Regression via Supervised Learning on Point Clouds
We use depth information represented by point clouds as the input to both deep networks and geometry-based pose refinement and use separate networks for rotation and translation regression.
Interpretable Disentanglement of Neural Networks by Extracting Class-Specific Subnetwork
We also apply extracted subnetworks in visual explanation and adversarial example detection tasks by merely replacing the original full model with class-specific subnetworks.
Pruning from Scratch
Network pruning is an important research field aiming at reducing computational costs of neural networks.
Threshold Regression with Nonparametric Sample Splitting
This paper develops a threshold regression model where an unknown relationship between two variables nonparametrically determines the threshold.
Interpret Neural Networks by Identifying Critical Data Routing Paths
Interpretability of a deep neural network aims to explain the rationale behind its decisions and enable the users to understand the intelligent agents, which has become an important issue due to its importance in practical applications.
A Hierarchical Recurrent Neural Network for Symbolic Melody Generation
In this paper, we present a hierarchical recurrent neural network for melody generation, which consists of three Long-Short-Term-Memory (LSTM) subnetworks working in a coarse-to-fine manner along time.
Sound Multimedia
Modal Regression based Atomic Representation for Robust Face Recognition
In this paper, we propose a modal regression based atomic representation and classification (MRARC) framework to alleviate such limitation.
