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Found 174 papers, 81 papers with code
Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference
In this work, we conduct a pioneering study of the English variety use of African American English (AAE) in NLI task.
Retrieval-Augmented Generation with Graphs (GraphRAG)
However, unlike conventional RAG, where the retriever, generator, and external data sources can be uniformly designed in the neural-embedding space, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains.
An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling
Unlike the standard learning-to-optimize framework that requires optimization solutions generated by solvers, our unsupervised method adjusts the network weights directly from the evaluation of the primal-dual gap.
One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs
Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate network patterns, achieving success across different domains.
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective
We conduct a comprehensive analysis of LLM representation behaviors and demonstrate the significance of using representations in knowledge checking.
Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data
However, the large size and high computation demands of LLMs limit their practicality in many applications, especially when further fine-tuning is required.
Exploring the Alignment Landscape: LLMs and Geometric Deep Models in Protein Representation
In this study, we explore the alignment of multimodal representations between LLMs and Geometric Deep Models (GDMs) in the protein domain.
A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration
Few-shot Chain-of-Thought (CoT) prompting has demonstrated strong performance in improving the reasoning capabilities of large language models (LLMs).
Make LLMs better zero-shot reasoners: Structure-orientated autonomous reasoning
In this paper, we address this limitation by introducing a novel structure-oriented analysis method to help LLMs better understand the question and guide the problem-solving process of LLMs.
Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Models
Existing methods, such as sample-level Membership Inference Attacks (MIA) and distribution-based dataset inference, distinguish member data (data used for training) and non-member data by leveraging the common observation that models tend to memorize and show greater confidence in member data.
Towards the Effect of Examples on In-Context Learning: A Theoretical Case Study
To better understand how ICL integrates the examples with the knowledge learned by the LLM during pre-training (i. e., pre-training knowledge) and how the examples impact ICL, this paper conducts a theoretical study in binary classification tasks.
How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension
The benchmark encompasses both synthetic and real datasets, and a variety of models, with a total of 11 tasks and 7 models.
A LLM-Powered Automatic Grading Framework with Human-Level Guidelines Optimization
Open-ended short-answer questions (SAGs) have been widely recognized as a powerful tool for providing deeper insights into learners' responses in the context of learning analytics (LA).
Sub-graph Based Diffusion Model for Link Prediction
In particular, we treat link prediction between a pair of nodes as a conditional likelihood estimation of its enclosing sub-graph.
Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Path
In this work, we focus on the graph reasoning ability of LLMs.
SA-GDA: Spectral Augmentation for Graph Domain Adaptation
Following the observation, we align the category feature space of different domains in the spectral domain instead of aligning the whole features space, and we theoretical proof the stability of proposed \method{}.
Exploring Query Understanding for Amazon Product Search
Next, we delve into how the query understanding system contributes to understanding the performance of a ranking model.
Intrinsic Self-correction for Enhanced Morality: An Analysis of Internal Mechanisms and the Superficial Hypothesis
Through empirical investigation with tasks of language generation and multi-choice question answering, we conclude:(i) LLMs exhibit good performance across both tasks, and self-correction instructions are particularly beneficial when the correct answer is already top-ranked; (ii) The morality levels in intermediate hidden states are strong indicators as to whether one instruction would be more effective than another; (iii) Based on our analysis of intermediate hidden states and task case studies of self-correction behaviors, we are first to propose the hypothesis that intrinsic moral self-correction is in fact superficial.
Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness
To address this gap, our work aims to explore the potential of LLMs in the context of adversarial attacks on graphs.
Six-CD: Benchmarking Concept Removals for Benign Text-to-image Diffusion Models
To address these gaps, we propose to benchmark the concept removal methods by introducing a new dataset, Six-CD, along with a novel evaluation metric.
A Pure Transformer Pretraining Framework on Text-attributed Graphs
Pretraining plays a pivotal role in acquiring generalized knowledge from large-scale data, achieving remarkable successes as evidenced by large models in CV and NLP.
Knowledge Tagging System on Math Questions via LLMs with Flexible Demonstration Retriever
Knowledge tagging for questions plays a crucial role in contemporary intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course content organization.
Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis
Large language models (LLMs) are susceptible to a type of attack known as jailbreaking, which misleads LLMs to output harmful contents.
Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
First, the absence of a comprehensive benchmark with unified problem settings hinders a clear understanding of the comparative effectiveness and practical value of different text-space GFMs.
Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion
Knowledge Graph Completion (KGC) attempts to predict missing facts in a Knowledge Graph (KG).
Understanding the Generalizability of Link Predictors Under Distribution Shifts on Graphs
To tackle the distribution shift problem, recent work focuses on creating datasets that feature distribution shifts and designing generalization methods that perform well on the new data.
Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach
Traditionally, GNNs employ a uniform global filter, typically a low-pass filter for homophilic graphs and a high-pass filter for heterophilic graphs.
PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming
In this work, we propose an FOM-unrolled neural network (NN) called PDHG-Net, and propose a two-stage L2O method to solve large-scale LP problems.
Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models
To achieve effective data scaling, we aim to develop a general model that is able to capture diverse data patterns of graphs and can be utilized to adaptively help the downstream tasks.
On the Intrinsic Self-Correction Capability of LLMs: Uncertainty and Latent Concept
Our findings are verified in: (1) the scenario of multi-round question answering, by comprehensively demonstrating that intrinsic self-correction can progressively introduce performance gains through iterative interactions, ultimately converging to stable performance; and (2) the context of intrinsic self-correction for enhanced morality, in which we provide empirical evidence that iteratively applying instructions reduces model uncertainty towards convergence, which then leads to convergence of both the calibration error and self-correction performance, ultimately resulting in a stable state of intrinsic self-correction.
Large Language Models for Education: A Survey and Outlook
The advent of Large Language Models (LLMs) has brought in a new era of possibilities in the realm of education.
Automate Knowledge Concept Tagging on Math Questions with LLMs
Knowledge concept tagging for questions plays a crucial role in contemporary intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course content organization.
Content Knowledge Identification with Multi-Agent Large Language Models (LLMs)
Teachers' mathematical content knowledge (CK) is of vital importance and need in teacher professional development (PD) programs.
Unveiling and Mitigating Memorization in Text-to-image Diffusion Models through Cross Attention
Recent advancements in text-to-image diffusion models have demonstrated their remarkable capability to generate high-quality images from textual prompts.
The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)
In this work, we conduct extensive empirical studies with novel attack methods, which demonstrate the vulnerability of RAG systems on leaking the private retrieval database.
Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation
To integrate text information, various methods have been introduced, mostly following a naive fusion framework.
Rethinking Large Language Model Architectures for Sequential Recommendations
This design stems from our empirical observation that beam search decoding is ultimately unnecessary for sequential recommendations.
Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective
These insights have empowered us to develop a novel GNN backbone model, DGAT, designed to harness the robust properties of both graph self-attention mechanism and the decoupled architecture.
Mixture of Link Predictors on Graphs
Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning.
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
Second, how does A-X dependence affect GNNs?
Masked Graph Autoencoder with Non-discrete Bandwidths
Inspired by these understandings, we explore non-discrete edge masks, which are sampled from a continuous and dispersive probability distribution instead of the discrete Bernoulli distribution.
Copyright Protection in Generative AI: A Technical Perspective
We examine from two distinct viewpoints: the copyrights pertaining to the source data held by the data owners and those of the generative models maintained by the model builders.
Position: Graph Foundation Models are Already Here
Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph domain, aiming to develop graph models trained on extensive and diverse data to enhance their applicability across various tasks and domains.
A Data Generation Perspective to the Mechanism of In-Context Learning
In-Context Learning (ICL) empowers Large Language Models (LLMs) with the capacity to learn in context, achieving downstream generalization without gradient updates but with a few in-context examples.
Towards Neural Scaling Laws on Graphs
Yet, the neural scaling laws on graphs, i. e., how the performance of deep graph models changes with model and dataset sizes, have not been systematically investigated, casting doubts on the feasibility of achieving large graph models.
Bringing Generative AI to Adaptive Learning in Education
The recent surge in generative AI technologies, such as large language models and diffusion models, has boosted the development of AI applications in various domains, including science, finance, and education.
Superiority of Multi-Head Attention in In-Context Linear Regression
We present a theoretical analysis of the performance of transformer with softmax attention in in-context learning with linear regression tasks.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
Distance-Based Propagation for Efficient Knowledge Graph Reasoning
A new class of methods have been proposed to tackle this problem by aggregating path information.
Embedding in Recommender Systems: A Survey
This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
LPFormer: An Adaptive Graph Transformer for Link Prediction
These methods perform predictions by using the output of an MPNN in conjunction with a "pairwise encoding" that captures the relationship between nodes in the candidate link.
Exploring Memorization in Fine-tuned Language Models
In this work, we conduct the first comprehensive analysis to explore language models' (LMs) memorization during fine-tuning across tasks.
Label-free Node Classification on Graphs with Large Language Models (LLMS)
In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN.
FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models
FT-Shield addresses copyright protection challenges by designing new watermark generation and detection strategies.
On the Generalization of Training-based ChatGPT Detection Methods
ChatGPT is one of the most popular language models which achieve amazing performance on various natural language tasks.
Revisiting Link Prediction: A Data Perspective
We recognize three fundamental factors critical to link prediction: local structural proximity, global structural proximity, and feature proximity.
Graph-level Representation Learning with Joint-Embedding Predictive Architectures
They aim to learn an energy-based model by predicting the latent representation of a target signal y from the latent representation of a context signal x. JEPAs bypass the need for negative and positive samples, traditionally required by contrastive learning while avoiding the overfitting issues associated with generative pretraining.
Ranked #11 on
Graph Classification
on REDDIT-B
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding.
Recommender Systems in the Era of Large Language Models (LLMs)
As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems.
Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
Furthermore, new and diverse datasets have also been created to better evaluate the effectiveness of these new models.
Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective
In this work, we propose a novel LLM-based framework (MolReGPT) for molecule-caption translation, where an In-Context Few-Shot Molecule Learning paradigm is introduced to empower molecule discovery with LLMs like ChatGPT to perform their in-context learning capability without domain-specific pre-training and fine-tuning.
Ranked #5 on
Text-based de novo Molecule Generation
on ChEBI-20
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and certain heterophilic graphs.
DiffusionShield: A Watermark for Copyright Protection against Generative Diffusion Models
By detecting the watermark from generated images, copyright infringement can be exposed with evidence.
Single-Cell Multimodal Prediction via Transformers
The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding of cellular states and dynamics.
Toward Degree Bias in Embedding-Based Knowledge Graph Completion
It aims to predict unseen edges by learning representations for all the entities and relations in a KG.
Multimodal Recommender Systems: A Survey
In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views.
Generative Diffusion Models on Graphs: Methods and Applications
Given the great success of diffusion models in image generation, increasing efforts have been made to leverage these techniques to advance graph generation in recent years.
Deep Learning in Single-Cell Analysis
Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages.
Whole Page Unbiased Learning to Rank
To address the above challenges, we propose a Bias Agnostic whole-page unbiased Learning to rank algorithm, named BAL, to automatically find the user behavior model with causal discovery and mitigate the biases induced by multiple SERP features with no specific design.
Transferable Unlearnable Examples
The unlearnable strategies have been introduced to prevent third parties from training on the data without permission.
Towards Fair Classification against Poisoning Attacks
We demonstrate that the fairly trained classifiers can be greatly vulnerable to such poisoning attacks, with much worse accuracy & fairness trade-off, even when we apply some of the most effective defenses (originally proposed to defend traditional classification tasks).
Probabilistic Categorical Adversarial Attack & Adversarial Training
To tackle this problem, we propose Probabilistic Categorical Adversarial Attack (PCAA), which transfers the discrete optimization problem to a continuous problem that can be solved efficiently by Projected Gradient Descent.
Test-Time Training for Graph Neural Networks
To bridge such gap, in this work we introduce the first test-time training framework for GNNs to enhance the model generalization capacity for the graph classification task.
Empowering Graph Representation Learning with Test-Time Graph Transformation
In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTrans which adapts and refines graph data at test time to achieve better performance.
Learning Representations for Hyper-Relational Knowledge Graphs
It motivates us to design a framework that utilizes multiple aggregators to learn representations for hyper-relational facts: one from the perspective of the base triple and the other one from the perspective of the qualifiers.
A Large Scale Search Dataset for Unbiased Learning to Rank
The unbiased learning to rank (ULTR) problem has been greatly advanced by recent deep learning techniques and well-designed debias algorithms.
pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models
However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge.
Condensing Graphs via One-Step Gradient Matching
However, existing approaches have their inherent limitations: (1) they are not directly applicable to graphs where the data is discrete; and (2) the condensation process is computationally expensive due to the involved nested optimization.
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
In this paper, we propose a new perspective to look at the performance degradation of deep GNNs, i. e., feature overcorrelation.
Detecting Harmful Online Conversational Content towards LGBTQIA+ Individuals
Online discussions, panels, talk page edits, etc., often contain harmful conversational content i. e., hate speech, death threats and offensive language, especially towards certain demographic groups.
Alternately Optimized Graph Neural Networks
Extensive experiments demonstrate that the proposed method can achieve comparable or better performance with state-of-the-art baselines while it has significantly better computation and memory efficiency.
Defense Against Gradient Leakage Attacks via Learning to Obscure Data
However, federated learning is still under the risk of privacy leakage because of the existence of attackers who deliberately conduct gradient leakage attacks to reconstruct the client data.
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?
This suggests a conflation of scoring function design, loss function design, and MP in prior work, with promising insights regarding the scalability of state-of-the-art KGC methods today, as well as careful attention to more suitable MP designs for KGC tasks tomorrow.
Enhancing Adversarial Training with Feature Separability
Deep Neural Network (DNN) are vulnerable to adversarial attacks.
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society.
Graph Enhanced BERT for Query Understanding
In other words, GE-BERT can capture both the semantic information and the users' search behavioral information of queries.
Graph Neural Networks for Multimodal Single-Cell Data Integration
Recent advances in multimodal single-cell technologies have enabled simultaneous acquisitions of multiple omics data from the same cell, providing deeper insights into cellular states and dynamics.
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering
Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations.
Graph Neural Networks with Adaptive Residual
Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks.
Graph Condensation for Graph Neural Networks
Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns.
Toward Annotator Group Bias in Crowdsourcing
We conduct experiments on both synthetic and real-world datasets.
Towards Feature Overcorrelation in Deeper Graph Neural Networks
In this paper, we observe a new issue in deeper GNNs, i. e., feature overcorrelation, and perform a thorough study to deepen our understanding on this issue.
What Truly Matters? Using Linguistic Cues for Analyzing the #BlackLivesMatter Movement and its Counter Protests: 2013 to 2020
Consequently, our findings highlight that social activism done by Black Lives Matter activists does not diverge from the social issues and topics involving police-brutality related and racially-motivated killings of Black individuals due to the shape of its topical graph that topics and conversations encircling the largest component directly relate to the topic of Black Lives Matter.
Graph Trend Filtering Networks for Recommendations
The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e. g., clicks, add-to-cart, purchases, etc.
Localized Graph Collaborative Filtering
These methods often make recommendations based on the learned user and item embeddings.
Decentralized Composite Optimization with Compression
Decentralized optimization and communication compression have exhibited their great potential in accelerating distributed machine learning by mitigating the communication bottleneck in practice.
Jointly Attacking Graph Neural Network and its Explanations
Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs.
Imbalanced Adversarial Training with Reweighting
Adversarial training has been empirically proven to be one of the most effective and reliable defense methods against adversarial attacks.
Solving ESL Sentence Completion Questions via Pre-trained Neural Language Models
Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options.
Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models
In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits.
Trustworthy AI: A Computational Perspective
In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society.
Elastic Graph Neural Networks
While many existing graph neural networks (GNNs) have been proven to perform $\ell_2$-based graph smoothing that enforces smoothness globally, in this work we aim to further enhance the local smoothness adaptivity of GNNs via $\ell_1$-based graph smoothing.
AutoLoss: Automated Loss Function Search in Recommendations
Unlike existing algorithms, the proposed controller can adaptively generate the loss probabilities for different data examples according to their varied convergence behaviors.
Is Homophily a Necessity for Graph Neural Networks?
We find that this claim is not quite true, and in fact, GCNs can achieve strong performance on heterophilous graphs under certain conditions.
Automated Self-Supervised Learning for Graphs
Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks.
Towards the Memorization Effect of Neural Networks in Adversarial Training
In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN's accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN's performance on typical samples.
Graph Feature Gating Networks
Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs.
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification
The results show that the text classification models trained under our proposed framework outperform traditional models significantly in terms of fairness, and also slightly in terms of classification performance.
Node Similarity Preserving Graph Convolutional Networks
Specifically, to balance information from graph structure and node features, we propose a feature similarity preserving aggregation which adaptively integrates graph structure and node features.
Personalized Multimodal Feedback Generation in Education
The automatic evaluation for school assignments is an important application of AI in the education field.
To be Robust or to be Fair: Towards Fairness in Adversarial Training
However, we find that adversarial training algorithms tend to introduce severe disparity of accuracy and robustness between different groups of data.
A Unified View on Graph Neural Networks as Graph Signal Denoising
In this work, we establish mathematically that the aggregation processes in a group of representative GNN models including GCN, GAT, PPNP, and APPNP can be regarded as (approximately) solving a graph denoising problem with a smoothness assumption.
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning
Extensive experiments on two real-world conversation datasets show that our framework significantly reduces gender bias in dialogue models while maintaining the response quality.
Representation Learning from Limited Educational Data with Crowdsourced Labels
Extensive experiments conducted on three real-world data sets demonstrate the superiority of our framework on learning representations from limited data with crowdsourced labels, comparing with various state-of-the-art baselines.
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily
Thus, in this paper, we perform the initial study about adversarial attacks on meta learning under the few-shot classification problem.
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
To address these challenges, in this paper, we propose OC4Seq, a multi-scale one-class recurrent neural network for detecting anomalies in discrete event sequences.
Linear Convergent Decentralized Optimization with Compression
Communication compression has become a key strategy to speed up distributed optimization.
Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks
Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective.
Memory-efficient Embedding for Recommendations
Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.
Self-supervised Learning on Graphs: Deep Insights and New Direction
Thus, we seek to harness SSL for GNNs to fully exploit the unlabeled data.
XGNN: Towards Model-Level Explanations of Graph Neural Networks
Furthermore, our experimental results indicate that the generated graphs can provide guidance on how to improve the trained GNNs.
Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models
Recently, neural network based dialogue systems have become ubiquitous in our increasingly digitalized society.
Customized Graph Neural Networks
Therefore, in this paper, we aim to develop customized graph neural networks for graph classification.
Graph Structure Learning for Robust Graph Neural Networks
A natural idea to defend adversarial attacks is to clean the perturbed graph.
Attacking Black-box Recommendations via Copying Cross-domain User Profiles
In this work, we present our framework CopyAttack, which is a reinforcement learning based black-box attack method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items.
Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms
Asking questions is one of the most crucial pedagogical techniques used by teachers in class.
Siamese Neural Networks for Class Activity Detection
Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms.
DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses
DeepRobust is a PyTorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field.
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
As the extensions of DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability.
Jointly Learning to Recommend and Advertise
Online recommendation and advertising are two major income channels for online recommendation platforms (e. g. e-commerce and news feed site).
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e. g. user/item identifiers) and meaningfully transform them in the low-dimensional space.
Social Science Guided Feature Engineering: A Novel Approach to Signed Link Analysis
The existence of negative links piques research interests in investigating whether properties and principles of signed networks differ from those of unsigned networks, and mandates dedicated efforts on link analysis for signed social networks.
Graduate Employment Prediction with Bias
The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide.
Characterizing the Decision Boundary of Deep Neural Networks
In this regard, one crucial aspect of deep neural network classifiers that can help us deepen our knowledge about their decision-making behavior is to investigate their decision boundaries.
Learning Multi-level Dependencies for Robust Word Recognition
Robust language processing systems are becoming increasingly important given the recent awareness of dangerous situations where brittle machine learning models can be easily broken with the presence of noises.
A Double Residual Compression Algorithm for Efficient Distributed Learning
Large-scale machine learning models are often trained by parallel stochastic gradient descent algorithms.
Does Gender Matter? Towards Fairness in Dialogue Systems
In particular, we construct a benchmark dataset and propose quantitative measures to understand fairness in dialogue models.
Automatic Short Answer Grading via Multiway Attention Networks
Automatic short answer grading (ASAG), which autonomously score student answers according to reference answers, provides a cost-effective and consistent approach to teaching professionals and can reduce their monotonous and tedious grading workloads.
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for the three popular data types, i. e., images, graphs and text.
Say What I Want: Towards the Dark Side of Neural Dialogue Models
Neural dialogue models have been widely adopted in various chatbot applications because of their good performance in simulating and generalizing human conversations.
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems
However, most RL-based advertising algorithms focus on optimizing ads' revenue while ignoring the possible negative influence of ads on user experience of recommended items (products, articles and videos).
Deep Knowledge Tracing with Side Information
Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems.