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Found 54 papers, 20 papers with code
How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study
We empirically study the effectiveness of machine-generated fake news detectors by understanding the model’s sensitivity to different synthetic perturbations during test time.
Evaluating Biomedical Word Embeddings for Vocabulary Alignment at Scale in the UMLS Metathesaurus Using Siamese Networks
Recent work uses a Siamese Network, initialized with BioWordVec embeddings (distributed word embeddings), for predicting synonymy among biomedical terms to automate a part of the UMLS (Unified Medical Language System) Metathesaurus construction process.
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
To address this issue, we propose a Multi-Level Embedding framework of nodes on a heterogeneous graph (HeteroMILE) - a generic methodology that allows contemporary graph embedding methods to scale to large graphs.
Grounding from an AI and Cognitive Science Lens
Grounding is a challenging problem, requiring a formal definition and different levels of abstraction.
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain
In this paper, we introduce a new neural network architecture, termed LoGoNet, with a tailored self-supervised learning (SSL) method to mitigate such challenges.
Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation
As widely demonstrated in the literature, this issue could lead to a loss of information in individual items, and significantly degrade models' scalability and performance.
Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation
In addition, SSNA adapts the top-a layers of LLMs jointly, and integrates adapters sequentially for enhanced effectiveness (i. e., recommendation performance).
Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation
Our experimental results demonstrate that ANT does not suffer from the negative transfer issue on any of the target tasks.
Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models
Ligand-based drug design aims to identify novel drug candidates of similar shapes with known active molecules.
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks
Specifically, the subgraph-based sampling approaches such as ClusterGCN and GraphSAINT have achieved state-of-the-art performance on the node classification tasks.
FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning
FairMILE is a multi-level paradigm that can efficiently learn graph representations while enforcing fairness and preserving utility.
Recursive Attentive Methods with Reused Item Representations for Sequential Recommendation
Our run-time performance comparison signifies that RAM could also be more efficient on benchmark datasets.
Prospective Preference Enhanced Mixed Attentive Model for Session-based Recommendation
In this manuscript, we develop prospective preference enhanced mixed attentive model (P2MAM) to generate session-based recommendations using two important factors: temporal patterns and estimates of users' prospective preferences.
MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest
MultiBiSage can capture the graph structure of multiple bipartite graphs to learn high-quality pin embeddings.
UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS Metathesaurus
The effectiveness of UBERT for UMLS Metathesaurus construction process is evaluated using the UMLS Vocabulary Alignment (UVA) task.
FairEGM: Fair Link Prediction and Recommendation via Emulated Graph Modification
As machine learning becomes more widely adopted across domains, it is critical that researchers and ML engineers think about the inherent biases in the data that may be perpetuated by the model.
Semi-Supervised Deep Learning for Multiplex Networks
Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations, each relation representing a distinct layer.
Privacy Policy Question Answering Assistant: A Query-Guided Extractive Summarization Approach
We address these problems by paraphrasing to bring the style and language of the user's question closer to the language of privacy policies.
Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus
Given the SOTA performance of these BERT models for other downstream tasks, our experiments yield surprisingly interesting results: (1) in both model architectures, the approaches employing these biomedical BERT-based models do not outperform the existing approaches using Siamese Network with BioWordVec embeddings for the UMLS synonymy prediction task, (2) the original BioBERT large model that has not been pre-trained with the UMLS outperforms the SapBERT models that have been pre-trained with the UMLS, and (3) using the Siamese Networks yields better performance for synonymy prediction when compared to using the biomedical BERT models.
Fairness-aware Summarization for Justified Decision-Making
In other words, decision-relevant features should provide sufficient information for the predicted outcome and should be independent of the membership of individuals in protected groups such as race and gender.
A Machine Learning Model for Nowcasting Epidemic Incidence
Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data.
SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis
Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities.
eDarkTrends: Harnessing Social Media Trends in Substance use disorders for Opioid Listings on Cryptomarket
However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means.
Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification
Using CNN, we capture semantic patterns of driver behavior from trajectories (such as a turn or a braking event).
A Tight Bound for Stochastic Submodular Cover
We show that the Adaptive Greedy algorithm of Golovin and Krause (2011) achieves an approximation bound of $(\ln (Q/\eta)+1)$ for Stochastic Submodular Cover: here $Q$ is the "goal value" and $\eta$ is the smallest non-zero marginal increase in utility deliverable by an item.
A Deep Generative Model for Molecule Optimization via One Fragment Modification
A pipeline of multiple, identical Modof models is implemented into Modof-pipe to modify an input molecule at multiple disconnection sites.
Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing
After identifying KGs for each question, we examine the skew in the distribution of questions for each KG.
M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation
We compared M2 with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket.
A Unified Non-Negative Matrix Factorization Framework for Semi-Supervised Learning on Graphs
We propose a Semi-Supervised Learning (SSL) methodology that explicitly encodes different necessary priors to learn efficient representations for nodes in a network.
HAM: Hybrid Associations Models for Sequential Recommendation
We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings.
Interpretable Multi-Headed Attention for Abstractive Summarization at Controllable Lengths
Abstractive summarization at controllable lengths is a challenging task in natural language processing.
Towards Quantifying the Distance between Opinions
Similarly, in a supervised setting, our opinion distance measure achieves considerably better accuracy (up to 20% increase) compared to extant approaches that rely on text similarity, stance similarity, and sentiment similarity
An End-to-End Framework for Cold Question Routing in Community Question Answering Services
Routing newly posted questions (a. k. a cold questions) to potential answerers with the suitable expertise in Community Question Answering sites (CQAs) is an important and challenging task.
Automatic Table completion using Knowledge Base
In this work, we propose to resolve a new type of heterogeneous query viz: tabular query, which contains a natural language query description, column names of the desired table, and an example row.
Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights
Further, we have shown the impact of traffic information, time, and points-of-interest data for real-time accident prediction.
Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis.
A Countrywide Traffic Accident Dataset
Reducing traffic accidents is an important public safety challenge.
Databases Computers and Society
Optimal Exploitation of Clustering and History Information in Multi-Armed Bandit
We consider the stochastic multi-armed bandit problem and the contextual bandit problem with historical observations and pre-clustered arms.
Network Representation Learning: Consolidation and Renewed Bearing
An important area of research that has emerged over the last decade is the use of graphs as a vehicle for non-linear dimensionality reduction in a manner akin to previous efforts based on manifold learning with uses for downstream database processing, machine learning and visualization.
Towards Open Intent Discovery for Conversational Text
Existing research for intent discovery model it as a classification task with a predefined set of known categories.
PL-NMF: Parallel Locality-Optimized Non-negative Matrix Factorization
Non-negative Matrix Factorization (NMF) is a key kernel for unsupervised dimension reduction used in a wide range of applications, including topic modeling, recommender systems and bioinformatics.
Hypergraph Clustering: A Modularity Maximization Approach
Clustering on hypergraphs has been garnering increased attention with potential applications in network analysis, VLSI design and computer vision, among others.
ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation
Directed graphs have been widely used in Community Question Answering services (CQAs) to model asymmetric relationships among different types of nodes in CQA graphs, e. g., question, answer, user.
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ColdRoute: Effective Routing of Cold Questions in Stack Exchange Sites
Using data from eight stack exchange sites, we are able to improve upon the routing metrics (Precision$@1$, Accuracy, MRR) over the state-of-the-art models such as semantic matching by $159. 5\%$,$31. 84\%$, and $40. 36\%$ for cold questions posted by existing askers, and $123. 1\%$, $27. 03\%$, and $34. 81\%$ for cold questions posted by new askers respectively.
Fusion Graph Convolutional Networks
State-of-the-art models for node classification on such attributed graphs use differentiable recursive functions that enable aggregation and filtering of neighborhood information from multiple hops.
HOPF: Higher Order Propagation Framework for Deep Collective Classification
Given a graph where every node has certain attributes associated with it and some nodes have labels associated with them, Collective Classification (CC) is the task of assigning labels to every unlabeled node using information from the node as well as its neighbors.
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user.
MILE: A Multi-Level Framework for Scalable Graph Embedding
We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs.
Adaptive Bayesian Sampling with Monte Carlo EM
Our approach provides a simpler alternative, by using existing dynamics in the sampling step of a Monte Carlo EM framework, and learning the mass matrices in the M step with a novel online technique.
Multimodal Content Analysis for Effective Advertisements on YouTube
The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users.
Fast Change Point Detection on Dynamic Social Networks
In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model.
REMIX: Automated Exploration for Interactive Outlier Detection
This formulation incorporates multiple aspects such as (i) an upper limit on the total execution time of detectors (ii) diversity in the space of algorithms and features, and (iii) meta-learning for evaluating the cost and utility of detectors.
Semi-supervised Embedding in Attributed Networks with Outliers
In this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of vertices in a partially labeled attributed network (PLAN).
Robust Contextual Outlier Detection: Where Context Meets Sparsity
To address these problems, here we propose a novel and robust approach alternative to the state-of-the-art called RObust Contextual Outlier Detection (ROCOD).
