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ORCIDThomas Hofmann
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- affiliation: ETH Zurich, Switzerland
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2020 – today
- 2024
[j25]Marc Pinski
, Thomas Hofmann, Alexander Benlian:
AI Literacy for the top management: An upper echelons perspective on corporate AI orientation and implementation ability. Electron. Mark. 34(1): 24 (2024)- [c135]Anton Schäfer, Thomas Hofmann, Imanol Schlag, Tiago Pimentel:
On the Effect of (Near) Duplicate Subwords in Language Modelling. ACL (Findings) 2024: 9580-9597 - [c134]Pietro Lesci, Clara Meister, Thomas Hofmann, Andreas Vlachos, Tiago Pimentel:
Causal Estimation of Memorisation Profiles. ACL (1) 2024: 15616-15635 - [c133]Kai Lion, Lorenzo Noci, Thomas Hofmann, Gregor Bachmann:
How Good is a Single Basin? AISTATS 2024: 4015-4023 - [c132]Piera Riccio, Thomas Hofmann, Nuria Oliver:
Exposed or Erased: Algorithmic Censorship of Nudity in Art. CHI 2024: 26:1-26:17 - [c131]Bobby He, Thomas Hofmann:
Simplifying Transformer Blocks. ICLR 2024 - [c130]Moritz Imfeld, Jacopo Graldi, Marco Giordano, Thomas Hofmann, Sotiris Anagnostidis, Sidak Pal Singh:
Transformer Fusion with Optimal Transport. ICLR 2024 - [c129]Alexander Theus, Olin Geimer, Friedrich Wicke, Thomas Hofmann, Sotiris Anagnostidis, Sidak Pal Singh:
Towards Meta-Pruning via Optimal Transport. ICLR 2024 - [c128]Sotiris Anagnostidis, Gregor Bachmann, Imanol Schlag, Thomas Hofmann:
Navigating Scaling Laws: Compute Optimality in Adaptive Model Training. ICML 2024 - [c127]Yuhui Ding, Antonio Orvieto, Bobby He, Thomas Hofmann:
Recurrent Distance Filtering for Graph Representation Learning. ICML 2024 - [c126]Dimitri von Rütte, Sotiris Anagnostidis, Gregor Bachmann, Thomas Hofmann:
A Language Model's Guide Through Latent Space. ICML 2024 - [i95]Michael Hersche, Francesco Di Stefano, Thomas Hofmann, Abu Sebastian, Abbas Rahimi:
Probabilistic Abduction for Visual Abstract Reasoning via Learning Rules in Vector-symbolic Architectures. CoRR abs/2401.16024 (2024) - [i94]Kai Lion, Lorenzo Noci, Thomas Hofmann, Gregor Bachmann:
How Good is a Single Basin? CoRR abs/2402.03187 (2024) - [i93]Alexander Theus, Olin Geimer, Friedrich Wicke, Thomas Hofmann, Sotiris Anagnostidis, Sidak Pal Singh:
Towards Meta-Pruning via Optimal Transport. CoRR abs/2402.07839 (2024) - [i92]Dimitri von Rütte, Sotiris Anagnostidis, Gregor Bachmann, Thomas Hofmann:
A Language Model's Guide Through Latent Space. CoRR abs/2402.14433 (2024) - [i91]Lorenzo Noci, Alexandru Meterez, Thomas Hofmann, Antonio Orvieto:
Why do Learning Rates Transfer? Reconciling Optimization and Scaling Limits for Deep Learning. CoRR abs/2402.17457 (2024) - [i90]Sidak Pal Singh, Bobby He, Thomas Hofmann, Bernhard Schölkopf:
Hallmarks of Optimization Trajectories in Neural Networks and LLMs: The Lengths, Bends, and Dead Ends. CoRR abs/2403.07379 (2024) - [i89]Anton Schäfer, Thomas Hofmann, Imanol Schlag, Tiago Pimentel:
On the Effect of (Near) Duplicate Subwords in Language Modelling. CoRR abs/2404.06508 (2024) - [i88]Anton Schäfer, Shauli Ravfogel, Thomas Hofmann, Tiago Pimentel, Imanol Schlag:
Language Imbalance Can Boost Cross-lingual Generalisation. CoRR abs/2404.07982 (2024) - [i87]Maria Mihaela Trusca, Wolf Nuyts, Jonathan Thomm, Robert Honig, Thomas Hofmann, Tinne Tuytelaars, Marie-Francine Moens:
Object-Attribute Binding in Text-to-Image Generation: Evaluation and Control. CoRR abs/2404.13766 (2024) - [i86]Bobby He, Lorenzo Noci, Daniele Paliotta, Imanol Schlag, Thomas Hofmann:
Understanding and Minimising Outlier Features in Neural Network Training. CoRR abs/2405.19279 (2024) - [i85]Pietro Lesci, Clara Meister, Thomas Hofmann, Andreas Vlachos, Tiago Pimentel:
Causal Estimation of Memorisation Profiles. CoRR abs/2406.04327 (2024) - [i84]Jovan Andonov, Octavian Ganea, Paulina Grnarova, Gary Bécigneul, Thomas Hofmann:
Explicit Word Density Estimation for Language Modelling. CoRR abs/2406.10256 (2024) - [i83]Sidak Pal Singh, Linara Adilova, Michael Kamp, Asja Fischer, Bernhard Schölkopf, Thomas Hofmann:
Landscaping Linear Mode Connectivity. CoRR abs/2406.16300 (2024) - [i82]Giulia Lanzillotta, Sidak Pal Singh, Benjamin F. Grewe, Thomas Hofmann:
Local vs Global continual learning. CoRR abs/2407.16611 (2024) - 2023
- [j24]Alessandro Nicoli, Franziska Haag, Patrick Marcinek, Ruiming He, Johanna Kreißl, Jörg Stein, Alessandro Marchetto, Andreas Dunkel, Thomas Hofmann, Dietmar Krautwurst, Antonella Di Pizio:
Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1. J. Chem. Inf. Model. 63(7): 2014-2029 (2023) - [c125]Alexandros Delitzas, Maria Parelli, Nikolas Hars, Georgios Vlassis, Sotirios-Konstantinos Anagnostidis, Gregor Bachmann, Thomas Hofmann:
Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes. BMVC 2023: 748-749 - [c124]Maria Parelli, Alexandros Delitzas, Nikolas Hars, Georgios Vlassis, Sotiris Anagnostidis, Gregor Bachmann, Thomas Hofmann:
CLIP-Guided Vision-Language Pre-training for Question Answering in 3D Scenes. CVPR Workshops 2023: 5607-5612 - [c123]Sanghwan Kim, Lorenzo Noci, Antonio Orvieto, Thomas Hofmann:
Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning. CVPR 2023: 11930-11939 - [c122]Sotiris Anagnostidis, Aurélien Lucchi, Thomas Hofmann:
Mastering Spatial Graph Prediction of Road Networks. ICCV 2023: 5385-5395 - [c121]Sotiris Anagnostidis, Gregor Bachmann, Lorenzo Noci, Thomas Hofmann:
The Curious Case of Benign Memorization. ICLR 2023 - [c120]Dimitri von Rütte, Luca Biggio, Yannic Kilcher, Thomas Hofmann:
FIGARO: Controllable Music Generation using Learned and Expert Features. ICLR 2023 - [c119]Felix Sarnthein, Gregor Bachmann, Sotiris Anagnostidis, Thomas Hofmann:
Random Teachers are Good Teachers. ICML 2023: 30022-30041 - [c118]Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf:
The Hessian perspective into the Nature of Convolutional Neural Networks. ICML 2023: 31930-31968 - [c117]Enea Monzio Compagnoni, Anna Scampicchio, Luca Biggio, Antonio Orvieto, Thomas Hofmann, Josef Teichmann:
On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics. IJCNN 2023: 1-8 - [c116]Sotiris Anagnostidis, Dario Pavllo, Luca Biggio, Lorenzo Noci, Aurélien Lucchi, Thomas Hofmann:
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers. NeurIPS 2023 - [c115]Gregor Bachmann, Sotiris Anagnostidis, Thomas Hofmann:
Scaling MLPs: A Tale of Inductive Bias. NeurIPS 2023 - [c114]Lorenzo Noci, Chuning Li, Mufan Bill Li, Bobby He, Thomas Hofmann, Chris J. Maddison, Dan Roy:
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit. NeurIPS 2023 - [c113]Marc Pinski, Thomas Hofmann, Alexander Benlian:
Executive AI Literacy: A Text-Mining Approach to Understand Existing and Demanded AI Skills of Leaders in Unicorn Firms. Wirtschaftsinformatik 2023: 7 - [i81]Felix Sarnthein, Gregor Bachmann, Sotiris Anagnostidis, Thomas Hofmann:
Random Teachers are Good Teachers. CoRR abs/2302.12091 (2023) - [i80]Sanghwan Kim, Lorenzo Noci, Antonio Orvieto, Thomas Hofmann:
Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning. CoRR abs/2303.09483 (2023) - [i79]Maria Parelli, Alexandros Delitzas, Nikolas Hars, Georgios Vlassis, Sotiris Anagnostidis, Gregor Bachmann, Thomas Hofmann:
CLIP-Guided Vision-Language Pre-training for Question Answering in 3D Scenes. CoRR abs/2304.06061 (2023) - [i78]Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf:
The Hessian perspective into the Nature of Convolutional Neural Networks. CoRR abs/2305.09088 (2023) - [i77]Sotiris Anagnostidis, Dario Pavllo, Luca Biggio, Lorenzo Noci, Aurélien Lucchi, Thomas Hofmann:
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers. CoRR abs/2305.15805 (2023) - [i76]Alexandros Delitzas, Maria Parelli, Nikolas Hars, Georgios Vlassis, Sotiris Anagnostidis, Gregor Bachmann, Thomas Hofmann:
Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes. CoRR abs/2306.02329 (2023) - [i75]Gregor Bachmann, Sotiris Anagnostidis, Thomas Hofmann:
Scaling MLPs: A Tale of Inductive Bias. CoRR abs/2306.13575 (2023) - [i74]Lorenzo Noci, Chuning Li, Mufan Bill Li, Bobby He, Thomas Hofmann, Chris J. Maddison, Daniel M. Roy:
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit. CoRR abs/2306.17759 (2023) - [i73]Aleksandar Stanic, Dylan R. Ashley, Oleg Serikov, Louis Kirsch, Francesco Faccio, Jürgen Schmidhuber, Thomas Hofmann, Imanol Schlag:
The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute. CoRR abs/2309.11197 (2023) - [i72]Giulia Lanzillotta, Sidak Pal Singh, Benjamin F. Grewe, Thomas Hofmann:
Towards guarantees for parameter isolation in continual learning. CoRR abs/2310.01165 (2023) - [i71]Moritz Imfeld, Jacopo Graldi, Marco Giordano, Thomas Hofmann, Sotiris Anagnostidis, Sidak Pal Singh:
Transformer Fusion with Optimal Transport. CoRR abs/2310.05719 (2023) - [i70]Bobby He, Thomas Hofmann:
Simplifying Transformer Blocks. CoRR abs/2311.01906 (2023) - [i69]Sotiris Anagnostidis, Gregor Bachmann, Thomas Hofmann:
Navigating Scaling Laws: Accelerating Vision Transformer's Training via Adaptive Strategies. CoRR abs/2311.03233 (2023) - [i68]Elior Benarous, Sotiris Anagnostidis, Luca Biggio, Thomas Hofmann:
Harnessing Synthetic Datasets: The Role of Shape Bias in Deep Neural Network Generalization. CoRR abs/2311.06224 (2023) - [i67]Yuhui Ding, Antonio Orvieto, Bobby He, Thomas Hofmann:
Recurrent Distance-Encoding Neural Networks for Graph Representation Learning. CoRR abs/2312.01538 (2023) - [i66]Enis Simsar, Alessio Tonioni, Yongqin Xian, Thomas Hofmann, Federico Tombari:
LIME: Localized Image Editing via Attention Regularization in Diffusion Models. CoRR abs/2312.09256 (2023) - [i65]Gul Sena Altintas, Gregor Bachmann, Lorenzo Noci, Thomas Hofmann:
Disentangling Linear Mode-Connectivity. CoRR abs/2312.09832 (2023) - 2022
- [j23]Leonard Adolphs, Benjamin Börschinger, Christian Buck, Michelle Chen Huebscher, Massimiliano Ciaramita, Lasse Espeholt, Thomas Hofmann, Yannic Kilcher, Sascha Rothe, Pier Giuseppe Sessa, Lierni Sestorain:
Boosting Search Engines with Interactive Agents. Trans. Mach. Learn. Res. 2022 (2022) - [c112]Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi:
Vanishing Curvature in Randomly Initialized Deep ReLU Networks. AISTATS 2022: 7942-7975 - [c111]Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann:
Decoding a Neural Retriever's Latent Space for Query Suggestion. EMNLP 2022: 8786-8804 - [c110]Gregor Bachmann, Thomas Hofmann, Aurélien Lucchi:
Generalization Through the Lens of Leave-One-Out Error. ICLR 2022 - [c109]Sidak Pal Singh, Aurélien Lucchi, Thomas Hofmann, Bernhard Schölkopf:
Phenomenology of Double Descent in Finite-Width Neural Networks. ICLR 2022 - [c108]Gregor Bachmann, Lorenzo Noci, Thomas Hofmann:
How Tempering Fixes Data Augmentation in Bayesian Neural Networks. ICML 2022: 1244-1260 - [c107]Piera Riccio, Bill Psomas, Francesco Galati, Francisco Escolano, Thomas Hofmann, Nuria Oliver:
OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters. NeurIPS 2022 - [i64]Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Thomas Hofmann, Josef Teichmann:
Randomized Signature Layers for Signal Extraction in Time Series Data. CoRR abs/2201.00384 (2022) - [i63]Gregor Bachmann, Thomas Hofmann, Aurélien Lucchi:
Generalization Through The Lens Of Leave-One-Out Error. CoRR abs/2203.03443 (2022) - [i62]Sidak Pal Singh, Aurélien Lucchi, Thomas Hofmann, Bernhard Schölkopf:
Phenomenology of Double Descent in Finite-Width Neural Networks. CoRR abs/2203.07337 (2022) - [i61]Gregor Bachmann, Lorenzo Noci, Thomas Hofmann:
How Tempering Fixes Data Augmentation in Bayesian Neural Networks. CoRR abs/2205.13900 (2022) - [i60]Piera Riccio, Bill Psomas, Francesco Galati, Francisco Escolano, Thomas Hofmann, Nuria Oliver:
OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters. CoRR abs/2207.12319 (2022) - [i59]Sotiris Anagnostidis, Aurélien Lucchi, Thomas Hofmann:
Mastering Spatial Graph Prediction of Road Networks. CoRR abs/2210.00828 (2022) - [i58]Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann:
Decoding a Neural Retriever's Latent Space for Query Suggestion. CoRR abs/2210.12084 (2022) - [i57]Sotiris Anagnostidis, Gregor Bachmann, Lorenzo Noci, Thomas Hofmann:
The Curious Case of Benign Memorization. CoRR abs/2210.14019 (2022) - [i56]Sotiris Anagnostidis, Arne Thomsen, Tomasz Kacprzak, Tilman Tröster, Luca Biggio, Alexandre Refregier, Thomas Hofmann:
Cosmology from Galaxy Redshift Surveys with PointNet. CoRR abs/2211.12346 (2022) - 2021
- [c106]Mandra Bensmann, Alicia Lampe, Thomas Hofmann, Steffen Loth:
Collaborative Workspace - Concept Design of an Interactive System for Total Airport Management. AHFE (15) 2021: 323-330 - [c105]Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand, Thomas Hofmann, Roy S. Smith:
Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization. AISTATS 2021: 3979-3987 - [c104]Svenja Knothe, Thomas Hofmann, Christian Blessmann:
Theory and Practice in UX Design - Identification of Discrepancies in the Development Process of User-Oriented HMI. HCI (44) 2021: 37-43 - [c103]Mandra Bensmann, Alicia Lampe, Thomas Hofmann, Steffen Loth:
Collaborative Workspace - Concept Design and Proof of Concept of an Interactive Visual System to Support Collaborative Decision-Making for Total Airport Management. HCI (44) 2021: 511-516 - [c102]Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurélien Lucchi:
Learning Generative Models of Textured 3D Meshes from Real-World Images. ICCV 2021: 13859-13869 - [c101]Gregor Bachmann, Seyed-Mohsen Moosavi-Dezfooli, Thomas Hofmann:
Uniform Convergence, Adversarial Spheres and a Simple Remedy. ICML 2021: 490-499 - [c100]Lorenzo Noci, Kevin Roth, Gregor Bachmann, Sebastian Nowozin, Thomas Hofmann:
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect. NeurIPS 2021: 12738-12748 - [c99]Lorenzo Noci, Gregor Bachmann, Kevin Roth, Sebastian Nowozin, Thomas Hofmann:
Precise characterization of the prior predictive distribution of deep ReLU networks. NeurIPS 2021: 20851-20862 - [c98]Sidak Pal Singh, Gregor Bachmann, Thomas Hofmann:
Analytic Insights into Structure and Rank of Neural Network Hessian Maps. NeurIPS 2021: 23914-23927 - [i55]Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand, Thomas Hofmann, Roy S. Smith:
Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization. CoRR abs/2102.11537 (2021) - [i54]Pelin Dogan-Schönberger, Julian Mäder, Thomas Hofmann:
SwissDial: Parallel Multidialectal Corpus of Spoken Swiss German. CoRR abs/2103.11401 (2021) - [i53]Paulina Grnarova, Yannic Kilcher, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann:
Generative Minimization Networks: Training GANs Without Competition. CoRR abs/2103.12685 (2021) - [i52]Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurélien Lucchi:
Learning Generative Models of Textured 3D Meshes from Real-World Images. CoRR abs/2103.15627 (2021) - [i51]Gregor Bachmann, Seyed-Mohsen Moosavi-Dezfooli, Thomas Hofmann:
Uniform Convergence, Adversarial Spheres and a Simple Remedy. CoRR abs/2105.03491 (2021) - [i50]Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi:
Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks. CoRR abs/2106.03763 (2021) - [i49]Lorenzo Noci, Kevin Roth, Gregor Bachmann, Sebastian Nowozin, Thomas Hofmann:
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect. CoRR abs/2106.06596 (2021) - [i48]Lorenzo Noci, Gregor Bachmann, Kevin Roth, Sebastian Nowozin, Thomas Hofmann:
Precise characterization of the prior predictive distribution of deep ReLU networks. CoRR abs/2106.06615 (2021) - [i47]Sidak Pal Singh, Gregor Bachmann, Thomas Hofmann:
Analytic Insights into Structure and Rank of Neural Network Hessian Maps. CoRR abs/2106.16225 (2021) - [i46]Leonard Adolphs, Shehzaad Dhuliawala, Thomas Hofmann:
How to Query Language Models? CoRR abs/2108.01928 (2021) - [i45]Leonard Adolphs, Benjamin Börschinger, Christian Buck, Michelle Chen Huebscher, Massimiliano Ciaramita, Lasse Espeholt, Thomas Hofmann, Yannic Kilcher:
Boosting Search Engines with Interactive Agents. CoRR abs/2109.00527 (2021) - 2020
- [j22]Zoltán R. Bárdosi, Christian Plattner, Yusuf Özbek, Thomas Hofmann, Srdjan Milosavljevic, Volker Schartinger, Wolfgang Freysinger:
CIGuide: in situ augmented reality laser guidance. Int. J. Comput. Assist. Radiol. Surg. 15(1): 49-57 (2020) - [c97]Leonard Adolphs, Thomas Hofmann:
LeDeepChef Deep Reinforcement Learning Agent for Families of Text-Based Games. AAAI 2020: 7342-7349 - [c96]Dario Pavllo, Aurélien Lucchi, Thomas Hofmann:
Controlling Style and Semantics in Weakly-Supervised Image Generation. ECCV (6) 2020: 482-499 - [c95]Thomas Hofmann, Jörn Jakobi, Marcus Biella, Christian Blessmann, Fabian Reuschling, Tom Kamender:
Design and Implementation of a Virtual Workstation for a Remote AFISO. HCI (46) 2020: 152-163 - [c94]Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi:
Batch normalization provably avoids ranks collapse for randomly initialised deep networks. NeurIPS 2020 - [c93]Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurélien Lucchi:
Convolutional Generation of Textured 3D Meshes. NeurIPS 2020 - [c92]Kevin Roth, Yannic Kilcher, Thomas Hofmann:
Adversarial Training is a Form of Data-dependent Operator Norm Regularization. NeurIPS 2020 - [i44]Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi:
Theoretical Understanding of Batch-normalization: A Markov Chain Perspective. CoRR abs/2003.01652 (2020) - [i43]Florian Schmidt, Thomas Hofmann:
BERT as a Teacher: Contextual Embeddings for Sequence-Level Reward. CoRR abs/2003.02738 (2020) - [i42]Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurélien Lucchi:
Convolutional Generation of Textured 3D Meshes. CoRR abs/2006.07660 (2020)
2010 – 2019
- 2019
- [c91]Leonard Adolphs, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann:
Local Saddle Point Optimization: A Curvature Exploitation Approach. AISTATS 2019: 486-495 - [c90]Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Ming Zhou, Klaus Neymeyr:
Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization. AISTATS 2019: 806-815 - [c89]Florian Schmidt, Stephan Mandt, Thomas Hofmann:
Autoregressive Text Generation Beyond Feedback Loops. EMNLP/IJCNLP (1) 2019: 3398-3404 - [c88]Kevin Roth, Yannic Kilcher, Thomas Hofmann:
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples. ICML 2019: 5498-5507 - [c87]Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Ian Goodfellow, Thomas Hofmann, Andreas Krause:
A Domain Agnostic Measure for Monitoring and Evaluating GANs. NeurIPS 2019: 12069-12079 - [i41]Kevin Roth, Yannic Kilcher, Thomas Hofmann:
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples. CoRR abs/1902.04818 (2019) - [i40]Kevin Roth, Yannic Kilcher, Thomas Hofmann:
Adversarial Training Generalizes Data-dependent Spectral Norm Regularization. CoRR abs/1906.01527 (2019) - [i39]Nathanaël Perraudin, Ankit Srivastava, Aurélien Lucchi, Tomasz Kacprzak, Thomas Hofmann, Alexandre Réfrégier:
Cosmological N-body simulations: a challenge for scalable generative models. CoRR abs/1908.05519 (2019) - [i38]Florian Schmidt, Stephan Mandt, Thomas Hofmann:
Autoregressive Text Generation Beyond Feedback Loops. CoRR abs/1908.11658 (2019) - [i37]Leonard Adolphs, Thomas Hofmann:
LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games. CoRR abs/1909.01646 (2019) - [i36]Peiyuan Zhang, Hadi Daneshmand, Thomas Hofmann:
Mixing of Stochastic Accelerated Gradient Descent. CoRR abs/1910.14616 (2019) - [i35]Dario Pavllo, Aurélien Lucchi, Thomas Hofmann:
Controlling Style and Semantics in Weakly-Supervised Image Generation. CoRR abs/1912.03161 (2019) - 2018
- [c86]Nikolaos Kolitsas, Octavian-Eugen Ganea, Thomas Hofmann:
End-to-End Neural Entity Linking. CoNLL 2018: 519-529 - [c85]Valentin Trifonov, Octavian-Eugen Ganea, Anna Potapenko, Thomas Hofmann:
Learning and Evaluating Sparse Interpretable Sentence Embeddings. BlackboxNLP@EMNLP 2018: 200-210 - [c84]Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann, Andreas Krause:
An Online Learning Approach to Generative Adversarial Networks. ICLR (Poster) 2018 - [c83]Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann:
Semantic Interpolation in Implicit Models. ICLR (Poster) 2018 - [c82]Hadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann:
Escaping Saddles with Stochastic Gradients. ICML 2018: 1163-1172 - [c81]