Search Results for author: Florian Schmidt

Found 13 papers, 3 papers with code

Towards AIOps in Edge Computing Environments

no code implementations12 Feb 2021 Soeren Becker, Florian Schmidt, Anton Gulenko, Alexander Acker, Odej Kao

Edge computing was introduced as a technical enabler for the demanding requirements of new network technologies like 5G.

Anomaly Detection Cloud Computing +2

Optimizing Convergence for Iterative Learning of ARIMA for Stationary Time Series

no code implementations25 Jan 2021 Kevin Styp-Rekowski, Florian Schmidt, Odej Kao

Forecasting of time series in continuous systems becomes an increasingly relevant task due to recent developments in IoT and 5G.

Time Series Time Series Analysis

Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper

no code implementations15 Jan 2021 Jasmin Bogatinovski, Sasho Nedelkoski, Alexander Acker, Florian Schmidt, Thorsten Wittkopp, Soeren Becker, Jorge Cardoso, Odej Kao

Finally, all this will result in faster adoption of AIOps, further increase the interest in this research field and contribute to bridging the gap towards fully-autonomous operating IT systems.

Decision Making Management

The voice of COVID-19: Acoustic correlates of infection

no code implementations17 Dec 2020 Katrin D. Bartl-Pokorny, Florian B. Pokorny, Anton Batliner, Shahin Amiriparian, Anastasia Semertzidou, Florian Eyben, Elena Kramer, Florian Schmidt, Rainer Schönweiler, Markus Wehler, Björn W. Schuller

Group differences in the front vowels /i:/ and /e:/ are additionally reflected in the variation of the fundamental frequency and the harmonics-to-noise ratio, group differences in back vowels /o:/ and /u:/ in statistics of the Mel-frequency cepstral coefficients and the spectral slope.

BERT as a Teacher: Contextual Embeddings for Sequence-Level Reward

no code implementations5 Mar 2020 Florian Schmidt, Thomas Hofmann

Measuring the quality of a generated sequence against a set of references is a central problem in many learning frameworks, be it to compute a score, to assign a reward, or to perform discrimination.

Generalization in Generation: A closer look at Exposure Bias

no code implementations WS 2019 Florian Schmidt

Exposure bias refers to the train-test discrepancy that seemingly arises when an autoregressive generative model uses only ground-truth contexts at training time but generated ones at test time.

Language Modelling reinforcement-learning +2

Autoregressive Text Generation Beyond Feedback Loops

1 code implementation IJCNLP 2019 Florian Schmidt, Stephan Mandt, Thomas Hofmann

Autoregressive state transitions, where predictions are conditioned on past predictions, are the predominant choice for both deterministic and stochastic sequential models.

Sentence Text Generation

Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic

no code implementations12 Oct 2018 Oleh Bodunov, Florian Schmidt, André Martin, Andrey Brito, Christof Fetzer

The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context.

Ensemble Learning General Classification

Deep State Space Models for Unconditional Word Generation

no code implementations NeurIPS 2018 Florian Schmidt, Thomas Hofmann

Autoregressive feedback is considered a necessity for successful unconditional text generation using stochastic sequence models.

Text Generation Variational Inference

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

no code implementations23 Apr 2018 Nicolas Weber, Florian Schmidt, Mathias Niepert, Felipe Huici

Neural network frameworks such as PyTorch and TensorFlow are the workhorses of numerous machine learning applications ranging from object recognition to machine translation.

Machine Translation Object Recognition +1

Representation Learning for Resource Usage Prediction

no code implementations2 Feb 2018 Florian Schmidt, Mathias Niepert, Felipe Huici

Creating a model of a computer system that can be used for tasks such as predicting future resource usage and detecting anomalies is a challenging problem.

Anomaly Detection Representation Learning

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