no code implementations • 10 Feb 2024 • Fedor Borisyuk, Mingzhou Zhou, Qingquan Song, Siyu Zhu, Birjodh Tiwana, Ganesh Parameswaran, Siddharth Dangi, Lars Hertel, Qiang Xiao, Xiaochen Hou, Yunbo Ouyang, Aman Gupta, Sheallika Singh, Dan Liu, Hailing Cheng, Lei Le, Jonathan Hung, Sathiya Keerthi, Ruoyan Wang, Fengyu Zhang, Mohit Kothari, Chen Zhu, Daqi Sun, Yun Dai, Xun Luan, Sirou Zhu, Zhiwei Wang, Neil Daftary, Qianqi Shen, Chengming Jiang, Haichao Wei, Maneesh Varshney, Amol Ghoting, Souvik Ghosh
We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods.
no code implementations • 29 Jul 2020 • Lars Hertel, Pierre Baldi, Daniel L. Gillen
Reinforcement learning algorithms can show strong variation in performance between training runs with different random seeds.
1 code implementation • 8 May 2020 • Lars Hertel, Julian Collado, Peter Sadowski, Jordan Ott, Pierre Baldi
Sherpa is a hyperparameter optimization library for machine learning models.
no code implementations • 6 Oct 2017 • Lars Hertel, Erhardt Barth, Thomas Käster, Thomas Martinetz
Deep convolutional neural networks, trained on large datasets, achieve convincing results and are currently the state-of-the-art approach for this task.
Ranked #34 on Image Classification on MNIST
1 code implementation • 11 Jul 2016 • Lars Hertel, Huy Phan, Alfred Mertins
We trained a deep all-convolutional neural network with masked global pooling to perform single-label classification for acoustic scene classification and multi-label classification for domestic audio tagging in the DCASE-2016 contest.
no code implementations • 8 Jul 2016 • Huy Phan, Lars Hertel, Marco Maass, Philipp Koch, Alfred Mertins
This category taxonomy is then used in the feature extraction step in which an audio scene instance is represented by a label tree embedding image.
no code implementations • 8 Jul 2016 • Huy Phan, Lars Hertel, Marco Maass, Philipp Koch, Alfred Mertins
The regression phase is then carried out to let the positive audio segments vote for the event onsets and offsets, and therefore model the temporal structure of audio events.
no code implementations • 25 Jun 2016 • Huy Phan, Lars Hertel, Marco Maass, Philipp Koch, Alfred Mertins
We present in this paper an efficient approach for acoustic scene classification by exploring the structure of class labels.
no code implementations • 29 Apr 2016 • Huy Phan, Marco Maass, Lars Hertel, Radoslaw Mazur, Ian McLoughlin, Alfred Mertins
The entries of the descriptor are produced by evaluating a set of regressors on the input signal.
1 code implementation • 21 Apr 2016 • Huy Phan, Lars Hertel, Marco Maass, Alfred Mertins
We present in this paper a simple, yet efficient convolutional neural network (CNN) architecture for robust audio event recognition.
no code implementations • 18 Mar 2016 • Lars Hertel, Huy Phan, Alfred Mertins
Recognizing acoustic events is an intricate problem for a machine and an emerging field of research.