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Thomas Hofmann 0001
Person information
- affiliation (since 2014): ETH Zurich, Switzerland
- affiliation (2006 - 2013): Google, Zurich, Switzerland
- affiliation (2004 - 2005): Technical University of Darmstadt, Germany
- affiliation (2001 - 2015): Brown University, Providence, RI, USA
- affiliation (former): Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
- affiliation (former): University of California Berkeley, Computer Science Division, CA, USA
- affiliation (former): Massachusetts Institute of Technology, Cambridge, MA, USA
- affiliation (PhD 1997): University of Bonn, Germany
Other persons with the same name
- Thomas Hofmann
- Thomas Hofmann 0002 — University of Applied Sciences Osnabrueck, Germany
- Thomas Hofmann 0003 — Technical University of Darmstadt, Germany
- Thomas F. Hofmann
(aka: Thomas Hofmann 0004) — Technical University of Munich, Chair of Food Chemistry and Molecular Sensory Science, Freising, Germany
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Books and Theses
- 1997
- [b1]Thomas Hofmann:
Data clustering and beyond: a deterministic annealing framework for exploratory data analysis. University of Bonn, Germany, Shaker 1997, ISBN 3-8265-3115-9, pp. 1-238
Journal Articles
- 2022
- [j22]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) - 2017
- [j21]Thomas Hofmann:
Probabilistic Latent Semantic Indexing. SIGIR Forum 51(2): 211-218 (2017) - [j20]Jason Lee, Kyunghyun Cho, Thomas Hofmann:
Fully Character-Level Neural Machine Translation without Explicit Segmentation. Trans. Assoc. Comput. Linguistics 5: 365-378 (2017) - [j19]Pascal Kaiser
Learning Aerial Image Segmentation From Online Maps. IEEE Trans. Geosci. Remote. Sens. 55(11): 6054-6068 (2017) - 2009
- [j18]Thorsten Joachims, Thomas Hofmann, Yisong Yue, Chun-Nam John Yu:
Predicting structured objects with support vector machines. Commun. ACM 52(11): 97-104 (2009) - [j17]Christoph H. Lampert, Matthew B. Blaschko
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization. IEEE Trans. Pattern Anal. Mach. Intell. 31(12): 2129-2142 (2009) - 2008
- [j16]Bhaskar Mehta, Thomas Hofmann:
A Survey of Attack-Resistant Collaborative Filtering Algorithms. IEEE Data Eng. Bull. 31(2): 14-22 (2008) - 2007
- [j15]David Gondek, Thomas Hofmann:
Non-redundant data clustering. Knowl. Inf. Syst. 12(1): 1-24 (2007) - [j14]Peter Groche, Knut Großmann, Thomas Hofmann, Hajo Wiemer:
Advanced experimental and numerical methods for the analysis of the dynamic forming press behavior. Prod. Eng. 1(3): 303-308 (2007) - 2005
- [j13]Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun:
Large Margin Methods for Structured and Interdependent Output Variables. J. Mach. Learn. Res. 6: 1453-1484 (2005) - 2004
- [j12]Thomas Hofmann:
Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22(1): 89-115 (2004) - 2003
- [j11]James Allan, Jay Aslam, Nicholas J. Belkin, Chris Buckley, James P. Callan, W. Bruce Croft, Susan T. Dumais, Norbert Fuhr, Donna Harman, David J. Harper, Djoerd Hiemstra, Thomas Hofmann, Eduard H. Hovy, Wessel Kraaij, John D. Lafferty, Victor Lavrenko, David D. Lewis, Liz Liddy, R. Manmatha, Andrew McCallum, Jay M. Ponte, John M. Prager, Dragomir R. Radev, Philip Resnik, Stephen E. Robertson, Ronald Rosenfeld
Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002. SIGIR Forum 37(1): 31-47 (2003) - 2002
- [j10]Scott Doniger, Thomas Hofmann, Miao-Hui Joanne Yeh:
Predicting CNS Permeability of Drug Molecules: Comparison of Neural Network and Support Vector Machine Algorithms. J. Comput. Biol. 9(6): 849-864 (2002) - 2001
- [j9]Thomas Hofmann:
Unsupervised Learning by Probabilistic Latent Semantic Analysis. Mach. Learn. 42(1/2): 177-196 (2001) - 2000
- [j8]Thomas Hofmann:
ProbMap - A probabilistic approach for mapping large document collections. Intell. Data Anal. 4(2): 149-164 (2000) - [j7]Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann:
A theory of proximity based clustering: structure detection by optimization. Pattern Recognit. 33(4): 617-634 (2000) - 1999
- [j6]Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann:
Histogram clustering for unsupervised segmentation and image retrieval. Pattern Recognit. Lett. 20(9): 899-909 (1999) - 1998
- [j5]Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann:
Unsupervised Texture Segmentation in a Deterministic Annealing Framework. IEEE Trans. Pattern Anal. Mach. Intell. 20(8): 803-818 (1998) - [j4]Thomas Hofmann, Joachim M. Buhmann:
Competitive learning algorithms for robust vector quantization. IEEE Trans. Signal Process. 46(6): 1665-1675 (1998) - 1997
- [j3]Thomas Hofmann, Joachim M. Buhmann:
Pairwise Data Clustering by Deterministic Annealing. IEEE Trans. Pattern Anal. Mach. Intell. 19(1): 1-14 (1997) - [j2]Thomas Hofmann, Joachim M. Buhmann:
Correction to "Pairwise Data Clustering by Deterministic Annealing". IEEE Trans. Pattern Anal. Mach. Intell. 19(2): 192 (1997) - 1995
- [j1]Joachim M. Buhmann, Wolfram Burgard, Armin B. Cremers, Dieter Fox, Thomas Hofmann, Frank E. Schneider, Jiannis Strikos, Sebastian Thrun:
The Mobile Robot RHINO. AI Mag. 16(2): 31-38 (1995)
Conference and Workshop Papers
- 2025
- [c141]Aleksandar Terzic, Michael Hersche, Giacomo Camposampiero, Thomas Hofmann, Abu Sebastian, Abbas Rahimi
On the Expressiveness and Length Generalization of Selective State Space Models on Regular Languages. AAAI 2025: 20876-20884 - [c140]Pietro Lesci, Clara Meister, Thomas Hofmann, Andreas Vlachos, Tiago Pimentel:
Causal Estimation of Tokenisation Bias. ACL (1) 2025: 28325-28340 - [c139]Amir Joudaki, Thomas Hofmann:
Emergence of Globally Attracting Fixed Points in Deep Neural Networks With Nonlinear Activations. AISTATS 2025: 5212-5220 - [c138]Enis Simsar, Thomas Hofmann, Federico Tombari, Pinar Yanardag:
LoRACLR: Contrastive Adaptation for Customization of Diffusion Models. CVPR 2025: 13189-13198 - [c137]Sidak Pal Singh, Bobby He, Thomas Hofmann, Bernhard Schölkopf:
The Directionality of Optimization Trajectories in Neural Networks. ICLR 2025 - [c136]Enis Simsar, Alessio Tonioni, Yongqin Xian, Thomas Hofmann, Federico Tombari:
LIME: Localized Image Editing via Attention Regularization in Diffusion Models. WACV 2025: 222-231 - 2024
- [c135]Anton Schäfer, Thomas Hofmann, Imanol Schlag
On the Effect of (Near) Duplicate Subwords in Language Modelling. ACL (Findings) 2024: 9580-9597 - [c134]Pietro Lesci, Clara Meister, Thomas Hofmann, Andreas Vlachos
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
Exposed or Erased: Algorithmic Censorship of Nudity in Art. CHI 2024: 26:1-26:17 - [c131]Giulia Lanzillotta, Sidak Pal Singh, Benjamin F. Grewe, Thomas Hofmann:
Local vs Global continual learning. CoLLAs 2024: 121-143 - [c130]Piera Riccio
An Art-Centric Perspective on AI-Based Content Moderation of Nudity. ECCV Workshops (5) 2024: 121-138 - [c129]Daniel Gareev, Thomas Hofmann, Ezhilmathi Krishnasamy
Local and Global Decoding in Text Generation. EMNLP (Findings) 2024: 14577-14597 - [c128]Bobby He, Thomas Hofmann:
Simplifying Transformer Blocks. ICLR 2024 - [c127]Moritz Imfeld, Jacopo Graldi, Marco Giordano, Thomas Hofmann, Sotiris Anagnostidis, Sidak Pal Singh:
Transformer Fusion with Optimal Transport. ICLR 2024 - [c126]Alexander Theus, Olin Geimer, Friedrich Wicke, Thomas Hofmann, Sotiris Anagnostidis, Sidak Pal Singh:
Towards Meta-Pruning via Optimal Transport. ICLR 2024 - [c125]Sotiris Anagnostidis, Gregor Bachmann, Imanol Schlag, Thomas Hofmann:
Navigating Scaling Laws: Compute Optimality in Adaptive Model Training. ICML 2024 - [c124]Yuhui Ding, Antonio Orvieto, Bobby He, Thomas Hofmann:
Recurrent Distance Filtering for Graph Representation Learning. ICML 2024 - [c123]Dimitri von Rütte, Sotiris Anagnostidis, Gregor Bachmann, Thomas Hofmann:
A Language Model's Guide Through Latent Space. ICML 2024 - [c122]Bobby He, Lorenzo Noci, Daniele Paliotta, Imanol Schlag, Thomas Hofmann:
Understanding and Minimising Outlier Features in Transformer Training. NeurIPS 2024 - [c121]Lorenzo Noci, Alexandru Meterez, Thomas Hofmann, Antonio Orvieto:
Super Consistency of Neural Network Landscapes and Learning Rate Transfer. NeurIPS 2024 - 2023
- [c120]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 - [c119]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 - [c118]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 - [c117]Sotiris Anagnostidis, Aurélien Lucchi, Thomas Hofmann:
Mastering Spatial Graph Prediction of Road Networks. ICCV 2023: 5385-5395 - [c116]Sotiris Anagnostidis, Gregor Bachmann, Lorenzo Noci, Thomas Hofmann:
The Curious Case of Benign Memorization. ICLR 2023 - [c115]Dimitri von Rütte, Luca Biggio, Yannic Kilcher, Thomas Hofmann:
FIGARO: Controllable Music Generation using Learned and Expert Features. ICLR 2023 - [c114]Felix Sarnthein, Gregor Bachmann, Sotiris Anagnostidis, Thomas Hofmann:
Random Teachers are Good Teachers. ICML 2023: 30022-30041 - [c113]Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf:
The Hessian perspective into the Nature of Convolutional Neural Networks. ICML 2023: 31930-31968 - [c112]Enea Monzio Compagnoni, Anna Scampicchio
On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics. IJCNN 2023: 1-8 - [c111]Sotiris Anagnostidis, Dario Pavllo, Luca Biggio, Lorenzo Noci, Aurélien Lucchi, Thomas Hofmann:
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers. NeurIPS 2023 - [c110]Gregor Bachmann, Sotiris Anagnostidis, Thomas Hofmann:
Scaling MLPs: A Tale of Inductive Bias. NeurIPS 2023 - [c109]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 - 2022
- [c108]Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi:
Vanishing Curvature in Randomly Initialized Deep ReLU Networks. AISTATS 2022: 7942-7975 - [c107]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 - [c106]Gregor Bachmann, Thomas Hofmann, Aurélien Lucchi:
Generalization Through the Lens of Leave-One-Out Error. ICLR 2022 - [c105]Sidak Pal Singh, Aurélien Lucchi, Thomas Hofmann, Bernhard Schölkopf:
Phenomenology of Double Descent in Finite-Width Neural Networks. ICLR 2022 - [c104]Gregor Bachmann, Lorenzo Noci, Thomas Hofmann:
How Tempering Fixes Data Augmentation in Bayesian Neural Networks. ICML 2022: 1244-1260 - [c103]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 - 2021
- [c102]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 - [c101]Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurélien Lucchi
Learning Generative Models of Textured 3D Meshes from Real-World Images. ICCV 2021: 13859-13869 - [c100]Gregor Bachmann, Seyed-Mohsen Moosavi-Dezfooli, Thomas Hofmann:
Uniform Convergence, Adversarial Spheres and a Simple Remedy. ICML 2021: 490-499 - [c99]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 - [c98]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 - [c97]Sidak Pal Singh, Gregor Bachmann, Thomas Hofmann:
Analytic Insights into Structure and Rank of Neural Network Hessian Maps. NeurIPS 2021: 23914-23927 - 2020
- [c96]Leonard Adolphs, Thomas Hofmann:
LeDeepChef Deep Reinforcement Learning Agent for Families of Text-Based Games. AAAI 2020: 7342-7349 - [c95]Dario Pavllo, Aurélien Lucchi
Controlling Style and Semantics in Weakly-Supervised Image Generation. ECCV (6) 2020: 482-499 - [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 - 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 J. Goodfellow, Thomas Hofmann, Andreas Krause:
A Domain Agnostic Measure for Monitoring and Evaluating GANs. NeurIPS 2019: 12069-12079 - 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]Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi:
A Distributed Second-Order Algorithm You Can Trust. ICML 2018: 1357-1365 - [c80]Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann:
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings. ICML 2018: 1632-1641 - [c79]Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann:
Hyperbolic Neural Networks. NeurIPS 2018: 5350-5360 - [c78]Florian Schmidt, Thomas Hofmann:
Deep State Space Models for Unconditional Word Generation. NeurIPS 2018: 6161-6171 - 2017
- [c77]Octavian-Eugen Ganea, Thomas Hofmann:
Deep Joint Entity Disambiguation with Local Neural Attention. EMNLP 2017: 2619-2629 - [c76]Kevin Roth, Aurélien Lucchi, Sebastian Nowozin, Thomas Hofmann:
Stabilizing Training of Generative Adversarial Networks through Regularization. NIPS 2017: 2018-2028 - [c75]Jan Deriu
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification. WWW 2017: 1045-1052 - 2016
- [c74]Piyush Bansal, Carsten Eickhoff, Thomas Hofmann:
Active Content-Based Crowdsourcing Task Selection. CIKM 2016: 529-538 - [c73]Elias Sprengel, Martin Jaggi, Yannic Kilcher, Thomas Hofmann:
Audio Based Bird Species Identification using Deep Learning Techniques. CLEF (Working Notes) 2016: 547-559 - [c72]Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann:
Starting Small - Learning with Adaptive Sample Sizes. ICML 2016: 1463-1471 - [c71]Thomas Hofmann, Jörg Bergner:
DFS- ConceptDesk - Experimenteller Workspace für Fluglotsen. UP 2016 - [c70]Aryan Mokhtari, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Alejandro Ribeiro:
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy. NIPS 2016: 4062-4070 - [c69]Octavian-Eugen Ganea, Marina Ganea, Aurélien Lucchi
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking. WWW 2016: 927-938 - 2015
- [c68]Martin Davtyan, Carsten Eickhoff, Thomas Hofmann:
Exploiting Document Content for Efficient Aggregation of Crowdsourcing Votes. CIKM 2015: 783-790 - [c67]Thomas Hofmann, Aurélien Lucchi, Simon Lacoste-Julien, Brian McWilliams:
Variance Reduced Stochastic Gradient Descent with Neighbors. NIPS 2015: 2305-2313 - [c66]Carsten Eickhoff, Arjen P. de Vries
Modelling Term Dependence with Copulas. SIGIR 2015: 783-786 - 2014
- [c65]Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan:
Communication-Efficient Distributed Dual Coordinate Ascent. NIPS 2014: 3068-3076 - 2009
- [c64]Christina König, Thomas Hofmann, Jörg Bergner, Ralph Bruder:
Inkrementelle nutzergerechte Etablierung eines Towerlotsen-HMI. MuC 2009: 63-72 - 2008
- [c63]Christoph H. Lampert, Matthew B. Blaschko
Beyond sliding windows: Object localization by efficient subwindow search. CVPR 2008 - [c62]Thomas Hofmann, Peter Holzkämper:
NEW HMI Möglichkeiten und Grenzen abstrakt-geographischer Visualisierung in Bereich der Anlagensteuerung. UP 2008: 204-209 - [c61]Christina König, Thomas Hofmann, Jörg Bergner, Ralph Bruder:
Evaluation eines Human-Machine-Interface für Tower-Fluglotsen unter Einsatz eines Simulators. UP 2008: 295-297 - 2007
- [c60]Lijuan Cai, Thomas Hofmann:
Exploiting Known Taxonomies in Learning Overlapping Concepts. IJCAI 2007: 714-719 - [c59]Bhaskar Mehta, Thomas Hofmann, Peter Fankhauser:
Lies and propaganda: detecting spam users in collaborative filtering. IUI 2007: 14-21 - [c58]Bhaskar Mehta, Thomas Hofmann, Wolfgang Nejdl
Robust collaborative filtering. RecSys 2007: 49-56 - 2006
- [c57]Thomas Wolf, Benedikt Brors
Global Biclustering of Microarray Data. ICDM Workshops 2006: 125-129 - [c56]Bhaskar Mehta, Thomas Hofmann:
Cross System Personalization and Collaborative Filtering by Learning Manifold Alignments. KI 2006: 244-259 - 2005
- [c55]Alexander J. Smola, S. V. N. Vishwanathan, Thomas Hofmann:
Kernel Methods for Missing Variables. AISTATS 2005: 325-332 - [c54]Thomas Hofmann, Justin Basilico:
Collaborative Machine Learning. From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments 2005: 173-182 - [c53]Thomas Hofmann:
From bits and bytes to information and knowledge. CIKM 2005: 3 - [c52]Thomas Navin Lal, Michael Schröder, N. Jeremy Hill, Hubert Preißl, Thilo Hinterberger, Jürgen Mellinger, Martin Bogdan, Wolfgang Rosenstiel, Thomas Hofmann, Niels Birbaumer, Bernhard Schölkopf:
A brain computer interface with online feedback based on magnetoencephalography. ICML 2005: 465-472 - [c51]David Gondek, Thomas Hofmann:
Non-redundant clustering with conditional ensembles. KDD 2005: 70-77 - 2004
- [c50]Lijuan Cai, Thomas Hofmann:
Hierarchical document categorization with support vector machines. CIKM 2004: 78-87 - [c49]Ha Quang Minh, Thomas Hofmann:
Learning Over Compact Metric Spaces. COLT 2004: 239-254 - [c48]David Gondek, Thomas Hofmann:
Non-Redundant Data Clustering. ICDM 2004: 75-82 - [c47]Yasemin Altun, Thomas Hofmann, Alexander J. Smola:
Gaussian process classification for segmenting and annotating sequences. ICML 2004 - [c46]Justin Basilico, Thomas Hofmann:
Unifying collaborative and content-based filtering. ICML 2004 - [c45]Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, Yasemin Altun:
Support vector machine learning for interdependent and structured output spaces. ICML 2004 - [c44]Dengyong Zhou, Bernhard Schölkopf, Thomas Hofmann:
Semi-supervised Learning on Directed Graphs. NIPS 2004: 1633-1640 - [c43]Justin Basilico, Thomas Hofmann:
A joint framework for collaborative and content filtering. SIGIR 2004: 550-551 - [c42]Yasemin Altun, Alexander J. Smola, Thomas Hofmann:
Exponential Families for Conditional Random Fields. UAI 2004: 2-9 - 2003
- [c41]Yasemin Altun, Mark Johnson, Thomas Hofmann:
Investigating Loss Functions and Optimization Methods for Discriminative Learning of Label Sequences. EMNLP 2003 - [c40]Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofmann:
Hidden Markov Support Vector Machines. ICML 2003: 3-10 - [c39]Massimiliano Ciaramita, Thomas Hofmann, Mark Johnson:
Hierarchical Semantic Classification: Word Sense Disambiguation with World Knowledge. IJCAI 2003: 817-822 - [c38]Yasemin Altun, Thomas Hofmann:
Large margin methods for label sequence learning. INTERSPEECH 2003: 993-996 - [c37]Claudius Lazzeroni, Petra Gersch, Thomas Hofmann, Christian Noss:
Phaszination Ergonomie: Von der Entlastung am Fließband zur Inspiration im Design. UP 2003: 123-129 - [c36]Stuart Andrews, Thomas Hofmann:
Multiple-Instance Learning via Disjunctive Programming Boosting. NIPS 2003: 65-72 - [c35]Lijuan Cai, Thomas Hofmann:
Text categorization by boosting automatically extracted concepts. SIGIR 2003: 182-189 - [c34]Thomas Hofmann:
Collaborative filtering via gaussian probabilistic latent semantic analysis. SIGIR 2003: 259-266 - 2002
- [c33]Stuart Andrews, Thomas Hofmann, Ioannis Tsochantaridis:
Multiple Instance Learning with Generalized Support Vector Machines. AAAI/IAAI 2002: 943-944 - [c32]Ioannis Tsochantaridis, Thomas Hofmann:
Support Vector Machines for Polycategorical Classification. ECML 2002: 456-467 - [c31]Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann:
Support Vector Machines for Multiple-Instance Learning. NIPS 2002: 561-568 - [c30]Yasemin Altun, Thomas Hofmann, Mark Johnson:
Discriminative Learning for Label Sequences via Boosting. NIPS 2002: 977-984 - 2001
- [c29]Kristina Toutanova, Francine Chen, Kris Popat, Thomas Hofmann:
Text Classification in a Hierarchical Mixture Model for Small Training Sets. CIKM 2001: 105-112 - [c28]Thomas Hofmann:
Learning What People (Don't) Want. ECML 2001: 214-225 - 2000
- [c27]Stéphane Ducasse, Thomas Hofmann, Oscar Nierstrasz
OpenSpaces: An Object-Oriented Framework for Reconfigurable Coordination Spaces. COORDINATION 2000: 1-18 - [c26]Keith B. Hall, Thomas Hofmann:
Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval. ICML 2000: 351-358 - [c25]David A. Cohn, Thomas Hofmann:
The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity. NIPS 2000: 430-436 - [c24]Thomas Hofmann:
Learning probabilistic models of the Web. SIGIR 2000: 369-371 - 1999
- [c23]Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann:
Histogram Clustering for Unsupervised Image Segmentation. CVPR 1999: 2602-2608 - [c22]Thomas Hofmann:
Probabilistic Topic Maps: Navigating through Large Text Collections. IDA 1999: 161-172 - [c21]Thomas Hofmann:
The Cluster-Abstraction Model: Unsupervised Learning of Topic Hierarchies from Text Data. IJCAI 1999: 682-687 - [c20]Thomas Hofmann, Jan Puzicha:
Latent Class Models for Collaborative Filtering. IJCAI 1999: 688-693 - [c19]Daniel Gildea, Thomas Hofmann:
Topic-based language models using EM. EUROSPEECH 1999: 2167-2170 - [c18]Thomas Hofmann:
Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization. NIPS 1999: 914-920 - [c17]Thomas Hofmann:
Probabilistic Latent Semantic Indexing. SIGIR 1999: 50-57 - [c16]Thomas Hofmann:
Probabilistic Latent Semantic Analysis. UAI 1999: 289-296 - 1998
- [c15]Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann:
Discrete Mixture Models for Unsupervised Image Segmentation. DAGM-Symposium 1998: 135-142 - [c14]Thomas Hofmann, Jan Puzicha:
Mixture models for co-occurrence and histogram data. ICPR 1998: 192-194 - [c13]Thomas Hofmann, Jan Puzicha, Michael I. Jordan:
Learning from Dyadic Data. NIPS 1998: 466-472 - 1997
- [c12]Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann:
Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval. CVPR 1997: 267-272 - [c11]Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann:
Deterministic Annealing for Unsupervised Texture Segmentation. EMMCVPR 1997: 213-228 - [c10]Joachim M. Buhmann, Thomas Hofmann:
Robust vector quantization by competitive learning. ICASSP 1997: 139-142 - [c9]Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann:
An Optimization Approach to Unsupervised Hierarchical Texture Segmentation. ICIP (3) 1997: 213-216 - [c8]Thomas Hofmann, Joachim M. Buhmann:
Active Data Clustering. NIPS 1997: 528-534 - 1996
- [c7]Thomas Hofmann, Joachim M. Buhmann:
An Annealed "Neural Gas" Network for Robust Vector Quantization. ICANN 1996: 151-156 - [c6]Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann:
Unsupervised segmentation of textured images by pairwise data clustering. ICIP (3) 1996: 137-140 - [c5]Thomas Hofmann, Joachim M. Buhmann:
Inferring Hierarchical Clustering Structures by Deterministic Annealing. KDD 1996: 363-366 - 1995
- [c4]Joachim M. Buhmann, Wolfram Burgard, Armin B. Cremers, Dieter Fox, Thomas Hofmann, Frank E. Schneider
The Mobile Robot Rhino. SNN Symposium on Neural Networks 1995: 129-139 - 1994
- [c3]Joachim M. Buhmann, Thomas Hofmann:
A maximum entropy approach to pairwise data clustering. ICPR (2) 1994: 207-212 - [c2]Thomas Hofmann, Joachim M. Buhmann:
Multidimensional Scaling and Data Clustering. NIPS 1994: 459-466 - 1993
- [c1]Joachim M. Buhmann, Thomas Hofmann:
Central and Pairwise Data Clustering by Competitive Neural Networks. NIPS 1993: 104-111
Editorship
- 2011
- [e4]Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba, Michalis Vazirgiannis:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011. Proceedings, Part I. Lecture Notes in Computer Science 6911, Springer 2011, ISBN 978-3-642-23779-9 [contents] - [e3]Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba, Michalis Vazirgiannis:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II. Lecture Notes in Computer Science 6912, Springer 2011, ISBN 978-3-642-23782-9 [contents] - [e2]Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba, Michalis Vazirgiannis:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part III. Lecture Notes in Computer Science 6913, Springer 2011, ISBN 978-3-642-23807-9 [contents] - 2007
- [e1]Bernhard Schölkopf, John C. Platt, Thomas Hofmann:
Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 4-7, 2006. MIT Press 2007, ISBN 0-262-19568-2 [contents]
Informal and Other Publications
- 2025
- [i118]Tianxiang Xia, Lin Xiao, Yannick Montorfani, Francesco Pavia, Enis Simsar, Thomas Hofmann:
SHYI: Action Support for Contrastive Learning in High-Fidelity Text-to-Image Generation. CoRR abs/2501.09055 (2025) - [i117]Han Yang, Enis Simsar, Sotiris Anagnostidis, Yanlong Zang, Thomas Hofmann, Ziwei Liu:
IC-Portrait: In-Context Matching for View-Consistent Personalized Portrait. CoRR abs/2501.17159 (2025) - [i116]Dimitri von Rütte, Janis Fluri, Yuhui Ding, Antonio Orvieto, Bernhard Schölkopf, Thomas Hofmann:
Generalized Interpolating Discrete Diffusion. CoRR abs/2503.04482 (2025) - [i115]Eric Tillmann Bill, Enis Simsar, Thomas Hofmann:
JEDI: The Force of Jensen-Shannon Divergence in Disentangling Diffusion Models. CoRR abs/2505.19166 (2025) - [i114]Pietro Lesci, Clara Meister, Thomas Hofmann, Andreas Vlachos, Tiago Pimentel:
Causal Estimation of Tokenisation Bias. CoRR abs/2506.03149 (2025) - [i113]Yuhui Ding, Thomas Hofmann:
Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment. CoRR abs/2506.10186 (2025) - [i112]Jacopo Graldi, Alessandro Breccia, Giulia Lanzillotta, Thomas Hofmann, Lorenzo Noci:
The Importance of Being Lazy: Scaling Limits of Continual Learning. CoRR abs/2506.16884 (2025) - [i111]Phoomraphee Luenam, Andreas Spanopoulos, Amit Sant, Thomas Hofmann, Sotiris Anagnostidis, Sidak Pal Singh:
Model Fusion via Neuron Interpolation. CoRR abs/2507.00037 (2025) - [i110]Denis Sutter, Julian Minder, Thomas Hofmann, Tiago Pimentel:
The Non-Linear Representation Dilemma: Is Causal Abstraction Enough for Mechanistic Interpretability? CoRR abs/2507.08802 (2025) - [i109]Aleksandar Terzic, Nicolas Menet, Michael Hersche, Thomas Hofmann, Abbas Rahimi:
Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models. CoRR abs/2509.22284 (2025) - [i108]Amir Joudaki, Giulia Lanzillotta, Mohammad Samragh Razlighi, Iman Mirzadeh, Keivan Alizadeh, Thomas Hofmann, Mehrdad Farajtabar, Fartash Faghri:
Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of Plasticity. CoRR abs/2510.00304 (2025) - [i107]Eric Tillmann Bill, Enis Simsar, Thomas Hofmann:
Optimal Control Meets Flow Matching: A Principled Route to Multi-Subject Fidelity. CoRR abs/2510.02315 (2025) - [i106]Giulia Lanzillotta, Felix Sarnthein, Gil Kur, Thomas Hofmann, Bobby He:
Revisiting Knowledge Distillation: The Hidden Role of Dataset Size. CoRR abs/2510.15516 (2025) - [i105]Michael Hersche, Samuel Moor-Smith, Thomas Hofmann, Abbas Rahimi:
Soft-Masked Diffusion Language Models. CoRR abs/2510.17206 (2025) - [i104]Tiberiu Musat, Tiago Pimentel, Lorenzo Noci, Alessandro Stolfo, Mrinmaya Sachan, Thomas Hofmann:
On the Emergence of Induction Heads for In-Context Learning. CoRR abs/2511.01033 (2025) - 2024
- [i103]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) - [i102]Kai Lion, Lorenzo Noci, Thomas Hofmann, Gregor Bachmann:
How Good is a Single Basin? CoRR abs/2402.03187 (2024) - [i101]Alexander Theus, Olin Geimer, Friedrich Wicke, Thomas Hofmann, Sotiris Anagnostidis, Sidak Pal Singh:
Towards Meta-Pruning via Optimal Transport. CoRR abs/2402.07839 (2024) - [i100]Dimitri von Rütte, Sotiris Anagnostidis, Gregor Bachmann, Thomas Hofmann:
A Language Model's Guide Through Latent Space. CoRR abs/2402.14433 (2024) - [i99]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) - [i98]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) - [i97]Anton Schäfer, Thomas Hofmann, Imanol Schlag, Tiago Pimentel:
On the Effect of (Near) Duplicate Subwords in Language Modelling. CoRR abs/2404.06508 (2024) - [i96]Anton Schäfer, Shauli Ravfogel, Thomas Hofmann, Tiago Pimentel, Imanol Schlag:
Language Imbalance Can Boost Cross-lingual Generalisation. CoRR abs/2404.07982 (2024) - [i95]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) - [i94]Bobby He, Lorenzo Noci, Daniele Paliotta, Imanol Schlag, Thomas Hofmann:
Understanding and Minimising Outlier Features in Neural Network Training. CoRR abs/2405.19279 (2024) - [i93]Pietro Lesci, Clara Meister, Thomas Hofmann, Andreas Vlachos
Causal Estimation of Memorisation Profiles. CoRR abs/2406.04327 (2024) - [i92]Jovan Andonov, Octavian Ganea, Paulina Grnarova, Gary Bécigneul, Thomas Hofmann:
Explicit Word Density Estimation for Language Modelling. CoRR abs/2406.10256 (2024) - [i91]Sidak Pal Singh, Linara Adilova, Michael Kamp, Asja Fischer, Bernhard Schölkopf, Thomas Hofmann:
Landscaping Linear Mode Connectivity. CoRR abs/2406.16300 (2024) - [i90]Giulia Lanzillotta, Sidak Pal Singh, Benjamin F. Grewe, Thomas Hofmann:
Local vs Global continual learning. CoRR abs/2407.16611 (2024) - [i89]Piera Riccio, Georgina Curto, Thomas Hofmann, Nuria Oliver
An Art-centric perspective on AI-based content moderation of nudity. CoRR abs/2409.17156 (2024) - [i88]Stefan Stefanache, Lluís Pastor Pérez, Julen Costa Watanabe, Ernesto Sanchez Tejedor, Thomas Hofmann, Enis Simsar:
PixLens: A Novel Framework for Disentangled Evaluation in Diffusion-Based Image Editing with Object Detection + SAM. CoRR abs/2410.05710 (2024) - [i87]Daniel Gareev, Thomas Hofmann, Ezhilmathi Krishnasamy
Local and Global Decoding in Text Generation. CoRR abs/2410.10810 (2024) - [i86]Amir Joudaki, Thomas Hofmann:
Emergence of Globally Attracting Fixed Points in Deep Neural Networks With Nonlinear Activations. CoRR abs/2410.20107 (2024) - [i85]Han Yang, Sotiris Anagnostidis, Enis Simsar, Thomas Hofmann:
MegaPortrait: Revisiting Diffusion Control for High-fidelity Portrait Generation. CoRR abs/2411.04357 (2024) - [i84]Enis Simsar, Thomas Hofmann, Federico Tombari, Pinar Yanardag:
LoRACLR: Contrastive Adaptation for Customization of Diffusion Models. CoRR abs/2412.09622 (2024) - [i83]Enis Simsar, Alessio Tonioni, Yongqin Xian, Thomas Hofmann, Federico Tombari:
UIP2P: Unsupervised Instruction-based Image Editing via Cycle Edit Consistency. CoRR abs/2412.15216 (2024) - [i82]Aleksandar Terzic, Michael Hersche, Giacomo Camposampiero, Thomas Hofmann, Abu Sebastian, Abbas Rahimi:
On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages. CoRR abs/2412.19350 (2024) - 2023
- [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
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
- [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
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
Cosmology from Galaxy Redshift Surveys with PointNet. CoRR abs/2211.12346 (2022) - 2021
- [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
- [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) - 2019
- [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
- [i34]Hadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann:
Escaping Saddles with Stochastic Gradients. CoRR abs/1803.05999 (2018) - [i33]Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann:
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings. CoRR abs/1804.01882 (2018) - [i32]Leonard Adolphs, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann:
Local Saddle Point Optimization: A Curvature Exploitation Approach. CoRR abs/1805.05751 (2018) - [i31]Kevin Roth, Aurélien Lucchi, Sebastian Nowozin, Thomas Hofmann:
Adversarially Robust Training through Structured Gradient Regularization. CoRR abs/1805.08736 (2018) - [i30]Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann:
Hyperbolic Neural Networks. CoRR abs/1805.09112 (2018) - [i29]Lierni Sestorain, Massimiliano Ciaramita, Christian Buck, Thomas Hofmann:
Zero-Shot Dual Machine Translation. CoRR abs/1805.10338 (2018) - [i28]Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Ming Zhou, Klaus Neymeyr, Thomas Hofmann:
Towards a Theoretical Understanding of Batch Normalization. CoRR abs/1805.10694 (2018) - [i27]Florian Schmidt, Thomas Hofmann:
Deep State Space Models for Unconditional Word Generation. CoRR abs/1806.04550 (2018) - [i26]Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi:
A Distributed Second-Order Algorithm You Can Trust. CoRR abs/1806.07569 (2018) - [i25]Nikolaos Kolitsas, Octavian-Eugen Ganea, Thomas Hofmann:
End-to-End Neural Entity Linking. CoRR abs/1808.07699 (2018) - [i24]Valentin Trifonov, Octavian-Eugen Ganea, Anna Potapenko, Thomas Hofmann:
Learning and Evaluating Sparse Interpretable Sentence Embeddings. CoRR abs/1809.08621 (2018) - [i23]Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Thomas Hofmann, Andreas Krause:
Evaluating GANs via Duality. CoRR abs/1811.05512 (2018) - 2017
- [i22]Jan Deriu, Aurélien Lucchi, Valeria De Luca, Aliaksei Severyn, Simon Müller, Mark Cieliebak, Thomas Hofmann, Martin Jaggi:
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification. CoRR abs/1703.02504 (2017) - [i21]Octavian-Eugen Ganea, Thomas Hofmann:
Deep Joint Entity Disambiguation with Local Neural Attention. CoRR abs/1704.04920 (2017) - [i20]Kevin Roth, Aurélien Lucchi, Sebastian Nowozin, Thomas Hofmann:
Stabilizing Training of Generative Adversarial Networks through Regularization. CoRR abs/1705.09367 (2017) - [i19]Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann, Andreas Krause:
An Online Learning Approach to Generative Adversarial Networks. CoRR abs/1706.03269 (2017) - [i18]Hadi Daneshmand, Hamed Hassani, Thomas Hofmann:
Accelerated Dual Learning by Homotopic Initialization. CoRR abs/1706.03958 (2017) - [i17]Pascal Kaiser, Jan Dirk Wegner, Aurélien Lucchi, Martin Jaggi, Thomas Hofmann, Konrad Schindler:
Learning Aerial Image Segmentation from Online Maps. CoRR abs/1707.06879 (2017) - [i16]Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann:
Generator Reversal. CoRR abs/1707.09241 (2017) - [i15]Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann:
Semantic Interpolation in Implicit Models. CoRR abs/1710.11381 (2017) - [i14]Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann:
Flexible Prior Distributions for Deep Generative Models. CoRR abs/1710.11383 (2017) - [i13]Yannic Kilcher, Gary Bécigneul, Thomas Hofmann:
Parametrizing filters of a CNN with a GAN. CoRR abs/1710.11386 (2017) - [i12]Yannic Kilcher, Thomas Hofmann:
The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels. CoRR abs/1711.05475 (2017) - 2016
- [i11]Siddharth Sarda, Carsten Eickhoff, Thomas Hofmann:
Semantic Place Descriptors for Classification and Map Discovery. CoRR abs/1601.05952 (2016) - [i10]Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann:
Starting Small - Learning with Adaptive Sample Sizes. CoRR abs/1603.02839 (2016) - [i9]Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann:
DynaNewton - Accelerating Newton's Method for Machine Learning. CoRR abs/1605.06561 (2016) - [i8]Jason Lee, Kyunghyun Cho, Thomas Hofmann:
Fully Character-Level Neural Machine Translation without Explicit Segmentation. CoRR abs/1610.03017 (2016) - [i7]Wenhu Chen, Aurélien Lucchi, Thomas Hofmann:
Bootstrap, Review, Decode: Using Out-of-Domain Textual Data to Improve Image Captioning. CoRR abs/1611.05321 (2016) - 2015
- [i6]Aurélien Lucchi, Brian McWilliams, Thomas Hofmann:
A Variance Reduced Stochastic Newton Method. CoRR abs/1503.08316 (2015) - [i5]Thomas Hofmann, Aurélien Lucchi, Brian McWilliams:
Neighborhood Watch: Stochastic Gradient Descent with Neighbors. CoRR abs/1506.03662 (2015) - [i4]Octavian-Eugen Ganea, Marina Horlescu, Aurélien Lucchi, Carsten Eickhoff, Thomas Hofmann:
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking. CoRR abs/1509.02301 (2015) - 2014
- [i3]Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan:
Communication-Efficient Distributed Dual Coordinate Ascent. CoRR abs/1409.1458 (2014) - 2013
- [i2]Thomas Hofmann:
Probabilistic Latent Semantic Analysis. CoRR abs/1301.6705 (2013) - 2012
- [i1]Yasemin Altun, Alexander J. Smola, Thomas Hofmann:
Exponential Families for Conditional Random Fields. CoRR abs/1207.4131 (2012)
Coauthor Index
Sotiris Anagnostidis
aka: Sotirios-Konstantinos Anagnostidis
aka: Sotirios-Konstantinos Anagnostidis
Octavian-Eugen Ganea
aka: Octavian Ganea
aka: Octavian Ganea
Jonas Kohler
aka: Jonas Moritz Kohler
aka: Jonas Moritz Kohler
[j19] [c117] [c111] [c108] [c106] [c105] [c101] [c95] [c94] [c93] [c91] [c90] [c87] [c84] [c83] [c82] [c81] [c76] [c75] [c72] [c70] [c69] [c67] [i77] [i63] [i62] [i59] [i53] [i52] [i50] [i44] [i42] [i39] [i35] [i34] [i32] [i31] [i28] [i26] [i23] [i22] [i20] [i19] [i17] [i16] [i15] [i14] [i10] [i9] [i7] [i6] [i5] [i4]
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last updated on 2025-11-17 23:04 CET by the dblp team
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