Search Results for author: Chen Qiu

Found 12 papers, 8 papers with code

A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs

1 code implementation CoNLL (EMNLP) 2021 Mareike Hartmann, Miryam de Lhoneux, Daniel Hershcovich, Yova Kementchedjhieva, Lukas Nielsen, Chen Qiu, Anders Søgaard

Negation is one of the most fundamental concepts in human cognition and language, and several natural language inference (NLI) probes have been designed to investigate pretrained language models’ ability to detect and reason with negation.

Benchmark Natural Language Inference +1

Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review

no code implementations2 Jul 2022 Hao Wang, Bin Guo, Yating Zeng, Yasan Ding, Chen Qiu, Ying Zhang, Lina Yao, Zhiwen Yu

The intelligent dialogue system, aiming at communicating with humans harmoniously with natural language, is brilliant for promoting the advancement of human-machine interaction in the era of artificial intelligence.

Raising the Bar in Graph-level Anomaly Detection

1 code implementation27 May 2022 Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph

Graph-level anomaly detection has become a critical topic in diverse areas, such as financial fraud detection and detecting anomalous activities in social networks.

Anomaly Detection Fraud Detection +1

Treatment Choice with Nonlinear Regret

no code implementations17 May 2022 Toru Kitagawa, Sokbae Lee, Chen Qiu

Focusing on mean square regret, we derive the closed-form probabilities of randomization for finite-sample Bayes and minimax optimal rules when data are normal with known variance.

Latent Outlier Exposure for Anomaly Detection with Contaminated Data

1 code implementation16 Feb 2022 Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt

We propose a strategy for training an anomaly detector in the presence of unlabeled anomalies that is compatible with a broad class of models.

Anomaly Detection

Detecting Anomalies within Time Series using Local Neural Transformations

1 code implementation8 Feb 2022 Tim Schneider, Chen Qiu, Marius Kloft, Decky Aspandi Latif, Steffen Staab, Stephan Mandt, Maja Rudolph

We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology.

Anomaly Detection Epidemiology +3

Switching Recurrent Kalman Networks

no code implementations16 Nov 2021 Giao Nguyen-Quynh, Philipp Becker, Chen Qiu, Maja Rudolph, Gerhard Neumann

In addition, driving data can often be multimodal in distribution, meaning that there are distinct predictions that are likely, but averaging can hurt model performance.

Autonomous Driving Time Series

On Language Models for Creoles

1 code implementation CoNLL (EMNLP) 2021 Heather Lent, Emanuele Bugliarello, Miryam de Lhoneux, Chen Qiu, Anders Søgaard

Creole languages such as Nigerian Pidgin English and Haitian Creole are under-resourced and largely ignored in the NLP literature.

Neural Transformation Learning for Deep Anomaly Detection Beyond Images

1 code implementation30 Mar 2021 Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph

Data transformations (e. g. rotations, reflections, and cropping) play an important role in self-supervised learning.

Anomaly Detection Self-Supervised Learning +1

Variational Dynamic Mixtures

no code implementations20 Oct 2020 Chen Qiu, Stephan Mandt, Maja Rudolph

Deep probabilistic time series forecasting models have become an integral part of machine learning.

Probabilistic Time Series Forecasting Time Series

Learning Topometric Semantic Maps from Occupancy Grids

1 code implementation10 Jan 2020 Markus Hiller, Chen Qiu, Florian Particke, Christian Hofmann, Jörn Thielecke

Today's mobile robots are expected to operate in complex environments they share with humans.

Rewarding Coreference Resolvers for Being Consistent with World Knowledge

1 code implementation IJCNLP 2019 Rahul Aralikatte, Heather Lent, Ana Valeria Gonzalez, Daniel Hershcovich, Chen Qiu, Anders Sandholm, Michael Ringaard, Anders Søgaard

Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples.

reinforcement-learning Relation Extraction

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