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Found 39 papers, 13 papers with code
$\textit{LinkPrompt}$: Natural and Universal Adversarial Attacks on Prompt-based Language Models
Prompt-based learning is a new language model training paradigm that adapts the Pre-trained Language Models (PLMs) to downstream tasks, which revitalizes the performance benchmarks across various natural language processing (NLP) tasks.
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational complexity involved in aggregating billions of neighbors.
Dancing with Still Images: Video Distillation via Static-Dynamic Disentanglement
It first distills the videos into still images as static memory and then compensates the dynamic and motion information with a learnable dynamic memory block.
EgoPCA: A New Framework for Egocentric Hand-Object Interaction Understanding
With the surge in attention to Egocentric Hand-Object Interaction (Ego-HOI), large-scale datasets such as Ego4D and EPIC-KITCHENS have been proposed.
Towards Generic and Controllable Attacks Against Object Detection
To this end, we propose a generic white-box attack, LGP (local perturbations with adaptively global attacks), to blind mainstream object detectors with controllable perturbations.
Distill Gold from Massive Ores: Efficient Dataset Distillation via Critical Samples Selection
Our method consistently enhances the distillation algorithms, even on much larger-scale and more heterogeneous datasets, e. g. ImageNet-1K and Kinetics-400.
Multi-factor Sequential Re-ranking with Perception-Aware Diversification
Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications.
Multi-channel Integrated Recommendation with Exposure Constraints
Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms.
Feature Extraction Matters More: Universal Deepfake Disruption through Attacking Ensemble Feature Extractors
Adversarial example is a rising way of protecting facial privacy security from deepfake modification.
Beyond Object Recognition: A New Benchmark towards Object Concept Learning
To support OCL, we build a densely annotated knowledge base including extensive labels for three levels of object concept (category, attribute, affordance), and the causal relations of three levels.
GPatch: Patching Graph Neural Networks for Cold-Start Recommendations
State-of-the-art solutions rely on training hybrid models for both cold-start and existing users/items, based on the auxiliary information.
Flattened Graph Convolutional Networks For Recommendation
Third, we propose a layer ensemble technique which improves the expressiveness of the learned representations by assembling the layer-wise neighborhood representations at the final layer.
Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction
The results verify that the proposed model outperforms existing CTR models considerably, in terms of both CTR prediction performance and online cost-efficiency.
Model Order Estimation in the Presence of multipath Interference using Residual Convolutional Neural Networks
Model order estimation (MOE) is often a pre-requisite for Direction of Arrival (DoA) estimation.
Constructing Balance from Imbalance for Long-tailed Image Recognition
Previous methods tackle with data imbalance from the viewpoints of data distribution, feature space, and model design, etc.
Generative Adversarial Framework for Cold-Start Item Recommendation
Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions.
Intelligent Request Strategy Design in Recommender System
RSs typically put a large number of items into one page to reduce excessive resource consumption from numerous paging requests, which, however, would diminish the RSs' ability to timely renew the recommendations according to users' real-time interest and lead to a poor user experience.
Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling
In Meituan, one of the largest e-commerce platform in China, an item is typically displayed with its image and whether a user clicks the item or not is usually influenced by its image, which implies that user's image behaviors are helpful for understanding user's visual preference and improving the accuracy of CTR prediction.
Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation
The experimental results verify that our proposed NEGCN framework can significantly enhance the performance for various typical GCN models on both node classification and recommendation tasks.
HAKE: A Knowledge Engine Foundation for Human Activity Understanding
Human activity understanding is of widespread interest in artificial intelligence and spans diverse applications like health care and behavior analysis.
Learning Single/Multi-Attribute of Object with Symmetry and Group
To model the compositional nature of these concepts, it is a good choice to learn them as transformations, e. g., coupling and decoupling.
Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms
In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction.
Non-Recursive Graph Convolutional Networks
In this way, each node can be directly represented by concatenating the information extracted independently from each hop of its neighbors thereby avoiding the recursive neighborhood expansion across layers.
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey
Empowered by big data and machine learning, next-generation data-driven communication systems will be intelligent with the characteristics of expressiveness, scalability, interpretability, and especially uncertainty modeling, which can confidently involve diversified latent demands and personalized services in the foreseeable future.
Bayesian Inference Forgetting
This paper proposes a {\it Bayesian inference forgetting} (BIF) framework to realize the right to be forgotten in Bayesian inference.
Predicting Bit Error Rate from Meta Information using Random Forests
With the increasing power of machine learning-based reasoning, the use of meta-information (e. g., digital signal modulation parameters, channel conditions, etc.)
Single-Layer Graph Convolutional Networks For Recommendation
The results verify that the proposed model outperforms existing GCN models considerably and yields up to a few orders of magnitude speedup in training, in terms of the recommendation performance.
PaStaNet: Toward Human Activity Knowledge Engine
In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics.
Ranked #3 on
Human-Object Interaction Detection
on HICO
Symmetry and Group in Attribute-Object Compositions
To model the compositional nature of these general concepts, it is a good choice to learn them through transformations, such as coupling and decoupling.
Ranked #1 on
Compositional Zero-Shot Learning
on MIT-States
(Top-1 accuracy % metric)
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing
In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods.
Scalable Learning Paradigms for Data-Driven Wireless Communication
The marriage of wireless big data and machine learning techniques revolutionizes the wireless system by the data-driven philosophy.
Label-Aware Graph Convolutional Networks
In this paper, we consider the problem of node classification and propose the Label-Aware Graph Convolutional Network (LAGCN) framework which can directly identify valuable neighbors to enhance the performance of existing GCN models.
Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT
The recent success of single-agent reinforcement learning (RL) in Internet of things (IoT) systems motivates the study of multi-agent reinforcement learning (MARL), which is more challenging but more useful in large-scale IoT.
Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach
Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way.
HAKE: Human Activity Knowledge Engine
To address these and promote the activity understanding, we build a large-scale Human Activity Knowledge Engine (HAKE) based on the human body part states.
Ranked #2 on
Human-Object Interaction Detection
on HICO
(using extra training data)
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification
First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions.
Efforts estimation of doctors annotating medical image
In this paper, we propose a new criterion to evaluate efforts of doctors annotating medical image.
A Multi-channel Network with Image Retrieval for Accurate Brain Tissue Segmentation
Magnetic Resonance Imaging (MRI) is widely used in the pathological and functional studies of the brain, such as epilepsy, tumor diagnosis, etc.
Multi-task Layout Analysis for Historical Handwritten Documents Using Fully Convolutional Networks
Layout analysis is a fundamental process in document image analysis and understanding.
