Search Results for author:
Found 91 papers, 33 papers with code
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
This study led to Pangu Ultra MoE, a sparse LLM with 718 billion parameters, and we conducted experiments on the model to verify the simulation results.
WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
We demonstrate the effectiveness of WirelessAgent through a comprehensive case study on network slicing.
A Study on Group Decision Making Problem Based on Fuzzy Reasoning and Bayesian Networks
Aiming at the group decision - making problem with multi - objective attributes, this study proposes a group decision - making system that integrates fuzzy inference and Bayesian network.
Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model
UniGRF provides an effective, scalable, and compatible framework for generative recommendation systems.
Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs
Our exploration demonstrates that Ascend NPUs are capable of efficiently and effectively training dense models with more than 100 billion parameters.
Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms
Developing computer vision-based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production.
From Chaos to Order: The Atomic Reasoner Framework for Fine-grained Reasoning in Large Language Models
Recent advances in large language models (LLMs) have shown remarkable progress, yet their capacity for logical ``slow-thinking'' reasoning persists as a critical research frontier.
THz Beam Squint Mitigation via 3D Rotatable Antennas
In particular, we focus on a wideband wide-beam coverage problem in this paper, aiming to maximize the minimum beamforming gain within a given angle and frequency range by jointly optimizing the analog beamforming vector and the 3D rotation angles of the antenna array.
Benchmarking LLMs in Recommendation Tasks: A Comparative Evaluation with Conventional Recommenders
However, a comprehensive benchmark is needed to thoroughly evaluate and compare the recommendation capabilities of LLMs with traditional recommender systems.
COSINT-Agent: A Knowledge-Driven Multimodal Agent for Chinese Open Source Intelligence
Open Source Intelligence (OSINT) requires the integration and reasoning of diverse multimodal data, presenting significant challenges in deriving actionable insights.
A Universal Framework for Compressing Embeddings in CTR Prediction
Then, we integrate a contrastive learning mechanism to ensure a uniform distribution of quantized codes, enhancing the distinctiveness of embeddings.
Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
In the era of information overload, recommendation systems play a pivotal role in filtering data and delivering personalized content.
Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond
Given an unnormalized probability density $\pi\propto\mathrm{e}^{-V}$, estimating its normalizing constant $Z=\int_{\mathbb{R}^d}\mathrm{e}^{-V(x)}\mathrm{d}x$ or free energy $F=-\log Z$ is a crucial problem in Bayesian statistics, statistical mechanics, and machine learning.
Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms
We rigorously analyze the proposed schemes and establish the second-order accuracy of the $\theta$-trapezoidal method in KL divergence.
Image Classification with Deep Reinforcement Active Learning
Active learning is an alternative paradigm that mitigates the effort in hand-labeling data, where only a small fraction is iteratively selected from a large pool of unlabeled data, and annotated by an expert (a. k. a oracle), and eventually used to update the learning models.
Scaling New Frontiers: Insights into Large Recommendation Models
In this paper, we aim to enhance the understanding of scaling laws by conducting comprehensive evaluations of large recommendation models.
Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy
In response, we introduce the Performance Law for SR models, which aims to theoretically investigate and model the relationship between model performance and data quality.
Gotta Hear Them All: Sound Source Aware Vision to Audio Generation
By addressing V2A generation at the sound-source level, SSV2A surpasses state-of-the-art methods in both generation fidelity and relevance as evidenced by extensive experiments.
Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction
Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction.
Enhancing CTR Prediction in Recommendation Domain with Search Query Representation
Since users and items are often shared between the search and recommendation domains, there is a valuable opportunity to enhance the recommendation domain by leveraging user preferences extracted from the search domain.
Plug-and-Play Controllable Generation for Discrete Masked Models
This methodological development enables broad applications across downstream tasks such as class-specific image generation and protein design.
WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
Wireless networks are increasingly facing challenges due to their expanding scale and complexity.
Learning effective pruning at initialization from iterative pruning
To validate the accuracy and generalization of our method, we performed PaI across various models.
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation
Its objective is to extract knowledge from extensive pre-training data and fine-tune the model for downstream tasks.
High-throughput 3D shape completion of potato tubers on a harvester
With an average 3D shape completion time of 10 milliseconds per tuber, we can conclude that CoRe++ is both fast and accurate enough to be implemented on an operational harvester for high-throughput potato yield estimation.
Provable Benefit of Annealed Langevin Monte Carlo for Non-log-concave Sampling
We consider the outstanding problem of sampling from an unnormalized density that may be non-log-concave and multimodal.
All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users' diverse information needs.
Entropy Law: The Story Behind Data Compression and LLM Performance
Inspired by the information compression nature of LLMs, we uncover an ``entropy law'' that connects LLM performance with data compression ratio and first-epoch training loss, which reflect the information redundancy of a dataset and the mastery of inherent knowledge encoded in this dataset, respectively.
Dataset Regeneration for Sequential Recommendation
The sequential recommender (SR) system is a crucial component of modern recommender systems, as it aims to capture the evolving preferences of users.
WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence
To empower LLMs with knowledge and expertise in the wireless domain, this paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks.
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation
Cross-domain sequential recommendation (CDSR) aims to uncover and transfer users' sequential preferences across multiple recommendation domains.
DODA: Diffusion for Object-detection Domain Adaptation in Agriculture
The diverse and high-quality content generated by recent generative models demonstrates the great potential of using synthetic data to train downstream models.
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation
In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to a cart, and purchasing.
A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications
However, since FL enables a continuous share of knowledge among participants with each communication round while not allowing local data to be accessed by other participants, FTL faces many unique challenges that are not present in TL.
Learning to Manipulate Artistic Images
Recent advancement in computer vision has significantly lowered the barriers to artistic creation.
JMA: a General Algorithm to Craft Nearly Optimal Targeted Adversarial Example
Most of the approaches proposed so far to craft targeted adversarial examples against Deep Learning classifiers are highly suboptimal and typically rely on increasing the likelihood of the target class, thus implicitly focusing on one-hot encoding settings.
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation
To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems.
Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations
Existing methods fail to exploit flexibility and address the concerns of non-robustness simultaneously.
Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems
We propose a graph-based approach (named MMSR) to fuse modality features in an adaptive order, enabling each modality to prioritize either its inherent sequential nature or its interplay with other modalities.
Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, crucial in applications like recommender systems and online advertising, involves ranking items based on the likelihood of user clicks.
MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction
The large capacity of neural models helps digest such massive amounts of data under the supervised learning paradigm, yet they fail to utilize the substantial data to its full potential, since the 1-bit click signal is not sufficient to guide the model to learn capable representations of features and instances.
ATWM: Defense against adversarial malware based on adversarial training
In order to defend against malware attacks, researchers have proposed many Windows malware detection models based on deep learning.
FGAM:Fast Adversarial Malware Generation Method Based on Gradient Sign
Adversarial attacks are to deceive the deep learning model by generating adversarial samples.
CodeKGC: Code Language Model for Generative Knowledge Graph Construction
However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks.
Compressed Interaction Graph based Framework for Multi-behavior Recommendation
However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data ''as features'' and gradient conflict in multitask learning when treating multi-behavior data ''as labels''.
A holistically 3D-printed flexible millimeter-wave Doppler radar: Towards fully printed high-frequency multilayer flexible hybrid electronics systems
Flexible hybrid electronics (FHE) is an emerging technology enabled through the integration of advanced semiconductor devices and 3D printing technology.
A Survey on User Behavior Modeling in Recommender Systems
Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions.
Universal Detection of Backdoor Attacks via Density-based Clustering and Centroids Analysis
Experiments carried out on several classification tasks and network architectures, considering different types of backdoor attacks (with either clean or corrupted labels), and triggering signals, including both global and local triggering signals, as well as sample-specific and source-specific triggers, reveal that the proposed method is very effective to defend against backdoor attacks in all the cases, always outperforming the state of the art techniques.
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations.
A Survey on Reinforcement Learning in Aviation Applications
Then we survey the landscape of existing RL-based applications in aviation.
Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing behaviors.
IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System
FE-Block module performs fine-grained and early feature interactions to capture the interactive signals between user and item towers explicitly and CIR module leverages a contrastive interaction regularization to further enhance the interactions implicitly.
Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation
More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors.
A temporal chrominance trigger for clean-label backdoor attack against anti-spoof rebroadcast detection
We propose a stealthy clean-label video backdoor attack against Deep Learning (DL)-based models aiming at detecting a particular class of spoofing attacks, namely video rebroadcast attacks.
Improved-Flow Warp Module for Remote Sensing Semantic Segmentation
Second, the offsets help with the low-resolution deep feature up-sampling process to improve the feature accordance, which boosts the accuracy of semantic segmentation.
Cross Pairwise Ranking for Unbiased Item Recommendation
In this work, we develop a new learning paradigm named Cross Pairwise Ranking (CPR) that achieves unbiased recommendation without knowing the exposure mechanism.
Rotated Object Detection via Scale-invariant Mahalanobis Distance in Aerial Images
The eight-parameter (coordinates of box vectors) methods in rotated object detection usually use ln-norm losses (L1 loss, L2 loss, and smooth L1 loss) as loss functions.
Extending the WILDS Benchmark for Unsupervised Adaptation
Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction
With the help of two novel CNN-based multi-interest extractors, self-supervision signals are discovered with full considerations of different interest representations (point-wise and union-wise), interest dependencies (short-range and long-range), and interest correlations (inter-item and intra-item).
An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences
The classification guiding the analysis is based on the amount of control that the attacker has on the training process, and the capability of the defender to verify the integrity of the data used for training, and to monitor the operations of the DNN at training and test time.
Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models
The state-of-the-art deep CTR models with parallel structure (e. g., DCN) learn explicit and implicit feature interactions through independent parallel networks.
Dual Graph enhanced Embedding Neural Network for CTR Prediction
To solve these problems, we propose a novel module named Dual Graph enhanced Embedding, which is compatible with various CTR prediction models to alleviate these two problems.
Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: impact of the spatial resolution
Results show that Faster-RCNN achieved very good plant detection and counting (rRMSE=0. 08) performances when native HR images are used both for training and validation.
Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods
We now release a new version of the Global Wheat Head Detection (GWHD) dataset in 2021, which is bigger, more diverse, and less noisy than the 2020 version.
Global Wheat Challenge 2020: Analysis of the competition design and winning models
Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems.
Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurance
We demonstrate the effectiveness of the two sub-modules in an open-source air traffic simulator with challenging environment settings.
A Master Key Backdoor for Universal Impersonation Attack against DNN-based Face Verification
We introduce a new attack against face verification systems based on Deep Neural Networks (DNN).
Deep Learning for Click-Through Rate Estimation
In this survey, we provide a comprehensive review of deep learning models for CTR estimation tasks.
ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table
Different from the models with dense training data, the training data for CTR models is usually high-dimensional and sparse.
FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data
Our scalable real-time forecasting system FlashP (Flash Prediction) is built based on this idea, with two major challenges to be resolved in this paper: first, we need to figure out how approximate aggregations affect the fitting of forecasting models, and forecasting results; and second, accordingly, what sampling algorithms we should use to obtain these approximate aggregations and how large the samples are.
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.
Ranked #5 on
Image Classification
on iWildCam2020-WILDS
Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification
RGB-Infrared person re-identification (RGB-IR Re-ID) aims to match persons from heterogeneous images captured by visible and thermal cameras, which is of great significance in the surveillance system under poor light conditions.
ICNet: Intra-saliency Correlation Network for Co-Saliency Detection
Intra-saliency and inter-saliency cues have been extensively studied for co-saliency detection (Co-SOD).
Charge density wave and weak Kondo effect in a Dirac semimetal CeSbTe
Using angle-resolved photoemission spectroscopy (ARPES) and low-energy electron diffraction (LEED), together with density-functional theory (DFT) calculation, we report the formation of charge density wave (CDW) and its interplay with the Kondo effect and topological states in CeSbTe.
Strongly Correlated Electrons Materials Science
Commonsense knowledge adversarial dataset that challenges ELECTRA
To investigate machine comprehension models' ability in handling the commonsense knowledge, we created a Question and Answer Dataset with common knowledge of Synonyms (QADS).
GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
We develop a Graph Structure Aware Incremental Learning framework, GraphSAIL, to address the commonly experienced catastrophic forgetting problem that occurs when training a model in an incremental fashion.
A Framework for Recommending Accurate and Diverse ItemsUsing Bayesian Graph Convolutional Neural Networks
Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences.
Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey
In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield.
Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases
Furthermore, we develop two methods, Intersectional Bias Detection (IBD) and Emergent Intersectional Bias Detection (EIBD), to automatically identify the intersectional biases and emergent intersectional biases from static word embeddings in addition to measuring them in contextualized word embeddings.
Automatically Characterizing Targeted Information Operations Through Biases Present in Discourse on Twitter
This paper considers the problem of automatically characterizing overall attitudes and biases that may be associated with emerging information operations via artificial intelligence.
Semantic-Aware Label Placement for Augmented Reality in Street View
Our solution encodes the constraints for placing labels in an optimization problem to obtain the final label layout, and the labels will be placed in appropriate positions to reduce the chances of overlaying important real-world objects in street view AR scenarios.
Active Learning with Point Supervision for Cost-Effective Panicle Detection in Cereal Crops
We show promising results on two publicly available cereal crop datasets - Sorghum and Wheat.
An Adaptive Supervision Framework for Active Learning in Object Detection
Our extensive experiments show that the proposed framework can be used to train good generalizable models with much lesser annotation costs than the state of the art active learning approaches for object detection.
Deep Learning-Based Semantic Segmentation of Microscale Objects
Accurate estimation of the positions and shapes of microscale objects is crucial for automated imaging-guided manipulation using a non-contact technique such as optical tweezers.
Automatic estimation of heading date of paddy rice using deep learning
Accurate estimation of heading date of paddy rice greatly helps the breeders to understand the adaptability of different crop varieties in a given location.
Towards Personalized Management of Type B Aortic Dissection Using STENT: a STandard cta database with annotation of the ENtire aorta and True-false lumen
The database contains 274 CT angiography (CTA) scans from 274 unique TBAD patients and is split into a training set(254 cases including 210 preoperative and 44 postoperative scans ) and a test set(20 cases). Based on STENT, we develop a series of methods including automated TBAD segmentation and automated measurement of TBAD parameters that facilitate personalized and precise management of the disease.
MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank
Then on the selected instance subsets, a multiobjective feature selection algorithm with an adaptive mutation is developed, where good feature subsets are obtained by selecting the features with high ranking accuracy and low redundancy.
A Study of Deep Feature Fusion based Methods for Classifying Multi-lead ECG
An automatic classification method has been studied to effectively detect and recognize Electrocardiogram (ECG).
Detection and Attention: Diagnosing Pulmonary Lung Cancer from CT by Imitating Physicians
This paper proposes a novel and efficient method to build a Computer-Aided Diagnoses (CAD) system for lung nodule detection based on Computed Tomography (CT).
A Note on Community Trees in Networks
We introduce the concept of community trees that summarizes topological structures within a network.
Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification
Topological data analysis (TDA) has emerged as one of the most promising techniques to reconstruct the unknown shapes of high-dimensional spaces from observed data samples.
