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Found 132 papers, 57 papers with code
TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion
In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule network-based embedding model for Temporal knowledge graph completion.
Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework
Various temporal knowledge graph (KG) completion models have been proposed in the recent literature.
Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs
In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i. e., edge formation and dissolution.
Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?
Various jailbreak attacks have been proposed to red-team Large Language Models (LLMs) and revealed the vulnerable safeguards of LLMs.
Wiki-TabNER:Advancing Table Interpretation Through Named Entity Recognition
Finally, we propose a prompting framework for evaluating the newly developed large language models (LLMs) on this novel TI task.
Stop Reasoning! When Multimodal LLMs with Chain-of-Thought Reasoning Meets Adversarial Images
Our research evaluates the adversarial robustness of MLLMs when employing CoT reasoning, finding that CoT marginally improves adversarial robustness against existing attack methods.
Quantum Architecture Search with Unsupervised Representation Learning
Most QAS algorithms combine their search space and search algorithms together and thus generally require evaluating a large number of quantum circuits during the search process.
Understanding and Improving In-Context Learning on Vision-language Models
Our findings indicate that ICL in VLMs is predominantly driven by the textual information in the demonstrations whereas the visual information in the demonstrations barely affects the ICL performance.
Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation
A risk with these models is the potential generation of inappropriate content, such as biased or harmful images.
SPOT! Revisiting Video-Language Models for Event Understanding
This raises a question: with such weak supervision, can video representation in video-language models gain the ability to distinguish even factual discrepancies in textual description and understand fine-grained events?
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
We first input the text descriptions of KG relations into large language models (LLMs) for generating relation representations, and then introduce them into embedding-based TKGF methods.
Enhancing Multimodal Compositional Reasoning of Visual Language Models with Generative Negative Mining
Leveraging these generative hard negative samples, we significantly enhance VLMs' performance in tasks involving multimodal compositional reasoning.
Does Your Model Think Like an Engineer? Explainable AI for Bearing Fault Detection with Deep Learning
Deep Learning has already been successfully applied to analyze industrial sensor data in a variety of relevant use cases.
Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts
Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictions of complex deep neural networks.
GraphextQA: A Benchmark for Evaluating Graph-Enhanced Large Language Models
The proposed dataset is designed to evaluate graph-language models' ability to understand graphs and make use of it for answer generation.
GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models
Extensive experiments have shown that GenTKG outperforms conventional methods of temporal relational forecasting with low computation resources using extremely limited training data as few as 16 samples.
Differentiable Quantum Architecture Search for Quantum Reinforcement Learning
However, the pre-defined circuit needs more flexibility for different tasks, and the circuit design based on various datasets could become intractable in the case of a large circuit.
Adversarial Attacks on Tables with Entity Swap
The recently proposed tabular language models have reported state-of-the-art results across various tasks for table interpretation.
Multi-event Video-Text Retrieval
In this study, we introduce the Multi-event Video-Text Retrieval (MeVTR) task, addressing scenarios in which each video contains multiple different events, as a niche scenario of the conventional Video-Text Retrieval Task.
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning
FedDAT is the first approach that enables an efficient distributed finetuning of foundation models for a variety of heterogeneous Vision-Language tasks.
FedPop: Federated Population-based Hyperparameter Tuning
Similar to conventional ML pipelines, the client local optimization and server aggregation procedure in FL are sensitive to the hyperparameter (HP) selection.
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
This paper aims to provide a comprehensive survey of cutting-edge research in prompt engineering on three types of vision-language models: multimodal-to-text generation models (e. g. Flamingo), image-text matching models (e. g.
Exploring Link Prediction over Hyper-Relational Temporal Knowledge Graphs Enhanced with Time-Invariant Relational Knowledge
We develop two new benchmark hyper-relational TKG (HTKG) datasets, i. e., Wiki-hy and YAGO-hy, and propose an HTKG reasoning model that efficiently models both temporal facts and qualifiers.
Can Vision-Language Models be a Good Guesser? Exploring VLMs for Times and Location Reasoning
This makes us wonder if, based on visual cues, Vision-Language Models that are pre-trained with large-scale image-text resources can achieve and even outperform human's capability in reasoning times and location.
GRAtt-VIS: Gated Residual Attention for Auto Rectifying Video Instance Segmentation
Secondly, we propose a novel inter-instance interaction using gate activation as a mask for self-attention.
Ranked #4 on
Video Instance Segmentation
on YouTube-VIS 2021
(using extra training data)
A Knowledge Graph Perspective on Supply Chain Resilience
Global crises and regulatory developments require increased supply chain transparency and resilience.
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning
Most previous TKGC methods only consider predicting the missing links among the entities seen in the training set, while they are unable to achieve great performance in link prediction concerning newly-emerged unseen entities.
Modeling the evolution of temporal knowledge graphs with uncertainty
Forecasting future events is a fundamental challenge for temporal knowledge graphs (tKG).
Do DALL-E and Flamingo Understand Each Other?
To study this question, we propose a reconstruction task where Flamingo generates a description for a given image and DALL-E uses this description as input to synthesize a new image.
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering
Visual Question Answering (VQA) is a multi-discipline research task.
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information
Similar problem exists in temporal knowledge graphs (TKGs), and no previous temporal knowledge graph completion (TKGC) method is developed for modeling newly-emerged entities.
Named Entity Recognition in Industrial Tables using Tabular Language Models
Specialized transformer-based models for encoding tabular data have gained interest in academia.
InstanceFormer: An Online Video Instance Segmentation Framework
We propose three novel components to model short-term and long-term dependency and temporal coherence.
Ranked #5 on
Video Instance Segmentation
on Youtube-VIS 2022 Validation
(using extra training data)
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs
To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions.
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness
Since SegPGD can create more effective adversarial examples, the adversarial training with our SegPGD can boost the robustness of segmentation models.
Biologically Inspired Neural Path Finding
The human brain can be considered to be a graphical structure comprising of tens of billions of biological neurons connected by synapses.
On Calibration of Graph Neural Networks for Node Classification
We investigate the calibration of graph neural networks for node classification, study the effect of existing post-processing calibration methods, and analyze the influence of model capacity, graph density, and a new loss function on calibration.
Continuous Temporal Graph Networks for Event-Based Graph Data
Hence, we propose Continuous Temporal Graph Networks (CTGNs) to capture the continuous dynamics of temporal graph data.
FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation
Real-world applications usually involve a distribution shift across the datasets of the different clients, which hurts the generalization ability of the clients to unseen samples from their respective data distributions.
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction
In this paper, we follow the previous work that focuses on few-shot relational learning on static KGs and extend two fundamental TKG reasoning tasks, i. e., interpolated and extrapolated link prediction, to the one-shot setting.
Relationformer: A Unified Framework for Image-to-Graph Generation
We leverage direct set-based object prediction and incorporate the interaction among the objects to learn an object-relation representation jointly.
ECOLA: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations
Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embedding using texts.
A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs
The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics.
TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs
Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types.
A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion
Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time.
Adversarial Examples on Segmentation Models Can be Easy to Transfer
The high transferability achieved by our method shows that, in contrast to the observations in previous work, adversarial examples on a segmentation model can be easy to transfer to other segmentation models.
Are Vision Transformers Robust to Patch Perturbations?
However, when ViTs are attacked by an adversary, the attention mechanism can be easily fooled to focus more on the adversarially perturbed patches and cause a mistake.
Generating Table Vector Representations
High-quality Web tables are rich sources of information that can be used to populate Knowledge Graphs (KG).
Towards Data-Free Domain Generalization
In particular, we address the question: How can knowledge contained in models trained on different source domains be merged into a single model that generalizes well to unseen target domains, in the absence of source and target domain data?
Are Vision Transformers Robust to Patch-wise Perturbations?
Based on extensive qualitative and quantitative experiments, we discover that ViT's stronger robustness to natural corrupted patches and higher vulnerability against adversarial patches are both caused by the attention mechanism.
The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding
Although memory appears to be about the past, its main purpose is to support the agent in the present and the future.
Description-based Label Attention Classifier for Explainable ICD-9 Classification
ICD-9 coding is a relevant clinical billing task, where unstructured texts with information about a patient's diagnosis and treatments are annotated with multiple ICD-9 codes.
Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption
Machine learning models that can generalize to unseen domains are essential when applied in real-world scenarios involving strong domain shifts.
Adaptive Multi-Resolution Attention with Linear Complexity
Transformers have improved the state-of-the-art across numerous tasks in sequence modeling.
Categorical EHR Imputation with Generative Adversarial Nets
Recently, researchers have attempted to apply GANs to missing data generation and imputation for EHR data: a major challenge here is the categorical nature of the data.
Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models
Recurrent neural network based solutions are increasingly being used in the analysis of longitudinal Electronic Health Record data.
OODformer: Out-Of-Distribution Detection Transformer
A serious problem in image classification is that a trained model might perform well for input data that originates from the same distribution as the data available for model training, but performs much worse for out-of-distribution (OOD) samples.
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering
We conduct an experimental study on the challenging dataset GQA, based on both manually curated and automatically generated scene graphs.
Scenes and Surroundings: Scene Graph Generation using Relation Transformer
Identifying objects in an image and their mutual relationships as a scene graph leads to a deep understanding of image content.
Improving Inductive Link Prediction Using Hyper-Relational Facts
In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks.
Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning
We propose an uncertainty-aware deep kernel learning model which permits the estimation of the uncertainty in the prediction by a pipeline of a Convolutional Neural Network and a sparse Gaussian Process.
Capsule Network is Not More Robust than Convolutional Network
The examination reveals five major new/different components in CapsNet: a transformation process, a dynamic routing layer, a squashing function, a marginal loss other than cross-entropy loss, and an additional class-conditional reconstruction loss for regularization.
Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs
Biomedical knowledge graphs permit an integrative computational approach to reasoning about biological systems.
Mutual Information State Intrinsic Control
Reinforcement learning has been shown to be highly successful at many challenging tasks.
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification
Malicious software (malware) poses an increasing threat to the security of communication systems as the number of interconnected mobile devices increases exponentially.
Effective and Efficient Vote Attack on Capsule Networks
As alternatives to CNNs, the recently proposed Capsule Networks (CapsNets) are shown to be more robust to white-box attacks than CNNs under popular attack protocols.
Improving Scene Graph Classification by Exploiting Knowledge from Texts
We show that by fine-tuning the classification pipeline with the extracted knowledge from texts, we can achieve ~8x more accurate results in scene graph classification, ~3x in object classification, and ~1. 5x in predicate classification, compared to the supervised baselines with only 1% of the annotated images.
Temporal Knowledge Graph Forecasting with Neural ODE
In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i. e., edge formation and dissolution.
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs
We evaluate our model on four benchmark temporal knowledge graphs for the link forecasting task.
xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links
We evaluate our model on four benchmark temporal knowledge graphs for the link forecasting task.
Interpretable Graph Capsule Networks for Object Recognition
In the proposed model, individual classification explanations can be created effectively and efficiently.
Classification by Attention: Scene Graph Classification with Prior Knowledge
A major challenge in scene graph classification is that the appearance of objects and relations can be significantly different from one image to another.
DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion
Product manifolds enable our approach to better reflect a wide variety of geometric structures on temporal KGs.
Controllable Multi-Character Psychology-Oriented Story Generation
In our work, we aim to design an emotional line for each character that considers multiple emotions common in psychological theories, with the goal of generating stories with richer emotional changes in the characters.
Introspective Learning by Distilling Knowledge from Online Self-explanation
Motivated by the conclusion, we propose an implementation of introspective learning by distilling knowledge from online self-explanations.
ARCADe: A Rapid Continual Anomaly Detector
Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored.
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs.
Ranked #1 on
Link Prediction
on WN18
(training time (s) metric)
Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing
The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks.
Few-Shot One-Class Classification via Meta-Learning
Our experiments on eight datasets from the image and time-series domains show that our method leads to better results than classical OCC and few-shot classification approaches, and demonstrate the ability to learn unseen tasks from only few normal class samples.
Scene Graph Reasoning for Visual Question Answering
We propose a novel method that approaches the task by performing context-driven, sequential reasoning based on the objects and their semantic and spatial relationships present in the scene.
Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score
Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups.
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.
Relation Transformer Network
In this work, we propose a novel transformer formulation for scene graph generation and relation prediction.
Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs
The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types.
Causal Inference under Networked Interference and Intervention Policy Enhancement
After deriving causal effect estimators, we further study intervention policy improvement on the graph under capacity constraint.
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment.
Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning
In reinforcement learning, an agent learns to reach a set of goals by means of an external reward signal.
Search for Better Students to Learn Distilled Knowledge
The knowledge of a well-performed teacher is distilled to a student with a small architecture.
The Tensor Brain: Semantic Decoding for Perception and Memory
In particular, we propose that explicit perception and declarative memories require a semantic decoder, which, in a simple realization, is based on four layers: First, a sensory memory layer, as a buffer for sensory input, second, an index layer representing concepts, third, a memoryless representation layer for the broadcasting of information ---the "blackboard", or the "canvas" of the brain--- and fourth, a working memory layer as a processing center and data buffer.
Active Learning for Entity Alignment
In this work, we propose a novel framework for the labeling of entity alignments in knowledge graph datasets.
Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs
The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (thesis) or the fact being false (antithesis), respectively.
Quantum Machine Learning Algorithm for Knowledge Graphs
We simplify the problem by making a plausible assumption that the tensor representation of a knowledge graph can be approximated by its low-rank tensor singular value decomposition, which is verified by our experiments.
Reasoning on Knowledge Graphs with Debate Dynamics
The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively.
Neural Network Memorization Dissection
What is the difference between DNNs trained with random labels and the ones trained with true labels?
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task.
Ranked #33 on
Entity Alignment
on DBP15k zh-en
Improving the Robustness of Capsule Networks to Image Affine Transformations
Our investigation reveals that the routing procedure contributes neither to the generalization ability nor to the affine robustness of the CapsNets.
Walking the Tightrope: An Investigation of the Convolutional Autoencoder Bottleneck
We believe that the findings in this paper are directly applicable and will lead to improvements in models that rely on CAEs.
Contextual Prediction Difference Analysis for Explaining Individual Image Classifications
In this work, we first show that PDA can suffer from saturated classifiers.
Semantics for Global and Local Interpretation of Deep Neural Networks
Deep neural networks (DNNs) with high expressiveness have achieved state-of-the-art performance in many tasks.
Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation
We cast the problem of image denoising as a domain translation problem between high and low noise domains.
Push it to the Limit: Discover Edge-Cases in Image Data with Autoencoders
In this paper, we focus on the problem of identifying semantic factors of variation in large image datasets.
Self-Supervised State-Control through Intrinsic Mutual Information Rewards
Our results show that the mutual information between the context states and the states of interest can be an effective ingredient for overcoming challenges in robotic manipulation tasks with sparse rewards.
Saliency Methods for Explaining Adversarial Attacks
The idea behind saliency methods is to explain the classification decisions of neural networks by creating so-called saliency maps.
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
This objective encourages the agent to maximize the expected return, as well as to achieve more diverse goals.
Improving Visual Relation Detection using Depth Maps
We argue that depth maps can additionally provide valuable information on object relations, e. g. helping to detect not only spatial relations, such as standing behind, but also non-spatial relations, such as holding.
Ranked #1 on
Relationship Detection
on VRD
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing
To this end, we present the first approach to unsupervised text generation from KGs and show simultaneously how it can be used for unsupervised semantic parsing.
Ranked #1 on
Unsupervised KG-to-Text Generation
on WebNLG v2.1
Curiosity-Driven Experience Prioritization via Density Estimation
In Reinforcement Learning (RL), an agent explores the environment and collects trajectories into the memory buffer for later learning.
Variational Quantum Circuit Model for Knowledge Graphs Embedding
In this work, we propose the first quantum Ans\"atze for the statistical relational learning on knowledge graphs using parametric quantum circuits.
Understanding Individual Decisions of CNNs via Contrastive Backpropagation
The experiments and analysis conclude that the explanations generated by LRP are not class-discriminative.
Efficient Dialog Policy Learning via Positive Memory Retention
This paper is concerned with the training of recurrent neural networks as goal-oriented dialog agents using reinforcement learning.
Energy-Based Hindsight Experience Prioritization
We evaluate our Energy-Based Prioritization (EBP) approach on four challenging robotic manipulation tasks in simulation.
Improving Visual Relationship Detection using Semantic Modeling of Scene Descriptions
In this paper we consider scene descriptions which are represented as a set of triples (subject, predicate, object), where each triple consists of a pair of visual objects, which appear in the image, and the relationship between them (e. g. man-riding-elephant, man-wearing-hat).
Improving Information Extraction from Images with Learned Semantic Models
Many applications require an understanding of an image that goes beyond the simple detection and classification of its objects.
Learning Goal-Oriented Visual Dialog via Tempered Policy Gradient
Learning goal-oriented dialogues by means of deep reinforcement learning has recently become a popular research topic.
Embedding Models for Episodic Knowledge Graphs
In this paper, we extend models for static knowledge graphs to temporal knowledge graphs.
Semi-supervised Outlier Detection using Generative And Adversary Framework
In the adversarial process of training CorGAN, the Generator is supposed to generate outlier samples for negative class, and the Discriminator as an one-class classifier is trained to distinguish data from training datasets (i. e. positive class) and generated data from the Generator (i. e. negative class).
Tensor Decompositions for Modeling Inverse Dynamics
The decomposition of sparse tensors has successfully been used in relational learning, e. g., the modeling of large knowledge graphs.
Attention-based Information Fusion using Multi-Encoder-Decoder Recurrent Neural Networks
With the rising number of interconnected devices and sensors, modeling distributed sensor networks is of increasing interest.
The Tensor Memory Hypothesis
We show how episodic memory and semantic memory can be realized and discuss how new memory traces can be generated from sensory input: Existing memories are the basis for perception and new memories are generated via perception.
Tensor-Train Recurrent Neural Networks for Video Classification
The Recurrent Neural Networks and their variants have shown promising performances in sequence modeling tasks such as Natural Language Processing.
Predictive Clinical Decision Support System with RNN Encoding and Tensor Decoding
We also address the problem of correlation in target features: Often a physician is required to make multiple (sub-)decisions in a block, and that these decisions are mutually dependent.
Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks
In this work we present an approach based on RNNs, specifically designed for the clinical domain, that combines static and dynamic information in order to predict future events.
Predicting the Co-Evolution of Event and Knowledge Graphs
By predicting future events, we also predict likely changes in the knowledge graph and thus obtain a model for the evolution of the knowledge graph as well.
Learning with Memory Embeddings
We introduce a number of hypotheses on human memory that can be derived from the developed mathematical models.
Type-Constrained Representation Learning in Knowledge Graphs
Latent variable models have increasingly gained attention for the statistical modeling of knowledge graphs, showing promising results in tasks related to knowledge graph completion and cleaning.
A Review of Relational Machine Learning for Knowledge Graphs
In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph).
Reducing the Rank in Relational Factorization Models by Including Observable Patterns
Tensor factorizations have become popular methods for learning from multi-relational data.
Towards a New Science of a Clinical Data Intelligence
We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i. e., with data from many patients and with complete patient information.
Logistic Tensor Factorization for Multi-Relational Data
Tensor factorizations have become increasingly popular approaches for various learning tasks on structured data.
A Three-Way Model for Collective Learning on Multi-Relational Data
Relational learning is becoming increasingly important in many areas of application.
