| Generating sentences from a continuous space SR Bowman, L Vilnis, O Vinyals, AM Dai, R Jozefowicz, S Bengio Conference on Computational Natural Language Learning (CoNLL), 2016 | 2813 | 2016 |
| Word Representations via Gaussian Embedding L Vilnis, A McCallum International Conference on Learning Representations (ICLR), 2015 | 628 | 2015 |
| Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning R Das, S Dhuliawala, M Zaheer, L Vilnis, I Durugkar, A Krishnamurthy, ... International Conference on Learning Representations (ICLR), 2018 | 611 | 2018 |
| Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases with Reinforcement Learning R Das, S Dhuliawala, M Zaheer, L Vilnis, I Durugkar, A Krishnamurthy, ... NIPS Workshop on Automated Knowledge Base Construction (AKBC), 2017 | 611* | 2017 |
| Adding gradient noise improves learning for very deep networks A Neelakantan, L Vilnis, QV Le, I Sutskever, L Kaiser, K Kurach, J Martens International Conference on Learning Representations Workshop (ICLR WS), 2016 | 606 | 2016 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 394 | 2024 |
| Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures L Vilnis, X Li, S Murty, A McCallum Annual Meeting of the Association for Computational Linguistics (ACL), 2018 | 145 | 2018 |
| Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking S Murty, P Verga, L Vilnis, I Radovanovic, A McCallum | 112* | |
| Smoothing the geometry of probabilistic box embeddings X Li, L Vilnis, D Zhang, M Boratko, A McCallum International conference on learning representations, 2018 | 99 | 2018 |
| Unsupervised Hypernym Detection by Distributional Inclusion Vector Embedding HS Chang, ZY Wang, L Vilnis, A McCallum North American Chapter of the Association for Computational Linguistics (NAACL), 2018 | 71* | 2018 |
| Improving local identifiability in probabilistic box embeddings S Dasgupta, M Boratko, D Zhang, L Vilnis, X Li, A McCallum Advances in Neural Information Processing Systems 33, 182-192, 2020 | 59 | 2020 |
| Dynamic knowledge-base alignment for coreference resolution J Zheng, L Vilnis, S Singh, JD Choi, A McCallum Conference on Computational Natural Language Learning (CoNLL), 2013 | 34 | 2013 |
| Bethe Projections for Non-Local Inference L Vilnis, D Belanger, D Sheldon, A McCallum Uncertainty in Artificial Intelligence (UAI), 2015 | 31 | 2015 |
| Adding gradient noise improves learning for very deep networks. arXiv 2015 A Neelakantan, L Vilnis, QV Le, I Sutskever, L Kaiser, K Kurach, J Martens arXiv preprint arXiv:1511.06807, 2020 | 27 | 2020 |
| Representing joint hierarchies with box embeddings D Patel, S Sankar Automated Knowledge Base Construction, 2020 | 24 | 2020 |
| Finer Grained Entity Typing with TypeNet S Murty, P Verga, L Vilnis, A McCallum NIPS Workshop on Automated Knowledge Base Construction (AKBC), 2017 | 24 | 2017 |
| Learning Dynamic Feature Selection for Fast Sequential Prediction E Strubell, L Vilnis, K Silverstein, A McCallum Annual Meeting of the Association for Computational Linguistics (ACL), 2015 | 20 | 2015 |
| Improved Representation Learning for Predicting Commonsense Ontologies X Li, L Vilnis, A McCallum ICML Workshop on Deep Structured Prediction (ICML WS), 2017 | 15 | 2017 |
| Capacity and bias of learned geometric embeddings for directed graphs M Boratko, D Zhang, N Monath, L Vilnis, KL Clarkson, A McCallum Advances in Neural Information Processing Systems 34, 16423-16436, 2021 | 12 | 2021 |
| Embedded-state latent conditional random fields for sequence labeling D Thai, SH Ramesh, S Murty, L Vilnis, A McCallum arXiv preprint arXiv:1809.10835, 2018 | 9 | 2018 |
Andrew McCallumDistinguished Professor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
