Thomas Hofmann

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ETH Zurich

Professor at ETH Zurich, Co-Founder and CTO at 1plusX

Generative Minimization Networks: Training GANs Without Competition

Many applications in machine learning can be framed as minimization prob...
03/23/2021 ∙ by Paulina Grnarova, et al. ∙ 31 ∙ share

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SwissDial: Parallel Multidialectal Corpus of Spoken Swiss German

Swiss German is a dialect continuum whose natively acquired dialects sig...
03/21/2021 ∙ by Pelin Dogan-Schönberger, et al. ∙ 0 ∙ share

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Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization

Viewing optimization methods as numerical integrators for ordinary diffe...
02/23/2021 ∙ by Peiyuan Zhang, et al. ∙ 0 ∙ share

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Convolutional Generation of Textured 3D Meshes

Recent generative models for 2D images achieve impressive visual results...
06/13/2020 ∙ by Dario Pavllo, et al. ∙ 7 ∙ share

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

Measuring the quality of a generated sequence against a set of reference...
03/05/2020 ∙ by Florian Schmidt, et al. ∙ 0 ∙ share

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Controlling Style and Semantics in Weakly-Supervised Image Generation

We propose a weakly-supervised approach for conditional image generation...
12/06/2019 ∙ by Dario Pavllo, et al. ∙ 20 ∙ share

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Mixing of Stochastic Accelerated Gradient Descent

We study the mixing properties for stochastic accelerated gradient desce...
10/31/2019 ∙ by Peiyuan Zhang, et al. ∙ 0 ∙ share

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LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games

While Reinforcement Learning (RL) approaches lead to significant achieve...
09/04/2019 ∙ by Leonard Adolphs, et al. ∙ 0 ∙ share

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Autoregressive Text Generation Beyond Feedback Loops

Autoregressive state transitions, where predictions are conditioned on p...
08/30/2019 ∙ by Florian Schmidt, et al. ∙ 0 ∙ share

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Cosmological N-body simulations: a challenge for scalable generative models

Deep generative models, such as Generative Adversarial Networks (GANs) o...
08/15/2019 ∙ by Nathanaël Perraudin, et al. ∙ 0 ∙ share

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Adversarial Training Generalizes Data-dependent Spectral Norm Regularization

We establish a theoretical link between adversarial training and operato...
06/04/2019 ∙ by Kevin Roth, et al. ∙ 12 ∙ share

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The Odds are Odd: A Statistical Test for Detecting Adversarial Examples

We investigate conditions under which test statistics exist that can rel...
02/13/2019 ∙ by Kevin Roth, et al. ∙ 18 ∙ share

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Evaluating GANs via Duality

Generative Adversarial Networks (GANs) have shown great results in accur...
11/13/2018 ∙ by Paulina Grnarova, et al. ∙ 6 ∙ share

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Learning and Evaluating Sparse Interpretable Sentence Embeddings

Previous research on word embeddings has shown that sparse representatio...
09/23/2018 ∙ by Valentin Trifonov, et al. ∙ 0 ∙ share

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End-to-End Neural Entity Linking

Entity Linking (EL) is an essential task for semantic text understanding...
08/23/2018 ∙ by Nikolaos Kolitsas, et al. ∙ 0 ∙ share

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A Distributed Second-Order Algorithm You Can Trust

Due to the rapid growth of data and computational resources, distributed...
06/20/2018 ∙ by Celestine Dünner, et al. ∙ 2 ∙ share

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Deep State Space Models for Unconditional Word Generation

Autoregressive feedback is considered a necessity for successful uncondi...
06/12/2018 ∙ by Florian Schmidt, et al. ∙ 0 ∙ share

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Towards a Theoretical Understanding of Batch Normalization

Normalization techniques such as Batch Normalization have been applied v...
05/27/2018 ∙ by Hadi Daneshmand, et al. ∙ 0 ∙ share

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Zero-Shot Dual Machine Translation

Neural Machine Translation (NMT) systems rely on large amounts of parall...
05/25/2018 ∙ by Lierni Sestorain, et al. ∙ 0 ∙ share

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Hyperbolic Neural Networks

Hyperbolic spaces have recently gained momentum in the context of machin...
05/23/2018 ∙ by Octavian-Eugen Ganea, et al. ∙ 0 ∙ share

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Adversarially Robust Training through Structured Gradient Regularization

We propose a novel data-dependent structured gradient regularizer to inc...
05/22/2018 ∙ by Kevin Roth, et al. ∙ 2 ∙ share

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Local Saddle Point Optimization: A Curvature Exploitation Approach

Gradient-based optimization methods are the most popular choice for find...
05/15/2018 ∙ by Leonard Adolphs, et al. ∙ 0 ∙ share

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Hyperbolic Entailment Cones for Learning Hierarchical Embeddings

Learning graph representations via low-dimensional embeddings that prese...
04/03/2018 ∙ by Octavian-Eugen Ganea, et al. ∙ 0 ∙ share

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Escaping Saddles with Stochastic Gradients

We analyze the variance of stochastic gradients along negative curvature...
03/15/2018 ∙ by Hadi Daneshmand, et al. ∙ 0 ∙ share

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Fast Cosmic Web Simulations with Generative Adversarial Networks

Dark matter in the universe evolves through gravity to form a complex ne...
01/27/2018 ∙ by Andres C Rodriguez, et al. ∙ 0 ∙ share

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The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels

Black-Box attacks on machine learning models occur when an attacker, des...
11/15/2017 ∙ by Yannic Kilcher, et al. ∙ 0 ∙ share

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Parametrizing filters of a CNN with a GAN

It is commonly agreed that the use of relevant invariances as a good sta...
10/31/2017 ∙ by Yannic Kilcher, et al. ∙ 0 ∙ share

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Flexible Prior Distributions for Deep Generative Models

We consider the problem of training generative models with deep neural n...
10/31/2017 ∙ by Yannic Kilcher, et al. ∙ 0 ∙ share

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Semantic Interpolation in Implicit Models

In implicit models, one often interpolates between sampled points in lat...
10/31/2017 ∙ by Yannic Kilcher, et al. ∙ 0 ∙ share

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Generator Reversal

We consider the problem of training generative models with deep neural n...
07/28/2017 ∙ by Yannic Kilcher, et al. ∙ 0 ∙ share

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Learning Aerial Image Segmentation from Online Maps

This study deals with semantic segmentation of high-resolution (aerial) ...
07/21/2017 ∙ by Pascal Kaiser, et al. ∙ 0 ∙ share

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Cosmological model discrimination with Deep Learning

We demonstrate the potential of Deep Learning methods for measurements o...
07/17/2017 ∙ by Jorit Schmelzle, et al. ∙ 0 ∙ share

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An Online Learning Approach to Generative Adversarial Networks

We consider the problem of training generative models with a Generative ...
06/10/2017 ∙ by Paulina Grnarova, et al. ∙ 0 ∙ share

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Stabilizing Training of Generative Adversarial Networks through Regularization

Deep generative models based on Generative Adversarial Networks (GANs) h...
05/25/2017 ∙ by Kevin Roth, et al. ∙ 0 ∙ share

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Deep Joint Entity Disambiguation with Local Neural Attention

We propose a novel deep learning model for joint document-level entity d...
04/17/2017 ∙ by Octavian-Eugen Ganea, et al. ∙ 0 ∙ share

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Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification

This paper presents a novel approach for multi-lingual sentiment classif...
03/07/2017 ∙ by Jan Deriu, et al. ∙ 0 ∙ share

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A Semi-supervised Framework for Image Captioning

State-of-the-art approaches for image captioning require supervised trai...
11/16/2016 ∙ by Wenhu Chen, et al. ∙ 0 ∙ share

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Fully Character-Level Neural Machine Translation without Explicit Segmentation

Most existing machine translation systems operate at the level of words,...
10/10/2016 ∙ by Jason Lee, et al. ∙ 0 ∙ share

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Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

Many fundamental problems in natural language processing rely on determi...
09/08/2015 ∙ by Octavian-Eugen Ganea, et al. ∙ 0 ∙ share

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Variance Reduced Stochastic Gradient Descent with Neighbors

Stochastic Gradient Descent (SGD) is a workhorse in machine learning, ye...
06/11/2015 ∙ by Thomas Hofmann, et al. ∙ 0 ∙ share

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Probabilistic Latent Semantic Analysis

Probabilistic Latent Semantic Analysis is a novel statistical technique ...
01/23/2013 ∙ by Thomas Hofmann, et al. ∙ 0 ∙ share

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Exponential Families for Conditional Random Fields

In this paper we de ne conditional random elds in reproducing kernel Hil...
07/11/2012 ∙ by Yasemin Altun, et al. ∙ 0 ∙ share

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Thomas Hofmann

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