LEMMA
96 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in LEMMA
Most implemented papers
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
cyanrain7/trust-region-policy-optimisation-in-multi-agent-reinforcement-learning
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ICLR 2022
In this paper, we extend the theory of trust region learning to MARL.
UniMorph 2.0: Universal Morphology
The Universal Morphology UniMorph project is a collaborative effort to improve how NLP handles complex morphology across the world's languages.
Deep Generation of Coq Lemma Names Using Elaborated Terms
EngineeringSoftware/roosterize
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Our results show that Roosterize substantially outperforms baselines for suggesting lemma names, highlighting the importance of using multi-input models and elaborated terms.
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent
We propose HyperAgent, a reinforcement learning (RL) algorithm based on the hypermodel framework for exploration in RL.
Morphological analysis using a sequence decoder
Our Morse implementation and the TrMor2018 dataset are available online to support future research\footnote{See \url{https://github. com/ai-ku/Morse. jl} for a Morse implementation in Julia/Knet \cite{knet2016mlsys} and \url{https://github. com/ai-ku/TrMor2018} for the new Turkish dataset.
Improving Lemmatization of Non-Standard Languages with Joint Learning
emanjavacas/pie
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NAACL 2019
Lemmatization of standard languages is concerned with (i) abstracting over morphological differences and (ii) resolving token-lemma ambiguities of inflected words in order to map them to a dictionary headword.
Deterministic tensor completion with hypergraph expanders
We provide a novel analysis of low-rank tensor completion based on hypergraph expanders.
Generalizing to unseen domains via distribution matching
belaalb/g2dm
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In this work, we tackle such problem by focusing on domain generalization: a formalization where the data generating process at test time may yield samples from never-before-seen domains (distributions).
On the distance between two neural networks and the stability of learning
This paper relates parameter distance to gradient breakdown for a broad class of nonlinear compositional functions.
Post-selection inference with HSIC-Lasso
Detecting influential features in non-linear and/or high-dimensional data is a challenging and increasingly important task in machine learning.
