Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDThomas Hofmann
Person information
- affiliation: ETH Zurich, Switzerland
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 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)- [i69]Felix Sarnthein, Gregor Bachmann, Sotiris Anagnostidis, Thomas Hofmann:
Random Teachers are Good Teachers. CoRR abs/2302.12091 (2023) - [i68]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) - [i67]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) - [i66]Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf:
The Hessian perspective into the Nature of Convolutional Neural Networks. CoRR abs/2305.09088 (2023) - [i65]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) - 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]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 - [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
- [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, Jan Dirk Wegner, Aurélien Lucchi, Martin Jaggi, Thomas Hofmann, Konrad Schindler:
Learning Aerial Image Segmentation From Online Maps. IEEE Trans. Geosci. Remote. Sens. 55(11): 6054-6068 (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, 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. WWW 2017: 1045-1052 - [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
- [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. Usability Professionals 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, Carsten Eickhoff, Thomas Hofmann:
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking. WWW 2016: 927-938 - [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
- [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, Thomas Hofmann:
Modelling Term Dependence with Copulas. SIGIR 2015: 783-786 - [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
- [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 - [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) - 2011
- [e4]