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Found 5 papers, 2 papers with code
Resources and Evaluations for Multi-Distribution Dense Information Retrieval
We introduce and define the novel problem of multi-distribution information retrieval (IR) where given a query, systems need to retrieve passages from within multiple collections, each drawn from a different distribution.
Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning
The options framework in Hierarchical Reinforcement Learning breaks down overall goals into a combination of options or simpler tasks and associated policies, allowing for abstraction in the action space.
Accurate Online Posterior Alignments for Principled Lexically-Constrained Decoding
Online alignment in machine translation refers to the task of aligning a target word to a source word when the target sequence has only been partially decoded.
Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification
Such a joint learning is expected to provide a twofold advantage: i) the classifier generalizes better as it leverages the prior knowledge of existence of a hierarchy over the labels, and ii) in addition to the label co-occurrence information, the label-embedding may benefit from the manifold structure of the input datapoints, leading to embeddings that are more faithful to the label hierarchy.
Ranked #1 on
Multi-Label Text Classification
on RCV1
General Classification
Hierarchical Multi-label Classification
+1
Model-agnostic Fits for Understanding Information Seeking Patterns in Humans
In decision making tasks under uncertainty, humans display characteristic biases in seeking, integrating, and acting upon information relevant to the task.
