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Found 7 papers, 1 papers with code
Improving Multiple Documents Grounded Goal-Oriented Dialog Systems via Diverse Knowledge Enhanced Pretrained Language Model
In this paper, we mainly discuss about our submission to MultiDoc2Dial task, which aims to model the goal-oriented dialogues grounded in multiple documents.
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA
We first present CAMBIGNQ, a dataset consisting of 5, 654 ambiguous questions, each with relevant passages, possible answers, and a clarification question.
Plug-and-play dual-tree algorithm runtime analysis
In this paper, we present a problem-independent runtime guarantee for any dual-tree algorithm using the cover tree, separating out the problem-dependent and the problem-independent elements.
Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL
We show theoretically that minimax MTL tends to avoid worst case outcomes on newly drawn test tasks in the learning to learn (LTL) test setting.
Linear-time Algorithms for Pairwise Statistical Problems
Several key computational bottlenecks in machine learning involve pairwise distance computations, including all-nearest-neighbors (finding the nearest neighbor(s) for each point, e. g. in manifold learning) and kernel summations (e. g. in kernel density estimation or kernel machines).
Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions
The long-standing problem of efficient nearest-neighbor (NN) search has ubiquitous applications ranging from astrophysics to MP3 fingerprinting to bioinformatics to movie recommendations.
Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method
We propose a new fast Gaussian summation algorithm for high-dimensional datasets with high accuracy.
