ServiceNow AI Research

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Can Active Learning Preemptively Mitigate Fairness Issues?
Can Active Learning Preemptively Mitigate Fairness Issues?
Dataset bias is one of the prevailing causes of unfairness in machine learning. Addressing fairness at the data collection and dataset …
Workshop at the International Conference on Learning Representations (ICLR),  2022.
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Object-centric Compositional Imagination for Visual Abstract Reasoning
Object-centric Compositional Imagination for Visual Abstract Reasoning
Like humans devoid of imagination, current machine learning systems lack the ability to adapt to new, unexpected situations by …
Workshop at the International Conference on Learning Representations (ICLR),  2022.
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On the Value of ML Models
On the Value of ML Models
We argue that, when establishing and benchmarking Machine Learning (ML) models, the research community should favour evaluation metrics …
Workshop at the Neural Information Processing Systems (NeurIPS),  2021.
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Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to …
Workshop at the Neural Information Processing Systems (NeurIPS),  2021.
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Typing assumptions improve identification in causal discovery - Report and comments on future directions
Typing assumptions improve identification in causal discovery - Report and comments on future directions
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an …
Workshop at the Neural Information Processing Systems (NeurIPS),  2021.
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SSR: Semi-supervised Soft Rasterizer for single-view 2D to 3D Reconstruction
SSR: Semi-supervised Soft Rasterizer for single-view 2D to 3D Reconstruction
Recent work has made significant progress in learning object meshes with weak supervision. Soft Rasterization methods have achieved …
Workshop at the International Conference on Computer Vision (ICCV),  2021.
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Typing assumptions improve identification in causal discovery
Typing assumptions improve identification in causal discovery
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an …
Workshop at the International Conference on Machine Learning (ICML),  2021.
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Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Learning the causal structure that underlies data is a crucial step towards robust real-world decision making. The majority of existing …
Workshop at the International Conference on Machine Learning (ICML),  2021.
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Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery
Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery
Cattle farming is responsible for 8.8% of greenhouse gas emissions worldwide. In addition to the methane emitted due to their digestive …
Workshop at the Neural Information Processing Systems (NeurIPS),  2020.
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Like A Researcher Stating Broader Impact for the Very First Time
Like A Researcher Stating Broader Impact for the Very First Time
In requiring that a statement of broader impact accompany all submissions for this year’s conference, the NeurIPS program chairs …
Workshop at the Neural Information Processing Systems (NeurIPS),  2020.
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