Search Results for author:
Found 37 papers, 7 papers with code
Crime Forecasting: A Spatio-temporal Analysis with Deep Learning Models
We conducted a comparative analysis to access the effects of various data sequences, including raw and binned data, on the prediction errors of four deep learning forecasting models.
OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data
However, most of the cutting-edge progress in mathematical reasoning with LLMs has become \emph{closed-source} due to lack of access to training data.
Ranked #4 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Towards reliable respiratory disease diagnosis based on cough sounds and vision transformers
Based on the observations from such an extensive empirical study, we propose a novel approach to cough-based disease classification based on both self-supervised and supervised learning on a large-scale cough data set.
Improving Generalization of Neural Vehicle Routing Problem Solvers Through the Lens of Model Architecture
The DS decoder explicitly models VRPs of multiple training distribution patterns through multiple auxiliary light decoders, expanding the model representation space to encompass a broader range of distributional scenarios.
TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models
To improve the recall of the RAG for the target contexts, we introduce a knowledge graph to construct structured data to achieve hard matching at a fine-grained level.
An Enhanced Differential Grouping Method for Large-Scale Overlapping Problems
In this article, we propose a two-stage enhanced grouping method for large-scale overlapping problems, called OEDG, which achieves accurate grouping while significantly reducing computational resource consumption.
A Composite Decomposition Method for Large-Scale Global Optimization
Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a non-separable variable grouping method.
SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer
Although pre-training achieves remarkable performance, it suffers from task-agnostic backdoor attacks due to vulnerabilities in data and training mechanisms.
How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study
Previous work has showcased the intriguing capability of large language models (LLMs) in retrieving facts and processing context knowledge.
Revisiting the Information Capacity of Neural Network Watermarks: Upper Bound Estimation and Beyond
To trace the copyright of deep neural networks, an owner can embed its identity information into its model as a watermark.
Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models
Recent work has showcased the powerful capability of large language models (LLMs) in recalling knowledge and reasoning.
Detection of Opioid Users from Reddit Posts via an Attention-based Bidirectional Recurrent Neural Network
The opioid epidemic, referring to the growing hospitalizations and deaths because of overdose of opioid usage and addiction, has become a severe health problem in the United States.
Diffusion Model-Based Multiobjective Optimization for Gasoline Blending Scheduling
The presence of nonlinearity, integer constraints, and a large number of decision variables adds complexity to this problem, posing challenges for traditional and evolutionary algorithms.
Knowledge-Assisted Dual-Stage Evolutionary Optimization of Large-Scale Crude Oil Scheduling
On the basis of the proposed model, a dual-stage evolutionary algorithm driven by heuristic rules (denoted by DSEA/HR) is developed, where the dual-stage search mechanism consists of global search and local refinement.
A Novel Dual-Stage Evolutionary Algorithm for Finding Robust Solutions
The primary objective of the peak-detection stage is to identify peaks in the fitness landscape of the original optimization problem.
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations
Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs).
Effective Proxy for Human Labeling: Ensemble Disagreement Scores in Large Language Models for Industrial NLP
For industry applications, it is imperative to assess the performance of the LLM on unlabeled production data from time to time to validate for a real-world setting.
3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack
With the maturity of depth sensors, the vulnerability of 3D point cloud models has received increasing attention in various applications such as autonomous driving and robot navigation.
A Robust Classifier Under Missing-Not-At-Random Sample Selection Bias
In this paper, we propose BiasCorr, an algorithm that improves on Greene's method by modifying the original training set in order for a classifier to learn under MNAR sample selection bias.
UOR: Universal Backdoor Attacks on Pre-trained Language Models
In this paper, we first summarize the requirements that a more threatening backdoor attack against PLMs should satisfy, and then propose a new backdoor attack method called UOR, which breaks the bottleneck of the previous approach by turning manual selection into automatic optimization.
Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning
This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids.
Compressing Cross-Lingual Multi-Task Models at Qualtrics
Experience management is an emerging business area where organizations focus on understanding the feedback of customers and employees in order to improve their end-to-end experiences.
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning
In this paper, we propose "FedPrompt" to study prompt tuning in a model split aggregation way using FL, and prove that split aggregation greatly reduces the communication cost, only 0. 01% of the PLMs' parameters, with little decrease on accuracy both on IID and Non-IID data distribution.
Experience of the COVID-19 pandemic in Wuhan leads to a lasting increase in social distancing
On 11th Jan 2020, the first COVID-19 related death was confirmed in Wuhan, Hubei.
Towards Fairness-Aware Multi-Objective Optimization
Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications.
Poisoning Attacks on Fair Machine Learning
Our framework enables attackers to flexibly adjust the attack's focus on prediction accuracy or fairness and accurately quantify the impact of each candidate point to both accuracy loss and fairness violation, thus producing effective poisoning samples.
Robust Fairness-aware Learning Under Sample Selection Bias
However, the assumption is often violated in real world due to the sample selection bias between the training and test data.
Coordinated Frequency and Voltage Regulation of Grid-Following and Grid-Forming Inverters
In a purely inverter-based microgrid, both grid-forming (GFM) and grid-following (GFL) inverters will have a crucial role to play in frequency/voltage regulation and maintaining power sharing through their grid support capabilities.
Fairness-aware Agnostic Federated Learning
In this paper, we develop a fairness-aware agnostic federated learning framework (AgnosticFair) to deal with the challenge of unknown testing distribution.
PoliteCamera: Respecting Strangers' Privacy in Mobile Photographing
Through the cooperation between a photographer and a stranger, the stranger's face in a photo can be automatically blurred upon his request when the photo is taken.
Multi-Resolution A*
We show that MRA* is bounded suboptimal with respect to the anchor resolution search space and resolution complete.
Removing Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy
In this work, we analyze the inequality in utility loss by differential privacy and propose a modified differentially private stochastic gradient descent (DPSGD), called DPSGD-F, to remove the potential disparate impact of differential privacy on the protected group.
Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach
Peer-to-peer knowledge transfer in distributed environments has emerged as a promising method since it could accelerate learning and improve team-wide performance without relying on pre-trained teachers in deep reinforcement learning.
Prepaid or Postpaid? That is the question. Novel Methods of Subscription Type Prediction in Mobile Phone Services
Our study reveals that (a) postpaid customers are more active in terms of service usage and (b) there are strong structural correlations in the mobile phone call network as connections between customers of the same subscription type are much more frequent than those between customers of different subscription types.
Robust Order Scheduling in the Fashion Industry: A Multi-Objective Optimization Approach
In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines.
Differential Evolution with Event-Triggered Impulsive Control
In this paper, an event-triggered impulsive control scheme (ETI) is introduced to improve the performance of DE.
