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Found 12 papers, 4 papers with code
Supervised Hierarchical Clustering with Exponential Linkage
Thus we introduce an approach to supervised hierarchical clustering that smoothly interpolates between single, average, and complete linkage, and we give a training procedure that simultaneously learns a linkage function and a dissimilarity function.
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling
In climate and earth systems models, while governing equations follow from first principles and understanding of key processes has steadily improved, the largest uncertainties are often caused by parameterizations such as cloud physics, which in turn have witnessed limited improvements over the last several decades.
Clustering-based Inference for Biomedical Entity Linking
In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions.
Session-Aware Query Auto-completion using Extreme Multi-label Ranking
Previous queries in the user session can provide useful context for the user's intent and can be leveraged to suggest auto-completions that are more relevant while adhering to the user's prefix.
Stochastic Package Queries in Probabilistic Databases
We provide methods for specifying -- via a SQL extension -- and processing stochastic package queries (SPQs), in order to solve optimization problems over uncertain data, right where the data resides.
Decision Making
Decision Making Under Uncertainty
+1
Databases
Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations
Urban air pollution is a public health challenge in low- and middle-income countries (LMICs).
Robustness of Explanation Methods for NLP Models
Explanation methods have emerged as an important tool to highlight the features responsible for the predictions of neural networks.
Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization
When the similarity is measured by dot-product between dual-encoder vectors or $\ell_2$-distance, there already exist many scalable and efficient search methods.
CDA: Contrastive-adversarial Domain Adaptation
Recent advances in domain adaptation reveal that adversarial learning on deep neural networks can learn domain invariant features to reduce the shift between source and target domains.
Efficient k-NN Search with Cross-Encoders using Adaptive Multi-Round CUR Decomposition
While ANNCUR's one-time selection of anchors tends to approximate the cross-encoder distances on average, doing so forfeits the capacity to accurately estimate distances to items near the query, leading to regret in the crucial end-task: recall of top-k items.
Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions
Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference.
SUBSUME: A Dataset for Subjective Summary Extraction from Wikipedia Documents
Several datasets exist for summarization with objective intents where, for each document and intent (e. g., “weather”), a single summary suffices for all users.
