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Found 174 papers, 34 papers with code
MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning
Multimodal Federated Learning (MFL) is a distributed approach that enhances the efficiency and quality of multimodal learning, ensuring collaborative work and privacy protection.
Counterfactual Inference for Eliminating Sentiment Bias in Recommender Systems
To the best of our knowledge, this is the first work to employ counterfactual inference on sentiment bias mitigation in RSs.
CachePrune: Neural-Based Attribution Defense Against Indirect Prompt Injection Attacks
In this paper, we propose CachePrune that defends against this attack by identifying and pruning task-triggering neurons from the KV cache of the input prompt context.
A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms
This survey not only reviews the state-of-the-art methods but also provides a critical analysis of the trade-offs among the feature-based, the generative, and the agentic paradigms, outlining key open issues and future research directions.
Direct Advantage Regression: Aligning LLMs with Online AI Reward
Online AI Feedback (OAIF) presents a promising alternative to Reinforcement Learning from Human Feedback (RLHF) by utilizing online AI preference in aligning language models (LLMs).
Generalized Multilingual Text-to-Speech Generation with Language-Aware Style Adaptation
In this work, we propose LanStyleTTS, a non-autoregressive, language-aware style adaptive TTS framework that standardizes phoneme representations and enables fine-grained, phoneme-level style control across languages.
Learning Natural Language Constraints for Safe Reinforcement Learning of Language Agents
Generalizable alignment is a core challenge for deploying Large Language Models (LLMs) safely in real-world NLP applications.
Get the Agents Drunk: Memory Perturbations in Autonomous Agent-based Recommender Systems
DrunkAgent consists of a generation module, a strategy module, and a surrogate module.
Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising
Non-transferable learning (NTL) has been proposed to protect model intellectual property (IP) by creating a "non-transferable barrier" to restrict generalization from authorized to unauthorized domains.
Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models
Recent breakthroughs in Large Language Models (LLMs) have led to the emergence of agentic AI systems that extend beyond the capabilities of standalone models.
Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descent
By explicitly disentangling the optimization of visual understanding from task-specific alignment, MDGD preserves pre-trained visual knowledge while enabling efficient task adaptation.
Diffusion Models for Computational Neuroimaging: A Survey
Computational neuroimaging involves analyzing brain images or signals to provide mechanistic insights and predictive tools for human cognition and behavior.
Policy-Guided Causal State Representation for Offline Reinforcement Learning Recommendation
This policy is guided by a reward function based on the Wasserstein distance, which measures the causal effect of state components on the reward and encourages the preservation of CRCs that directly influence user interests.
From Data to Action: Charting A Data-Driven Path to Combat Antimicrobial Resistance
Antimicrobial-resistant (AMR) microbes are a growing challenge in healthcare, rendering modern medicines ineffective.
Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding
Using STE, we systematically compare implicit and explicit temporal modeling across dimensions such as overall performance, token compression effectiveness, and temporal-specific understanding.
GUI Agents: A Survey
Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction.
LatentSpeech: Latent Diffusion for Text-To-Speech Generation
By using latent embeddings as the intermediate representation, LatentSpeech reduces the target dimension to 5% of what is required for MelSpecs, simplifying the processing for the TTS encoder and vocoder and enabling efficient high-quality speech generation.
Aligner-Guided Training Paradigm: Advancing Text-to-Speech Models with Aligner Guided Duration
Recent advancements in text-to-speech (TTS) systems, such as FastSpeech and StyleSpeech, have significantly improved speech generation quality.
Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen combinations of seen attributes and objects.
Adaptive Rank, Reduced Forgetting: Knowledge Retention in Continual Learning Vision-Language Models with Dynamic Rank-Selective LoRA
We investigate whether the pre-trained knowledge of vision-language models (VLMs), such as CLIP, can be retained or even enhanced during continual learning (CL) while absorbing knowledge from a data stream.
Regularized Multi-LLMs Collaboration for Enhanced Score-based Causal Discovery
As the significance of understanding the cause-and-effect relationships among variables increases in the development of modern systems and algorithms, learning causality from observational data has become a preferred and efficient approach over conducting randomized control trials.
Towards Robust Cross-Domain Recommendation with Joint Identifiability of User Preference
To achieve this, we introduce a hierarchical user preference modeling framework that organizes user representations by the neural network encoder's depth, allowing separate treatment of shallow and deeper subspaces.
LightLLM: A Versatile Large Language Model for Predictive Light Sensing
We propose LightLLM, a model that fine tunes pre-trained large language models (LLMs) for light-based sensing tasks.
OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models
To overcome the reasoning heterogeneity and grounding problems, we leverage on-policy KG exploration and RL to model a KG policy that generates token-level likelihood distributions for LLM-generated chain-of-thought reasoning paths, simulating KG reasoning preference.
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation
To this end, in this paper, we proposed a novel hierarchical preference modeling framework to substantially model the complex low- and high-level preference dynamics for accurate sequential recommendation.
Modeling Temporal Positive and Negative Excitation for Sequential Recommendation
Most of the existing works on sequential recommendation model users' dynamic interest in specific items while overlooking users' static interest revealed by some static attribute information of items, e. g., category, or brand.
Dual Conditional Diffusion Models for Sequential Recommendation
Specifically, we introduce the Dual Conditional Diffusion Transformer (DCDT), which employs a cross-attention mechanism to dynamically integrate explicit signals throughout the diffusion stages, ensuring contextual understanding and minimizing the influence of irrelevant patterns.
Mining Your Own Secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models
Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images.
Flexiffusion: Segment-wise Neural Architecture Search for Flexible Denoising Schedule
The factors for Stable Diffusion V1. 5 and the SOTA are $5. 1\times$ and $2. 0\times$.
Federated Large Language Models: Current Progress and Future Directions
Large language models are rapidly gaining popularity and have been widely adopted in real-world applications.
Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis
Functional magnetic resonance imaging (fMRI) is an emerging neuroimaging modality that is commonly modeled as networks of Regions of Interest (ROIs) and their connections, named functional connectivity, for understanding the brain functions and mental disorders.
Visual Prompting in Multimodal Large Language Models: A Survey
This paper presents the first comprehensive survey on visual prompting methods in MLLMs, focusing on visual prompting, prompt generation, compositional reasoning, and prompt learning.
Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs
Large Language Models (LLMs) have demonstrated remarkable efficiency in tackling various tasks based on human instructions, but studies reveal that they often struggle with tasks requiring reasoning, such as math or physics.
Causality-Aware Transformer Networks for Robotic Navigation
We address these constraints by initially exploring the unique differences between Navigation tasks and other sequential data tasks through the lens of Causality, presenting a causal framework to elucidate the inadequacies of conventional sequential methods for Navigation.
Independence Constrained Disentangled Representation Learning from Epistemological Perspective
Disentangled Representation Learning aims to improve the explainability of deep learning methods by training a data encoder that identifies semantically meaningful latent variables in the data generation process.
Graph and Sequential Neural Networks in Session-based Recommendation: A Survey
Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem.
StyleSpeech: Parameter-efficient Fine Tuning for Pre-trained Controllable Text-to-Speech
This paper introduces StyleSpeech, a novel Text-to-Speech~(TTS) system that enhances the naturalness and accuracy of synthesized speech.
CoMMIT: Coordinated Instruction Tuning for Multimodal Large Language Models
Instruction tuning in multimodal large language models (MLLMs) aims to smoothly integrate a backbone LLM with a pre-trained feature encoder for downstream tasks.
PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning
In the training process, only the generated trajectories and their rewards obtained based on personal discriminators are shared between the server and devices, whose privacy is further preserved by our proposed perturbation mechanisms with theoretical proof to satisfy differential privacy.
On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender Systems
To tackle this problem, we introduce an innovative causal approach for decomposing the state and extracting \textbf{C}ausal-\textbf{I}n\textbf{D}ispensable \textbf{S}tate Representations (CIDS) in RLRS.
AI Safety in Generative AI Large Language Models: A Survey
In this survey, we explore the background and motivation for the identified harms and risks in the context of LLMs being generative language models; our survey differentiates by emphasising the need for unified theories of the distinct safety challenges in the research development and applications of LLMs.
Self-Distilled Disentangled Learning for Counterfactual Prediction
The advancements in disentangled representation learning significantly enhance the accuracy of counterfactual predictions by granting precise control over instrumental variables, confounders, and adjustable variables.
Maximum-Entropy Regularized Decision Transformer with Reward Relabelling for Dynamic Recommendation
In response, we introduce a novel methodology named Max-Entropy enhanced Decision Transformer with Reward Relabeling for Offline RLRS (EDT4Rec).
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
While Positive-Unlabeled (PU) learning is vital in many real-world scenarios, its application to graph data still remains under-explored.
Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning
Recent studies indicate that large multimodal models (LMMs) potentially act as general-purpose assistants and are highly robust against different distributions.
Modeling Pedestrian Intrinsic Uncertainty for Multimodal Stochastic Trajectory Prediction via Energy Plan Denoising
In response, we propose the Energy Plan Denoising (EPD) model for stochastic trajectory prediction.
Multi-agent Traffic Prediction via Denoised Endpoint Distribution
The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation.
Joint Identifiability of Cross-Domain Recommendation via Hierarchical Subspace Disentanglement
Such a failure may overlook the conditionality between two domains and how it contributes to latent factor disentanglement, leading to negative transfer when domains are weakly correlated.
Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models
However, it was observed by previous works that retrieval is not always helpful, especially when the LLM is already knowledgeable on the query to answer.
CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models
Cooperating with the diverse range of existing prompting methods, CLAP can surpass the predominant deterministic finetuning approaches for CL with CLIP.
Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning
SEMA automatically decides to reuse or add adapter modules on demand in CL, depending on whether significant distribution shift that cannot be handled is detected at different representation levels.
Uncertainty-aware Distributional Offline Reinforcement Learning
Offline reinforcement learning (RL) presents distinct challenges as it relies solely on observational data.
Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems
Reinforcement Learning-based Recommender Systems (RLRS) have shown promise across a spectrum of applications, from e-commerce platforms to streaming services.
Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning
Our framework evaluates the specificity of attributes by considering the diversity of objects they apply to and their related context.
Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models
This method leverages knowledge compressed in LLMs and knowledge LLMs extracted from scientific publication database as well as experiment data about factors of interest to achieve this goal.
MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts via Automating Deep Neural Network Porting for Mobile Deployment
In this paper, we propose MatchNAS, a novel scheme for porting DNNs to mobile devices.
Foundation Models for Recommender Systems: A Survey and New Perspectives
Recently, Foundation Models (FMs), with their extensive knowledge bases and complex architectures, have offered unique opportunities within the realm of recommender systems (RSs).
Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model
Recently, Denoising Diffusion Probabilistic Models (DDPMs) have attained leading performances across a diverse range of generative tasks.
Review-Incorporated Model-Agnostic Profile Injection Attacks on Recommender Systems
Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks.
Learning with Mixture of Prototypes for Out-of-Distribution Detection
To tackle these issues, we propose PrototypicAl Learning with a Mixture of prototypes (PALM) which models each class with multiple prototypes to capture the sample diversities, and learns more faithful and compact samples embeddings to enhance OOD detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization
Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features.
SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks
Besides, the node representation of normal entities can be perturbed easily by the noise relationships introduced by anomalous nodes.
Attention-aware Social Graph Transformer Networks for Stochastic Trajectory Prediction
We propose Attention-aware Social Graph Transformer Networks for multi-modal trajectory prediction.
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
We further investigate its adaptability to controlled data perturbations and examine the efficacy of in-context learning as a tool to enhance its adaptation.
Mask Propagation for Efficient Video Semantic Segmentation
By reusing predictions from key frames, we circumvent the need to process a large volume of video frames individually with resource-intensive segmentors, alleviating temporal redundancy and significantly reducing computational costs.
When Large Language Models Meet Citation: A Survey
Such information could be incorporated into LLMs pre-training and improve the text representation in LLMs.
Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation
Next, we aim to build distributional implicit matchings between the domain-level preferences of two domains.
Reinforcement Learning for Generative AI: A Survey
Deep Generative AI has been a long-standing essential topic in the machine learning community, which can impact a number of application areas like text generation and computer vision.
On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems
Offline reinforcement learning empowers agents to glean insights from offline datasets and deploy learned policies in online settings.
Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition
Subsequently, a frame features learning module and a two-stream multi-level feature aggregation module extract global and partial features from the sampled frames, effectively representing the local-region spatial information, appearance information, and motion information related to the interactions.
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition
Then the knowledge distillation method is applied to transfer the learned representation from the teacher model to a simpler DMFT student model, which consists of a lite version of the multi-modal spatial-temporal transformer module, to produce the results.
Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning
Reinforcement learning-based recommender systems have recently gained popularity.
Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation
This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems.
Limits of Predictability in Top-N Recommendation
This work investigates such predictability by studying the degree of regularity from a specific set of user behavior data.
Adversarially Contrastive Estimation of Conditional Neural Processes
Conditional Neural Processes~(CNPs) formulate distributions over functions and generate function observations with exact conditional likelihoods.
Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion
Tremendous efforts have been put forth on predicting pedestrian trajectory with generative models to accommodate uncertainty and multi-modality in human behaviors.
Guided Image-to-Image Translation by Discriminator-Generator Communication
The goal of Image-to-image (I2I) translation is to transfer an image from a source domain to a target domain, which has recently drawn increasing attention.
Generative Adversarial Network
Image-to-Image Translation
+1
Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning
Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects.
Emerging Synergies in Causality and Deep Generative Models: A Survey
In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance.
HTML: Hybrid Temporal-scale Multimodal Learning Framework for Referring Video Object Segmentation
To tackle this problem, we propose a concise Hybrid Temporal-scale Multimodal Learning (HTML) framework, which can effectively align lingual and visual features to discover core object semantics in the video, by learning multimodal interaction hierarchically from different temporal scales.
Ranked #8 on
Referring Video Object Segmentation
on Ref-DAVIS17
Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning
We design Semantic Attention (SA) and generative Knowledge Disentanglement (KD) to learn the dependence of feasibility and contextuality, respectively.
A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications
Transportation is the backbone of the economy and urban development.
Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation
It is more challenging to balance the exploration and exploitation in DRL RS where RS agent need to deeply explore the informative trajectories and exploit them efficiently in the context of recommender systems.
The Neural Process Family: Survey, Applications and Perspectives
We shed light on their potential to bring several recent advances in other deep learning domains under one umbrella.
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey
A key challenge is that while the application of deep models often incurs substantial memory and computational costs, edge devices typically offer only very limited storage and computational capabilities that may vary substantially across devices.
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems
Recognizing that confounders may be elusive, we propose a contrastive self-supervised learning to minimize exposure bias, employing inverse propensity scores and expanding the positive sample set.
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects
We regularly consider answering counterfactual questions in practice, such as "Would people with diabetes take a turn for the better had they choose another medication?".
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation
Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems.
IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation
In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests.
See What You See: Self-supervised Cross-modal Retrieval of Visual Stimuli from Brain Activity
Recent studies demonstrate the use of a two-stage supervised framework to generate images that depict human perception to visual stimuli from EEG, referring to EEG-visual reconstruction.
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System
This curriculum-driven noise introduction aids the memory-driven decoder by supplying more diverse and representative training data, enhancing the decoder's ability to model seasonal tendencies and dependencies in the time-series data.
A Survey on Participant Selection for Federated Learning in Mobile Networks
The main challenges of FL is that end devices usually possess various computation and communication capabilities and their training data are not independent and identically distributed (non-IID).
Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review
The intelligent dialogue system, aiming at communicating with humans harmoniously with natural language, is brilliant for promoting the advancement of human-machine interaction in the era of artificial intelligence.
Unsupervised Knowledge Adaptation for Passenger Demand Forecasting
These multimodal forecasting models can improve accuracy but be less practical when different parts of multimodal datasets are owned by different institutions who cannot directly share data among them.
Towards Explanation for Unsupervised Graph-Level Representation Learning
Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide the model's decision?}"
Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus
With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses.
Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis
The side-aware unsupervised domain adaptation module adapts the class-irrelevant information as domain variance to a new dataset and excludes the variance to obtain the class-distill features for the new dataset classification.
Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition
Learning spatial-temporal relation among multiple actors is crucial for group activity recognition.
Model-agnostic Counterfactual Synthesis Policy for Interactive Recommendation
Recent advances have convinced that the ability of reinforcement learning to handle the dynamic process can be effectively applied in the interactive recommendation.
Contrastive Graph Learning for Population-based fMRI Classification
Nonetheless, existing contrastive methods generate resemblant pairs only on pixel-level features of 3D medical images, while the functional connectivity that reveals critical cognitive information is under-explored.
Contrastive Conditional Neural Processes
Conditional Neural Processes~(CNPs) bridge neural networks with probabilistic inference to approximate functions of Stochastic Processes under meta-learning settings.
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning
We further propose a differentiable dataset-level balance and update the weights in a linear annealing schedule to simulate network pruning and thus obtain the optimal structure for BSNet with dataset-level balance achieved.
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems
Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods.
Rethink, Revisit, Revise: A Spiral Reinforced Self-Revised Network for Zero-Shot Learning
Our framework outperforms state-of-the-art algorithms on four benchmark datasets in both zero-shot and generalized zero-shot settings, which demonstrates the effectiveness of spiral learning in learning generalizable and complex correlations.
An Entropy-guided Reinforced Partial Convolutional Network for Zero-Shot Learning
Zero-Shot Learning (ZSL) aims to transfer learned knowledge from observed classes to unseen classes via semantic correlations.
Cycle-Balanced Representation Learning For Counterfactual Inference
With the widespread accumulation of observational data, researchers obtain a new direction to learn counterfactual effects in many domains (e. g., health care and computational advertising) without Randomized Controlled Trials(RCTs).
Locality-Sensitive Experience Replay for Online Recommendation
Existing studies encourage the agent to learn from past experience via experience replay (ER).
AskMe: Joint Individual-level and Community-level Behavior Interaction for Question Recommendation
On the one hand, we model the rich correlations between the users' diverse behaviors (e. g., answer, follow, vote) to obtain the individual-level behavior interaction.
A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions
In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems.
Global Convolutional Neural Processes
Latent neural process, a member of NPF, is believed to be capable of modelling the uncertainty on certain points (local uncertainty) as well as the general function priors (global uncertainties).
Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions
Therefore, comprehensive surveys on this topic are essential to summarise challenges and solutions to foster future research.
Three Birds with One Stone: Multi-Task Temporal Action Detection via Recycling Temporal Annotations
Temporal action detection on unconstrained videos has seen significant research progress in recent years.
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference
Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e. g., in reinforcement learning based recommender systems.
Attribute-Modulated Generative Meta Learning for Zero-Shot Classification
The promising strategies for ZSL are to synthesize visual features of unseen classes conditioned on semantic side information and to incorporate meta-learning to eliminate the model's inherent bias towards seen classes.
Task Aligned Generative Meta-learning for Zero-shot Learning
Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen).
Generative Adversarial U-Net for Domain-free Medical Image Augmentation
The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing.
Meta Gradient Boosting Neural Networks
A key challenge for meta-optimization based approaches is to determine whether an initialization condition can be generalized to tasks with diverse distributions to accelerate learning.
Generative Inverse Deep Reinforcement Learning for Online Recommendation
Deep reinforcement learning uses a reward function to learn user's interest and to control the learning process.
Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network
Accurate demand forecasting of different public transport modes(e. g., buses and light rails) is essential for public service operation. However, the development level of various modes often varies sig-nificantly, which makes it hard to predict the demand of the modeswith insufficient knowledge and sparse station distribution (i. e., station-sparse mode).
TRec: Sequential Recommender Based On Latent Item Trend Information
Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios.
Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding
We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers and to predict the top-$N$ purchase destinations of a consumer.
Spectrum-Guided Adversarial Disparity Learning
It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class.
Recommender Systems for the Internet of Things: A Survey
Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT).
MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users.
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.
Ranked #2 on
Weather Forecasting
on SD
Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images
Diagnosis from chest CT images is a promising direction.
NP-PROV: Neural Processes with Position-Relevant-Only Variances
Neural Processes (NPs) families encode distributions over functions to a latent representation, given context data, and decode posterior mean and variance at unknown locations.
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems
Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods.
Agglomerative Neural Networks for Multi-view Clustering
Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews.
Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems
In recent years, the emerging topics of recommender systems that take advantage of natural language processing techniques have attracted much attention, and one of their applications is the Conversational Recommender System (CRS).
Shape-Oriented Convolution Neural Network for Point Cloud Analysis
Point cloud is a principal data structure adopted for 3D geometric information encoding.
Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction
Most current studies on survey analysis and risk tolerance modelling lack professional knowledge and domain-specific models.
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation
Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy.
Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images
The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy.
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.
A Multi-view CNN-based Acoustic Classification System for Automatic Animal Species Identification
In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN).
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition.
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction
Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision.
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.
The Future of Misinformation Detection: New Perspectives and Trends
We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection.
Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals
In light of this, we propose a novel multi-task generative adversarial network to convert the individual's EEG signals evoked by geometrical shapes to the original geometry.
Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning
Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e. g., computer version).
Holographic Factorization Machines for Recommendation
Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and recommendation tasks.
Quaternion Collaborative Filtering for Recommendation
All in all, we conduct extensive experiments on six real-world datasets, demonstrating the effectiveness of Quaternion algebra in recommender systems.
DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns
Cross-domain recommendation has long been one of the major topics in recommender systems.
DeepRec: An Open-source Toolkit for Deep Learning based Recommendation
In this toolkit, we have implemented a number of deep learning based recommendation algorithms using Python and the widely used deep learning package - Tensorflow.
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services.
Multi-agent Attentional Activity Recognition
And the multiple agents in the proposed model represent activities with collective motions across body parts by independently selecting modalities associated with single motions.
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph
The resulting graph of prototypes can be continually re-used and updated for new tasks and classes.
A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers
Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices.
Adversarial Variational Embedding for Robust Semi-supervised Learning
However, the latent code learned by the traditional VAE is not exclusive (repeatable) for a specific input sample, which prevents it from excellent classification performance.
Quaternion Knowledge Graph Embeddings
In this work, we move beyond the traditional complex-valued representations, introducing more expressive hypercomplex representations to model entities and relations for knowledge graph embeddings.
Ranked #3 on
Link Prediction
on FB15k
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing.
Learning to Recommend with Multiple Cascading Behaviors
To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task.
Next Item Recommendation with Self-Attention
In this paper, we propose a novel sequence-aware recommendation model.
Adversarial Collaborative Auto-encoder for Top-N Recommendation
In this work, to address the above issue, we propose a general adversial training framework for neural network-based recommendation models, which improves both the model robustness and the overall performance.
GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest
We develop a gradient boost module and embed it into the proposed convolutional autoencoder with neural decision forest to improve the performance.
Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling
Multimodal features play a key role in wearable sensor-based human activity recognition (HAR).
Opinion Fraud Detection via Neural Autoencoder Decision Forest
Online reviews play an important role in influencing buyers' daily purchase decisions.
NeuRec: On Nonlinear Transformation for Personalized Ranking
Modeling user-item interaction patterns is an important task for personalized recommendations.
Multi-modality Sensor Data Classification with Selective Attention
Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment.
Metric Factorization: Recommendation beyond Matrix Factorization
In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations.
Adversarially Regularized Graph Autoencoder for Graph Embedding
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.
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Fullie and Wiselie: A Dual-Stream Recurrent Convolutional Attention Model for Activity Recognition
Multimodal features play a key role in wearable sensor based Human Activity Recognition (HAR).
MindID: Person Identification from Brain Waves through Attention-based Recurrent Neural Network
The proposed approach is evaluated over 3 datasets (two local and one public).
Human-Computer Interaction
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots.
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis
In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition.
Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface
Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions.
Human-Computer Interaction Neurons and Cognition
Deep Learning based Recommender System: A Survey and New Perspectives
This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems.
DeepKey: An EEG and Gait Based Dual-Authentication System
Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics.
Uncovering Locally Discriminative Structure for Feature Analysis
We propose a method that utilizes both the manifold structure of data and local discriminant information.
Unsupervised Feature Analysis with Class Margin Optimization
In this paper, we propose an unsupervised feature selection method seeking a feature coefficient matrix to select the most distinctive features.
