- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Europe > Germany (0.04)
- Asia > Singapore (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- North America > United States > New Jersey > Mercer County > Princeton (0.04)
- North America > Canada > British Columbia > Vancouver (0.04)
- Europe > United Kingdom > England > Oxfordshire > Oxford (0.04)
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- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.48)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Fuzzy Logic (0.41)
- North America > United States > New Jersey > Mercer County > Princeton (0.04)
- North America > Canada > British Columbia > Vancouver (0.04)
- Europe > United Kingdom > England > Oxfordshire > Oxford (0.04)
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- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.48)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Fuzzy Logic (0.41)
Beyond Grids: Learning Graph Representations for Visual Recognition
Neural Information Processing Systems http://nips.cc/
- North America > United States > Wisconsin > Dane County > Madison (0.04)
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- North America > Canada > Quebec > Montreal (0.04)
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
Elliot Meyerson, Risto Miikkulainen
Neural Information Processing Systems http://nips.cc/
8526e0962a844e4a2f158d831d5fddf7-AuthorFeedback.pdf
Thank you to the reviewers for the feedback. MUiR outperforms the existing methods10 (Table 1). Interestingly, the existing methods each outperforms single task learning (STL) on two out of three tasks. Thismethod (Table1: MUiR+Hierarchical Init.) stilloutperforms theprevious methods on15 all tasks, but may be better or worse than MUiR for a given task. This result confirms the value of MUiR as a framework, and16 indicates thatexploring initialization schemes isapromising areaoffuture work.
Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation Yuan Wang
Previous works focus their efforts on exploring the support information while paying less attention to the mining of the critical query branch.
- Asia > Middle East > Israel (0.04)
- Asia > China > Anhui Province > Hefei (0.04)
- Information Technology > Sensing and Signal Processing > Image Processing (1.00)
- Information Technology > Artificial Intelligence > Vision (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.93)
- Information Technology > Artificial Intelligence > Natural Language (0.70)
Putting An End to End-to-End: Gradient-Isolated Learning of Representations
Sindy Löwe, Peter O'Connor, Bastiaan Veeling
In this paper, we introduce a novel learning approach,Greedy InfoMax (GIM), that improves upon these problems.
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- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)