Search Results for author: Volker Tresp

Found 94 papers, 40 papers with code

Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs

no code implementations EMNLP 2021 Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, Volker Tresp

In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i. e., edge formation and dissolution.

Knowledge Graphs

TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion

1 code implementation spnlp (ACL) 2022 Guirong Fu, Zhao Meng, Zhen Han, Zifeng Ding, Yunpu Ma, Matthias Schubert, Volker Tresp, Roger Wattenhofer

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.

Entity Embeddings Knowledge Graph Completion +1

Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations

no code implementations17 Mar 2022 Zhen Han, Ruotong Liao, Beiyan Liu, Yao Zhang, Zifeng Ding, Heinz Köppl, Hinrich Schütze, Volker Tresp

We align structured knowledge contained in temporal knowledge graphs with their textual descriptions extracted from news articles and propose a novel knowledge-text prediction task to inject the abundant information from descriptions into temporal knowledge embeddings.

Knowledge Graph Completion Temporal Knowledge Graph Completion

A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs

2 code implementations14 Mar 2022 Charles Tapley Hoyt, Max Berrendorf, Mikhail Galkin, Volker Tresp, Benjamin M. Gyori

The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics.

Knowledge Graph Embedding Knowledge Graphs +1

TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs

1 code implementation15 Dec 2021 Yushan Liu, Yunpu Ma, Marcel Hildebrandt, Mitchell Joblin, Volker Tresp

Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types.

Knowledge Graphs Link Prediction

Adversarial Examples on Segmentation Models Can be Easy to Transfer

no code implementations22 Nov 2021 Jindong Gu, Hengshuang Zhao, Volker Tresp, Philip Torr

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.

Adversarial Robustness Classification +3

Are Vision Transformers Robust to Patch Perturbations?

no code implementations20 Nov 2021 Jindong Gu, Volker Tresp, Yao Qin

In this work, we study the robustness of vision transformers to patch-wise perturbations.

Image Classification

Generating Table Vector Representations

no code implementations28 Oct 2021 Aneta Koleva, Martin Ringsquandl, Mitchell Joblin, Volker Tresp

High-quality Web tables are rich sources of information that can be used to populate Knowledge Graphs (KG).

Knowledge Graphs Transfer Learning

Towards Data-Free Domain Generalization

no code implementations9 Oct 2021 Ahmed Frikha, Haokun Chen, Denis Krompaß, Thomas Runkler, Volker Tresp

We define the novel problem of Data-Free Domain Generalization (DFDG), a practical setting where models trained on the source domains separately are available instead of the original datasets, and investigate how to effectively solve the domain generalization problem in that case.

Domain Generalization Knowledge Distillation

Are Vision Transformers Robust to Patch-wise Perturbations?

no code implementations29 Sep 2021 Jindong Gu, Volker Tresp, Yao Qin

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.

Image Classification

The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding

no code implementations27 Sep 2021 Volker Tresp, Sahand Sharifzadeh, Hang Li, Dario Konopatzki, Yunpu Ma

In our model, perception, episodic memory, and semantic memory are realized by different functional and operational modes of the oscillating interactions between an index layer and a representation layer in a bilayer tensor network (BTN).

Description-based Label Attention Classifier for Explainable ICD-9 Classification

no code implementations WNUT (ACL) 2021 Malte Feucht, Zhiliang Wu, Sophia Althammer, Volker Tresp

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.


COLUMBUS: Automated Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption

no code implementations9 Sep 2021 Ahmed Frikha, Denis Krompaß, Volker Tresp

Machine learning models that can generalize to unseen domains are essential when applied in real-world scenarios involving strong domain shifts.

Domain Generalization

Adaptive Multi-Resolution Attention with Linear Complexity

no code implementations10 Aug 2021 Yao Zhang, Yunpu Ma, Thomas Seidl, Volker Tresp

Transformers have improved the state-of-the-art across numerous tasks in sequence modeling.

Categorical EHR Imputation with Generative Adversarial Nets

no code implementations3 Aug 2021 Yinchong Yang, Zhiliang Wu, Volker Tresp, Peter A. Fasching

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.

Image Generation Imputation

Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models

1 code implementation26 Jul 2021 Zhiliang Wu, Yinchong Yang, Peter A. Fasching, Volker Tresp

Recurrent neural network based solutions are increasingly being used in the analysis of longitudinal Electronic Health Record data.

Metric Learning Time-to-Event Prediction

OODformer: Out-Of-Distribution Detection Transformer

1 code implementation19 Jul 2021 Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher, Stephan Günnemann, Volker Tresp

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.

Contrastive Learning OOD Detection +1

Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering

1 code implementation13 Jul 2021 Rajat Koner, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, Stephan Günnemann

We conduct an experimental study on the challenging dataset GQA, based on both manually curated and automatically generated scene graphs.

Question Answering Visual Question Answering +1

Scenes and Surroundings: Scene Graph Generation using Relation Transformer

1 code implementation12 Jul 2021 Rajat Koner, Poulami Sinhamahapatra, Volker Tresp

Identifying objects in an image and their mutual relationships as a scene graph leads to a deep understanding of image content.

Graph Generation Scene Graph Generation

Improving Inductive Link Prediction Using Hyper-Relational Facts

1 code implementation10 Jul 2021 Mehdi Ali, Max Berrendorf, Mikhail Galkin, Veronika Thost, Tengfei Ma, Volker Tresp, Jens Lehmann

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.

Inductive Link Prediction Knowledge Graphs

Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning

1 code implementation1 Jun 2021 Zhiliang Wu, Yinchong Yang, Jindong Gu, Volker Tresp

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

no code implementations CVPR 2021 Jindong Gu, Volker Tresp, Han Hu

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.

Image Classification

Mutual Information State Intrinsic Control

2 code implementations ICLR 2021 Rui Zhao, Yang Gao, Pieter Abbeel, Volker Tresp, Wei Xu

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

no code implementations5 Mar 2021 Julian Busch, Anton Kocheturov, Volker Tresp, Thomas Seidl

Malicious software (malware) poses an increasing threat to the security of communication systems as the number of interconnected mobile devices increases exponentially.

General Classification Malware Detection

Effective and Efficient Vote Attack on Capsule Networks

1 code implementation ICLR 2021 Jindong Gu, Baoyuan Wu, Volker Tresp

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.

Adversarial Robustness

Improving Scene Graph Classification by Exploiting Knowledge from Texts

no code implementations9 Feb 2021 Sahand Sharifzadeh, Sina Moayed Baharlou, Martin Schmitt, Hinrich Schütze, Volker Tresp

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.

Classification General Classification +8

Temporal Knowledge Graph Forecasting with Neural ODE

1 code implementation13 Jan 2021 Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, Volker Tresp

However, most of the existing models for temporal knowledge graph forecasting use Recurrent Neural Network (RNN) with discrete depth to capture temporal information, while time is a continuous variable.

Future prediction

Interpretable Graph Capsule Networks for Object Recognition

no code implementations3 Dec 2020 Jindong Gu, Volker Tresp

In the proposed model, individual classification explanations can be created effectively and efficiently.

Adversarial Robustness Object Recognition

Classification by Attention: Scene Graph Classification with Prior Knowledge

no code implementations19 Nov 2020 Sahand Sharifzadeh, Sina Moayed Baharlou, Volker Tresp

A major challenge in scene graph classification is that the appearance of objects and relations can be significantly different from one image to another.

Classification General Classification +5

Controllable Multi-Character Psychology-Oriented Story Generation

1 code implementation11 Oct 2020 Feifei Xu, Xinpeng Wang, Yunpu Ma, Volker Tresp, Yuyi Wang, Shanlin Zhou, Haizhou Du

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.

Story Generation

Introspective Learning by Distilling Knowledge from Online Self-explanation

no code implementations19 Sep 2020 Jindong Gu, Zhiliang Wu, Volker Tresp

Motivated by the conclusion, we propose an implementation of introspective learning by distilling knowledge from online self-explanations.

Knowledge Distillation

ARCADe: A Rapid Continual Anomaly Detector

1 code implementation10 Aug 2020 Ahmed Frikha, Denis Krompaß, Volker Tresp

Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored.

Anomaly Detection Continual Learning +2

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

2 code implementations28 Jul 2020 Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, Jens Lehmann

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)

Knowledge Graph Embedding Knowledge Graph Embeddings +1

Few-Shot One-Class Classification via Meta-Learning

1 code implementation8 Jul 2020 Ahmed Frikha, Denis Krompaß, Hans-Georg Köpken, Volker Tresp

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.

Anomaly Detection Classification +4

Scene Graph Reasoning for Visual Question Answering

no code implementations2 Jul 2020 Marcel Hildebrandt, Hang Li, Rajat Koner, Volker Tresp, Stephan Günnemann

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.

Question Answering Visual Question Answering

Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score

1 code implementation2 Jul 2020 Zhiliang Wu, Yinchong Yang, Yunpu Ma, Yushan Liu, Rui Zhao, Michael Moor, Volker Tresp

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

2 code implementations23 Jun 2020 Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Mikhail Galkin, Sahand Sharifzadeh, Asja Fischer, Volker Tresp, Jens Lehmann

The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.

Knowledge Graph Embedding

Relation Transformer Network

1 code implementation13 Apr 2020 Rajat Koner, Suprosanna Shit, Volker Tresp

In this work, we propose a novel transformer formulation for scene graph generation and relation prediction.

Graph Generation Relation Classification +1

Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs

1 code implementation AKBC 2020 Zhen Han, Yunpu Ma, Yuyi Wang, Stephan Günnemann, Volker Tresp

The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types.

Knowledge Graphs

Causal Inference under Networked Interference and Intervention Policy Enhancement

no code implementations20 Feb 2020 Yunpu Ma, Volker Tresp

After deriving causal effect estimators, we further study intervention policy improvement on the graph under capacity constraint.

Causal Inference

On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods

1 code implementation17 Feb 2020 Max Berrendorf, Evgeniy Faerman, Laurent Vermue, Volker Tresp

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.

Entity Alignment Informativeness +2

Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning

no code implementations5 Feb 2020 Rui Zhao, Yang Gao, Pieter Abbeel, Volker Tresp, Wei Xu

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

no code implementations30 Jan 2020 Jindong Gu, Volker Tresp

The knowledge of a well-performed teacher is distilled to a student with a small architecture.

Knowledge Distillation Model Compression

The Tensor Brain: Semantic Decoding for Perception and Memory

no code implementations29 Jan 2020 Volker Tresp, Sahand Sharifzadeh, Dario Konopatzki, Yunpu Ma

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.

Knowledge Graphs

Active Learning for Entity Alignment

1 code implementation24 Jan 2020 Max Berrendorf, Evgeniy Faerman, Volker Tresp

In this work, we propose a novel framework for the labeling of entity alignments in knowledge graph datasets.

Active Learning Entity Alignment

Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs

no code implementations9 Jan 2020 Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, Volker Tresp

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.

Common Sense Reasoning Fact Checking +4

Quantum Machine Learning Algorithm for Knowledge Graphs

no code implementations4 Jan 2020 Yunpu Ma, Volker Tresp

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.

Knowledge Graphs

Reasoning on Knowledge Graphs with Debate Dynamics

2 code implementations2 Jan 2020 Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, Volker Tresp

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.

Frame General Classification +3

Neural Network Memorization Dissection

no code implementations21 Nov 2019 Jindong Gu, Volker Tresp

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

1 code implementation19 Nov 2019 Max Berrendorf, Evgeniy Faerman, Valentyn Melnychuk, Volker Tresp, Thomas Seidl

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.

Entity Alignment Knowledge Graphs

Improving the Robustness of Capsule Networks to Image Affine Transformations

no code implementations CVPR 2020 Jindong Gu, Volker Tresp

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

1 code implementation18 Nov 2019 Ilja Manakov, Markus Rohm, Volker Tresp

We believe that the findings in this paper are directly applicable and will lead to improvements in models that rely on CAEs.

Outlier Detection Representation Learning +1

Contextual Prediction Difference Analysis for Explaining Individual Image Classifications

no code implementations21 Oct 2019 Jindong Gu, Volker Tresp

In this work, we first show that PDA can suffer from saturated classifiers.

Semantics for Global and Local Interpretation of Deep Neural Networks

no code implementations21 Oct 2019 Jindong Gu, Volker Tresp

Deep neural networks (DNNs) with high expressiveness have achieved state-of-the-art performance in many tasks.

Push it to the Limit: Discover Edge-Cases in Image Data with Autoencoders

1 code implementation7 Oct 2019 Ilja Manakov, Volker Tresp

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

1 code implementation25 Sep 2019 Rui Zhao, Volker Tresp, Wei Xu

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.

OpenAI Gym reinforcement-learning

Saliency Methods for Explaining Adversarial Attacks

no code implementations22 Aug 2019 Jindong Gu, Volker Tresp

The idea behind saliency methods is to explain the classification decisions of neural networks by creating so-called saliency maps.

General Classification

Maximum Entropy-Regularized Multi-Goal Reinforcement Learning

3 code implementations21 May 2019 Rui Zhao, Xudong Sun, Volker Tresp

This objective encourages the agent to maximize the expected return, as well as to achieve more diverse goals.

Multi-Goal Reinforcement Learning OpenAI Gym +1

Improving Visual Relation Detection using Depth Maps

1 code implementation2 May 2019 Sahand Sharifzadeh, Sina Moayed Baharlou, Max Berrendorf, Rajat Koner, Volker Tresp

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.

Curiosity-Driven Experience Prioritization via Density Estimation

no code implementations20 Feb 2019 Rui Zhao, Volker Tresp

In Reinforcement Learning (RL), an agent explores the environment and collects trajectories into the memory buffer for later learning.

Density Estimation OpenAI Gym +1

Variational Quantum Circuit Model for Knowledge Graphs Embedding

no code implementations19 Feb 2019 Yunpu Ma, Volker Tresp, Liming Zhao, Yuyi Wang

In this work, we propose the first quantum Ans\"atze for the statistical relational learning on knowledge graphs using parametric quantum circuits.

Knowledge Graph Embedding Knowledge Graphs +1

Understanding Individual Decisions of CNNs via Contrastive Backpropagation

1 code implementation5 Dec 2018 Jindong Gu, Yinchong Yang, Volker Tresp

The experiments and analysis conclude that the explanations generated by LRP are not class-discriminative.

General Classification

Energy-Based Hindsight Experience Prioritization

2 code implementations2 Oct 2018 Rui Zhao, Volker Tresp

We evaluate our Energy-Based Prioritization (EBP) approach on four challenging robotic manipulation tasks in simulation.


Efficient Dialog Policy Learning via Positive Memory Retention

2 code implementations2 Oct 2018 Rui Zhao, Volker Tresp

This paper is concerned with the training of recurrent neural networks as goal-oriented dialog agents using reinforcement learning.

Goal-Oriented Dialog Object Discovery +1

Improving Visual Relationship Detection using Semantic Modeling of Scene Descriptions

no code implementations1 Sep 2018 Stephan Baier, Yunpu Ma, Volker Tresp

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).

Link Prediction Object Detection +1

Improving Information Extraction from Images with Learned Semantic Models

no code implementations27 Aug 2018 Stephan Baier, Yunpu Ma, Volker Tresp

Many applications require an understanding of an image that goes beyond the simple detection and classification of its objects.

General Classification Visual Relationship Detection

Learning Goal-Oriented Visual Dialog via Tempered Policy Gradient

1 code implementation2 Jul 2018 Rui Zhao, Volker Tresp

Learning goal-oriented dialogues by means of deep reinforcement learning has recently become a popular research topic.

Policy Gradient Methods reinforcement-learning +1

Embedding Models for Episodic Knowledge Graphs

no code implementations30 Jun 2018 Yunpu Ma, Volker Tresp, Erik Daxberger

In this paper, we extend models for static knowledge graphs to temporal knowledge graphs.

Knowledge Graph Embeddings Knowledge Graphs

Semi-supervised Outlier Detection using Generative And Adversary Framework

no code implementations ICLR 2018 Jindong Gu, Matthias Schubert, Volker Tresp

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).

General Classification Multi-class Classification +1

Attention-based Information Fusion using Multi-Encoder-Decoder Recurrent Neural Networks

no code implementations13 Nov 2017 Stephan Baier, Sigurd Spieckermann, Volker Tresp

With the rising number of interconnected devices and sensors, modeling distributed sensor networks is of increasing interest.

Tensor Decompositions for Modeling Inverse Dynamics

no code implementations13 Nov 2017 Stephan Baier, Volker Tresp

The decomposition of sparse tensors has successfully been used in relational learning, e. g., the modeling of large knowledge graphs.

Knowledge Graphs Multi-class Classification +2

The Tensor Memory Hypothesis

no code implementations9 Aug 2017 Volker Tresp, Yunpu Ma

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

1 code implementation ICML 2017 Yinchong Yang, Denis Krompass, Volker Tresp

The Recurrent Neural Networks and their variants have shown promising performances in sequence modeling tasks such as Natural Language Processing.

Classification General Classification +1

Predictive Clinical Decision Support System with RNN Encoding and Tensor Decoding

no code implementations2 Dec 2016 Yinchong Yang, Peter A. Fasching, Markus Wallwiener, Tanja N. Fehm, Sara Y. Brucker, Volker Tresp

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

no code implementations8 Feb 2016 Cristóbal Esteban, Oliver Staeck, Yinchong Yang, Volker Tresp

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

no code implementations21 Dec 2015 Cristóbal Esteban, Volker Tresp, Yinchong Yang, Stephan Baier, Denis Krompaß

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.

Knowledge Graphs Representation Learning

Learning with Memory Embeddings

no code implementations25 Nov 2015 Volker Tresp, Cristóbal Esteban, Yinchong Yang, Stephan Baier, Denis Krompaß

We introduce a number of hypotheses on human memory that can be derived from the developed mathematical models.

Knowledge Graphs Representation Learning

Type-Constrained Representation Learning in Knowledge Graphs

no code implementations11 Aug 2015 Denis Krompaß, Stephan Baier, Volker Tresp

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.

Knowledge Graph Completion Link Prediction +2

A Review of Relational Machine Learning for Knowledge Graphs

2 code implementations2 Mar 2015 Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich

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).

Knowledge Graphs

Towards a New Science of a Clinical Data Intelligence

no code implementations17 Nov 2013 Volker Tresp, Sonja Zillner, Maria J. Costa, Yi Huang, Alexander Cavallaro, Peter A. Fasching, Andre Reis, Martin Sedlmayr, Thomas Ganslandt, Klemens Budde, Carl Hinrichs, Danilo Schmidt, Philipp Daumke, Daniel Sonntag, Thomas Wittenberg, Patricia G. Oppelt, Denis Krompass

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

no code implementations10 Jun 2013 Maximilian Nickel, Volker Tresp

Tensor factorizations have become increasingly popular approaches for various learning tasks on structured data.

Relational Reasoning

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