3 code implementations • 6 Nov 2024 • Niklas Schmidinger, Lisa Schneckenreiter, Philipp Seidl, Johannes Schimunek, Pieter-Jan Hoedt, Johannes Brandstetter, Andreas Mayr, Sohvi Luukkonen, Sepp Hochreiter, Günter Klambauer
While Transformers have yielded impressive results, their quadratic runtime dependency on the sequence length complicates their use for long genomic sequences and in-context learning on proteins and chemical sequences.
1 code implementation • 29 Oct 2024 • Thomas Schmied, Thomas Adler, Vihang Patil, Maximilian Beck, Korbinian Pöppel, Johannes Brandstetter, Günter Klambauer, Razvan Pascanu, Sepp Hochreiter
In recent years, there has been a trend in the field of Reinforcement Learning (RL) towards large action models trained offline on large-scale datasets via sequence modeling.
1 code implementation • 17 Oct 2024 • Kajetan Schweighofer, Adrian Arnaiz-Rodriguez, Sepp Hochreiter, Nuria Oliver
Furthermore, we identify the per-group difference in predictive diversity of ensemble members as the potential cause of the disparate benefits effect.
1 code implementation • 14 Oct 2024 • Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, Sepp Hochreiter
Reliable estimation of predictive uncertainty is crucial for machine learning applications, particularly in high-stakes scenarios where hedging against risks is essential.
1 code implementation • 9 Oct 2024 • Thomas Schmied, Fabian Paischer, Vihang Patil, Markus Hofmarcher, Razvan Pascanu, Sepp Hochreiter
Furthermore, we illuminate the limitations of current in-context RL methods on complex environments and discuss future directions.
2 code implementations • 9 Oct 2024 • Fabian Paischer, Lukas Hauzenberger, Thomas Schmied, Benedikt Alkin, Marc Peter Deisenroth, Sepp Hochreiter
Then, we initialize the LoRA matrices with the obtained right-singular vectors and re-distribute ranks among all weight matrices to explain the maximal amount of variance and continue the standard LoRA fine-tuning procedure.
no code implementations • 1 Oct 2024 • Vihang Patil, Markus Hofmarcher, Elisabeth Rumetshofer, Sepp Hochreiter
Contrastive abstraction learning first constructs clusters of state representations by contrastive learning and then applies modern Hopfield networks to determine the abstract states.
no code implementations • 1 Oct 2024 • Vihang Patil, Andreas Radler, Daniel Klotz, Sepp Hochreiter
Our method encodes the input image with a Convolutional Neural Network and then uses a branch of alternating Convolution and MaxPool layers to create specialized sub-networks and extract primitive slots.
no code implementations • 13 Jun 2024 • Wei Lin, Muhammad Jehanzeb Mirza, Sivan Doveh, Rogerio Feris, Raja Giryes, Sepp Hochreiter, Leonid Karlinsky
Comparing two images in terms of Commonalities and Differences (CaD) is a fundamental human capability that forms the basis of advanced visual reasoning and interpretation.
2 code implementations • 6 Jun 2024 • Benedikt Alkin, Maximilian Beck, Korbinian Pöppel, Sepp Hochreiter, Johannes Brandstetter
Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing.
1 code implementation • 6 Jun 2024 • Lukas Aichberger, Kajetan Schweighofer, Mykyta Ielanskyi, Sepp Hochreiter
Current LLMs generate text in an autoregressive fashion by predicting and appending text tokens.
1 code implementation • 3 Jun 2024 • Sebastian Sanokowski, Sepp Hochreiter, Sebastian Lehner
Learning to sample from intractable distributions over discrete sets without relying on corresponding training data is a central problem in a wide range of fields, including Combinatorial Optimization.
1 code implementation • 30 May 2024 • Ajay Patel, Markus Hofmarcher, Claudiu Leoveanu-Condrei, Marius-Constantin Dinu, Chris Callison-Burch, Sepp Hochreiter
Recent research has also demonstrated LLMs have the capability to exceed their base performance through self-improvement, i. e. fine-tuning on data generated by the model itself.
1 code implementation • 14 May 2024 • Claus Hofmann, Simon Schmid, Bernhard Lehner, Daniel Klotz, Sepp Hochreiter
Out-of-distribution (OOD) detection is critical when deploying machine learning models in the real world.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
3 code implementations • 7 May 2024 • Maximilian Beck, Korbinian Pöppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter
In the 1990s, the constant error carousel and gating were introduced as the central ideas of the Long Short-Term Memory (LSTM).
1 code implementation • 10 Apr 2024 • Florian Sestak, Lisa Schneckenreiter, Johannes Brandstetter, Sepp Hochreiter, Andreas Mayr, Günter Klambauer
However, the performance of GNNs at binding site identification is still limited potentially due to the lack of dedicated nodes that model hidden geometric entities, such as binding pockets.
1 code implementation • 21 Feb 2024 • Arturs Berzins, Andreas Radler, Eric Volkmann, Sebastian Sanokowski, Sepp Hochreiter, Johannes Brandstetter
Geometry is a ubiquitous tool in computer graphics, design, and engineering.
1 code implementation • 21 Feb 2024 • Lukas Gruber, Markus Holzleitner, Johannes Lehner, Sepp Hochreiter, Werner Zellinger
Estimating the ratio of two probability densities from finitely many samples, is a central task in machine learning and statistics.
2 code implementations • 15 Feb 2024 • Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter, Johannes Brandstetter
We introduce MIM (Masked Image Modeling)-Refiner, a contrastive learning boost for pre-trained MIM models.
Ranked #2 on Image Clustering on ImageNet
3 code implementations • 1 Feb 2024 • Marius-Constantin Dinu, Claudiu Leoveanu-Condrei, Markus Holzleitner, Werner Zellinger, Sepp Hochreiter
Through these operations based on in-context learning our framework enables the creation and evaluation of explainable computational graphs.
1 code implementation • NeurIPS 2023 • Sebastian Sanokowski, Wilhelm Berghammer, Sepp Hochreiter, Sebastian Lehner
Several recent unsupervised learning methods use probabilistic approaches to solve combinatorial optimization (CO) problems based on the assumption of statistically independent solution variables.
no code implementations • 14 Nov 2023 • Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, Sepp Hochreiter
Our analyses show that the current measure erroneously assumes that the BMA predictive distribution is equivalent to the predictive distribution of the true model that generated the dataset.
no code implementations • 4 Oct 2023 • Bernhard Nessler, Thomas Doms, Sepp Hochreiter
The authors are concerned about the safety, health, and rights of the European citizens due to inadequate measures and procedures required by the current draft of the EU Artificial Intelligence (AI) Act for the conformity assessment of AI systems.
1 code implementation • 10 Jul 2023 • Fabian Paischer, Markus Hofmarcher, Sepp Hochreiter, Thomas Adler
We propose a more efficient training protocol that fits a linear mapping between image and text embeddings of CLIP via a closed-form solution.
1 code implementation • NeurIPS 2023 • Thomas Schmied, Markus Hofmarcher, Fabian Paischer, Razvan Pascanu, Sepp Hochreiter
That is, the performance on the pre-training tasks deteriorates when fine-tuning on new tasks.
1 code implementation • NeurIPS 2023 • Fabian Paischer, Thomas Adler, Markus Hofmarcher, Sepp Hochreiter
Then we feed these tokens to a pretrained language model that serves the agent as memory and provides it with a coherent and human-readable representation of the past.
1 code implementation • 2 May 2023 • Marius-Constantin Dinu, Markus Holzleitner, Maximilian Beck, Hoan Duc Nguyen, Andrea Huber, Hamid Eghbal-zadeh, Bernhard A. Moser, Sergei Pereverzyev, Sepp Hochreiter, Werner Zellinger
Our method outperforms deep embedded validation (DEV) and importance weighted validation (IWV) on all datasets, setting a new state-of-the-art performance for solving parameter choice issues in unsupervised domain adaptation with theoretical error guarantees.
1 code implementation • 24 Apr 2023 • Johannes Schimunek, Philipp Seidl, Lukas Friedrich, Daniel Kuhn, Friedrich Rippmann, Sepp Hochreiter, Günter Klambauer
Our novel concept for molecule representation enrichment is to associate molecules from both the support set and the query set with a large set of reference (context) molecules through a Modern Hopfield Network.
1 code implementation • 20 Apr 2023 • Johannes Lehner, Benedikt Alkin, Andreas Fürst, Elisabeth Rumetshofer, Lukas Miklautz, Sepp Hochreiter
In this work, we study how to combine the efficiency and scalability of MIM with the ability of ID to perform downstream classification in the absence of large amounts of labeled data.
Ranked #1 on Image Clustering on Imagenet-dog-15 (using extra training data)
1 code implementation • NeurIPS 2023 • Andreas Auer, Martin Gauch, Daniel Klotz, Sepp Hochreiter
To quantify uncertainty, conformal prediction methods are gaining continuously more interest and have already been successfully applied to various domains.
1 code implementation • 14 Mar 2023 • Moritz Neun, Christian Eichenberger, Henry Martin, Markus Spanring, Rahul Siripurapu, Daniel Springer, Leyan Deng, Chenwang Wu, Defu Lian, Min Zhou, Martin Lumiste, Andrei Ilie, Xinhua Wu, Cheng Lyu, Qing-Long Lu, Vishal Mahajan, Yichao Lu, Jiezhang Li, Junjun Li, Yue-Jiao Gong, Florian Grötschla, Joël Mathys, Ye Wei, He Haitao, Hui Fang, Kevin Malm, Fei Tang, Michael Kopp, David Kreil, Sepp Hochreiter
We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task.
1 code implementation • 6 Mar 2023 • Philipp Seidl, Andreu Vall, Sepp Hochreiter, Günter Klambauer
Activity and property prediction models are the central workhorses in drug discovery and materials sciences, but currently they have to be trained or fine-tuned for new tasks.
no code implementations • 17 Feb 2023 • Bernhard Schäfl, Lukas Gruber, Johannes Brandstetter, Sepp Hochreiter
Graph neural networks (GNNs) have evolved into one of the most popular deep learning architectures.
1 code implementation • 8 Aug 2022 • Yonghao Xu, Weikang Yu, Pedram Ghamisi, Michael Kopp, Sepp Hochreiter
To better evaluate the realism and semantic consistency of the generated images, we further conduct zero-shot classification on real remote sensing data using the classification model trained on synthesized images.
1 code implementation • 12 Jul 2022 • Christian Steinparz, Thomas Schmied, Fabian Paischer, Marius-Constantin Dinu, Vihang Patil, Angela Bitto-Nemling, Hamid Eghbal-zadeh, Sepp Hochreiter
Therefore, exploration strategies and learning methods are required that are capable of tracking the steady domain shifts, and adapting to them.
1 code implementation • 7 Jun 2022 • Martin Gauch, Maximilian Beck, Thomas Adler, Dmytro Kotsur, Stefan Fiel, Hamid Eghbal-zadeh, Johannes Brandstetter, Johannes Kofler, Markus Holzleitner, Werner Zellinger, Daniel Klotz, Sepp Hochreiter, Sebastian Lehner
We introduce SubGD, a novel few-shot learning method which is based on the recent finding that stochastic gradient descent updates tend to live in a low-dimensional parameter subspace.
no code implementations • 2 Jun 2022 • Mathias Lechner, Ramin Hasani, Zahra Babaiee, Radu Grosu, Daniela Rus, Thomas A. Henzinger, Sepp Hochreiter
Residual mappings have been shown to perform representation learning in the first layers and iterative feature refinement in higher layers.
1 code implementation • 1 Jun 2022 • Bernhard Schäfl, Lukas Gruber, Angela Bitto-Nemling, Sepp Hochreiter
In experiments on small-sized tabular datasets with less than 1, 000 samples, Hopular surpasses Gradient Boosting, Random Forests, SVMs, and in particular several Deep Learning methods.
Ranked #1 on General Classification on Shrutime
2 code implementations • 24 May 2022 • Fabian Paischer, Thomas Adler, Vihang Patil, Angela Bitto-Nemling, Markus Holzleitner, Sebastian Lehner, Hamid Eghbal-zadeh, Sepp Hochreiter
We propose to utilize a frozen Pretrained Language Transformer (PLT) for history representation and compression to improve sample efficiency.
1 code implementation • 31 Mar 2022 • Christian Eichenberger, Moritz Neun, Henry Martin, Pedro Herruzo, Markus Spanring, Yichao Lu, Sungbin Choi, Vsevolod Konyakhin, Nina Lukashina, Aleksei Shpilman, Nina Wiedemann, Martin Raubal, Bo wang, Hai L. Vu, Reza Mohajerpoor, Chen Cai, Inhi Kim, Luca Hermes, Andrew Melnik, Riza Velioglu, Markus Vieth, Malte Schilling, Alabi Bojesomo, Hasan Al Marzouqi, Panos Liatsis, Jay Santokhi, Dylan Hillier, Yiming Yang, Joned Sarwar, Anna Jordan, Emil Hewage, David Jonietz, Fei Tang, Aleksandra Gruca, Michael Kopp, David Kreil, Sepp Hochreiter
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins.
1 code implementation • Journal of Chemical Information and Modeling 2022 • Philipp Seidl, Philipp Renz, Natalia Dyubankova, Paulo Neves, Jonas Verhoeven, Jörg K. Wegner, Marwin Segler, Sepp Hochreiter, and Günter Klambauer
Finding synthesis routes for molecules of interest is essential in the discovery of new drugs and materials.
Ranked #26 on Single-step retrosynthesis on USPTO-50k
2 code implementations • 8 Nov 2021 • Kajetan Schweighofer, Andreas Radler, Marius-Constantin Dinu, Markus Hofmarcher, Vihang Patil, Angela Bitto-Nemling, Hamid Eghbal-zadeh, Sepp Hochreiter
The dataset characteristics are determined by the behavioral policy that samples this dataset.
1 code implementation • 21 Oct 2021 • Andreas Fürst, Elisabeth Rumetshofer, Johannes Lehner, Viet Tran, Fei Tang, Hubert Ramsauer, David Kreil, Michael Kopp, Günter Klambauer, Angela Bitto-Nemling, Sepp Hochreiter
We suggest to use modern Hopfield networks to tackle the problem of explaining away.
1 code implementation • 21 Jun 2021 • Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter
However, it is notoriously difficult to integrate them into machine learning approaches due to their heterogeneity with respect to size and orientation.
1 code implementation • 4 May 2021 • Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter
Recently, the application of machine learning models has gained momentum in natural sciences and engineering, which is a natural fit due to the abundance of data in these fields.
1 code implementation • 7 Apr 2021 • Philipp Seidl, Philipp Renz, Natalia Dyubankova, Paulo Neves, Jonas Verhoeven, Marwin Segler, Jörg K. Wegner, Sepp Hochreiter, Günter Klambauer
Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials.
no code implementations • 31 Mar 2021 • Philip Matthias Winter, Sebastian Eder, Johannes Weissenböck, Christoph Schwald, Thomas Doms, Tom Vogt, Sepp Hochreiter, Bernhard Nessler
Artificial Intelligence is one of the fastest growing technologies of the 21st century and accompanies us in our daily lives when interacting with technical applications.
1 code implementation • 13 Jan 2021 • Pieter-Jan Hoedt, Frederik Kratzert, Daniel Klotz, Christina Halmich, Markus Holzleitner, Grey Nearing, Sepp Hochreiter, Günter Klambauer
MC-LSTMs set a new state-of-the-art for neural arithmetic units at learning arithmetic operations, such as addition tasks, which have a strong conservation law, as the sum is constant over time.
no code implementations • 15 Dec 2020 • Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Günter Klambauer, Sepp Hochreiter, Grey Nearing
Deep Learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales.
no code implementations • 2 Dec 2020 • Markus Holzleitner, Lukas Gruber, José Arjona-Medina, Johannes Brandstetter, Sepp Hochreiter
We prove under commonly used assumptions the convergence of actor-critic reinforcement learning algorithms, which simultaneously learn a policy function, the actor, and a value function, the critic.
1 code implementation • 15 Oct 2020 • Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Nearing, Jimmy Lin, Sepp Hochreiter
Compared to naive prediction with a distinct LSTM per timescale, the multi-timescale architectures are computationally more efficient with no loss in accuracy.
2 code implementations • 13 Oct 2020 • Thomas Adler, Johannes Brandstetter, Michael Widrich, Andreas Mayr, David Kreil, Michael Kopp, Günter Klambauer, Sepp Hochreiter
On the few-shot datasets miniImagenet and tieredImagenet with small domain shifts, CHEF is competitive with state-of-the-art methods.
1 code implementation • 29 Sep 2020 • Vihang P. Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M. Blies, Johannes Brandstetter, Jose A. Arjona-Medina, Sepp Hochreiter
For such complex tasks, the recently proposed RUDDER uses reward redistribution to leverage steps in the Q-function that are associated with accomplishing sub-tasks.
2 code implementations • ICLR 2021 • Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Thomas Adler, Lukas Gruber, Markus Holzleitner, Milena Pavlović, Geir Kjetil Sandve, Victor Greiff, David Kreil, Michael Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter
The new update rule is equivalent to the attention mechanism used in transformers.
Immune Repertoire Classification Multiple Instance Learning +1
1 code implementation • NeurIPS 2020 • Michael Widrich, Bernhard Schäfl, Hubert Ramsauer, Milena Pavlović, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer
We show that the attention mechanism of transformer architectures is actually the update rule of modern Hopfield networks that can store exponentially many patterns.
1 code implementation • 25 Mar 2020 • Markus Hofmarcher, Andreas Mayr, Elisabeth Rumetshofer, Peter Ruch, Philipp Renz, Johannes Schimunek, Philipp Seidl, Andreu Vall, Michael Widrich, Sepp Hochreiter, Günter Klambauer
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs.
no code implementations • 14 Nov 2019 • Susanne Kimeswenger, Elisabeth Rumetshofer, Markus Hofmarcher, Philipp Tschandl, Harald Kittler, Sepp Hochreiter, Wolfram Hötzenecker, Günter Klambauer
The aim of this study is to evaluate whether it is possible to detect basal cell carcinomas in histological sections using attention-based deep learning models and to overcome the ultra-high resolution and the weak labels of whole slide images.
no code implementations • 10 Nov 2019 • Frederik Kratzert, Daniel Klotz, Johannes Brandstetter, Pieter-Jan Hoedt, Grey Nearing, Sepp Hochreiter
Climate change affects occurrences of floods and droughts worldwide.
no code implementations • 30 Oct 2019 • Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler, Sepp Hochreiter
In this work, we show that machine learning models can provide significant improvement over random search.
no code implementations • 9 Oct 2019 • Johannes Lehner, Andreas Mitterecker, Thomas Adler, Markus Hofmarcher, Bernhard Nessler, Sepp Hochreiter
We introduce Patch Refinement a two-stage model for accurate 3D object detection and localization from point cloud data.
Ranked #1 on Object Detection on KITTI Cars Moderate
no code implementations • 25 Sep 2019 • Leila Arras, Jose A. Arjona-Medina, Michael Widrich, Grégoire Montavon, Michael Gillhofer, Klaus-Robert Müller, Sepp Hochreiter, Wojciech Samek
While neural networks have acted as a strong unifying force in the design of modern AI systems, the neural network architectures themselves remain highly heterogeneous due to the variety of tasks to be solved.
no code implementations • NeurIPS Workshop Deep_Invers 2019 • Michael Gillhofer, Hubert Ramsauer, Johannes Brandstetter, Bernhard Schäfl, Sepp Hochreiter
We propose a GAN based approach to solve inverse problems which have non-differential or non-continuous forward relations.
1 code implementation • 19 Jul 2019 • Frederik Kratzert, Daniel Klotz, Guy Shalev, Günter Klambauer, Sepp Hochreiter, Grey Nearing
The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone.
1 code implementation • ICLR 2019 • Elisabeth Rumetshofer, Markus Hofmarcher, Clemens Röhrl, Sepp Hochreiter, Günter Klambauer
We present the largest comparison of CNN architectures including GapNet-PL for protein localization in HTI images of human cells.
no code implementations • ICLR 2019 • Markus Hofmarcher, Elisabeth Rumetshofer, Sepp Hochreiter, Günter Klambauer
Surprisingly, we could predict 29% of the 209 pharmacological assays at high predictive performance (AUC > 0. 9).
no code implementations • 19 Mar 2019 • Frederik Kratzert, Mathew Herrnegger, Daniel Klotz, Sepp Hochreiter, Günter Klambauer
LSTMs are particularly well-suited for this problem since memory cells can represent dynamic reservoirs and storages, which are essential components in state-space modelling approaches of the hydrological system.
1 code implementation • 7 Mar 2019 • Kristina Preuer, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter, Thomas Unterthiner
Without any means of interpretation, neural networks that predict molecular properties and bioactivities are merely black boxes.
2 code implementations • NeurIPS 2019 • Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, Sepp Hochreiter
In MDPs the Q-values are equal to the expected immediate reward plus the expected future rewards.
Ranked #9 on Atari Games on Atari 2600 Bowling
2 code implementations • 26 Mar 2018 • Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, Günter Klambauer
We propose a novel distance measure between two sets of molecules, called Fr\'echet ChemNet distance (FCD), that can be used as an evaluation metric for generative models.
1 code implementation • ICML 2018 • Calvin Seward, Thomas Unterthiner, Urs Bergmann, Nikolay Jetchev, Sepp Hochreiter
To formally describe an optimal update direction, we introduce a theoretical framework which allows the derivation of requirements on both the divergence and corresponding method for determining an update direction, with these requirements guaranteeing unbiased mini-batch updates in the direction of steepest descent.
Ranked #3 on Image Generation on LSUN Bedroom 64 x 64
1 code implementation • Bioinformatics 2017 • Kristina Preuer, Richard P I Lewis, Sepp Hochreiter, Andreas Bender, Krishna C Bulusu, Günter Klambauer
While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space.
1 code implementation • ICLR 2018 • Thomas Unterthiner, Bernhard Nessler, Calvin Seward, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter
We prove that Coulomb GANs possess only one Nash equilibrium which is optimal in the sense that the model distribution equals the target distribution.
69 code implementations • NeurIPS 2017 • Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter
Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible.
Ranked #2 on Image Generation on LSUN Bedroom 64 x 64
13 code implementations • NeurIPS 2017 • Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations.
Ranked #8 on Drug Discovery on Tox21
2 code implementations • 1 Jun 2016 • Michael Treml, Jose A. Arjona-Medina, Thomas Unterthiner, Rupesh Durgesh, Felix Friedmann, Peter Schuberth, Andreas Mayr, Martin Heusel, Markus Hofmarcher, Michael Widrich, Bernhard Nessler, Sepp Hochreiter
We propose a novel deep network architecture for image segmentation that keeps the high accuracy while being efficient enough for embedded devices.
16 code implementations • 23 Nov 2015 • Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter
In contrast to ReLUs, ELUs have negative values which allows them to push mean unit activations closer to zero like batch normalization but with lower computational complexity.
Ranked #143 on Image Classification on CIFAR-100 (using extra training data)
1 code implementation • 4 Mar 2015 • Thomas Unterthiner, Andreas Mayr, Günter Klambauer, Sepp Hochreiter
The goal of this challenge was to assess the performance of computational methods in predicting the toxicity of chemical compounds.
no code implementations • NeurIPS 2015 • Djork-Arné Clevert, Andreas Mayr, Thomas Unterthiner, Sepp Hochreiter
We proof convergence and correctness of the RFN learning algorithm.