Search Results for author: Otilia Stretcu

Found 5 papers, 3 papers with code

Coarse-to-Fine Curriculum Learning

no code implementations8 Jun 2021 Otilia Stretcu, Emmanouil Antonios Platanios, Tom M. Mitchell, Barnabás Póczos

However, in machine learning, models are most often trained to solve the target tasks directly. Inspired by human learning, we propose a novel curriculum learning approach which decomposes challenging tasks into sequences of easier intermediate goals that are used to pre-train a model before tackling the target task.

Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction

1 code implementation NeurIPS 2020 Mariya Toneva, Otilia Stretcu, Barnabas Poczos, Leila Wehbe, Tom M. Mitchell

These results suggest that only the end of semantic processing of a word is task-dependent, and pose a challenge for future research to formulate new hypotheses for earlier task effects as a function of the task and stimuli.

Graph Agreement Models for Semi-Supervised Learning

1 code implementation NeurIPS 2019 Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil Platanios, Sujith Ravi, Andrew Tomkins

To address this, we propose Graph Agreement Models (GAM), which introduces an auxiliary model that predicts the probability of two nodes sharing the same label as a learned function of their features.

Classification General Classification +2

Competence-based Curriculum Learning for Neural Machine Translation

1 code implementation NAACL 2019 Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, Tom M. Mitchell

In this paper, we propose a curriculum learning framework for NMT that reduces training time, reduces the need for specialized heuristics or large batch sizes, and results in overall better performance.

Machine Translation Translation

Efficient Multitask Feature and Relationship Learning

no code implementations14 Feb 2017 Han Zhao, Otilia Stretcu, Alex Smola, Geoff Gordon

In this paper, we consider a formulation of multitask learning that learns the relationships both between tasks and between features, represented through a task covariance and a feature covariance matrix, respectively.

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