Tsendsuren Munkhdalai | DeepAI

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∙ 11/02/2021

Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP

Meta-learning considers the problem of learning an efficient learning pr...

7 Trapit Bansal, et al. ∙

∙ 10/05/2021

Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition

Fast contextual adaptation has shown to be effective in improving Automa...

0 Tsendsuren Munkhdalai, et al. ∙

∙ 11/16/2020

Learning Associative Inference Using Fast Weight Memory

Humans can quickly associate stimuli to solve problems in novel contexts...

8 Imanol Schlag, et al. ∙

∙ 09/17/2020

Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks

Self-supervised pre-training of transformer models has revolutionized NL...

0 Trapit Bansal, et al. ∙

∙ 09/03/2020

Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling

Training a deep neural network requires a large amount of single-task da...

0 Tsendsuren Munkhdalai, et al. ∙

∙ 05/07/2020

A Locally Adaptive Interpretable Regression

Machine learning models with both good predictability and high interpret...

0 Lkhagvadorj Munkhdalai, et al. ∙

∙ 05/02/2020

Exploring and Predicting Transferability across NLP Tasks

Recent advances in NLP demonstrate the effectiveness of training large-s...

0 Tu Vu, et al. ∙

∙ 07/23/2019

Metalearned Neural Memory

We augment recurrent neural networks with an external memory mechanism t...

10 Tsendsuren Munkhdalai, et al. ∙

∙ 10/12/2018

Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension

We propose a neural machine-reading model that constructs dynamic knowle...

0 Rajarshi Das, et al. ∙

∙ 07/12/2018

Metalearning with Hebbian Fast Weights

We unify recent neural approaches to one-shot learning with older ideas ...

4 Tsendsuren Munkhdalai, et al. ∙

∙ 04/20/2018

Sentence Simplification with Memory-Augmented Neural Networks

Sentence simplification aims to simplify the content and structure of co...

0 Tu Vu, et al. ∙

∙ 12/28/2017

Rapid Adaptation with Conditionally Shifted Neurons

We describe a mechanism by which artificial neural networks can learn ra...

0 Tsendsuren Munkhdalai, et al. ∙

∙ 12/28/2017

Learning Rapid-Temporal Adaptations

A hallmark of human intelligence and cognition is its flexibility. One o...

0 Tsendsuren Munkhdalai, et al. ∙

∙ 03/02/2017

Meta Networks

Neural networks have been successfully applied in applications with a la...

0 Tsendsuren Munkhdalai, et al. ∙

∙ 02/15/2017

An Analysis of Ability in Deep Neural Networks

Deep neural networks (DNNs) have made significant progress in a number o...

0 John P. Lalor, et al. ∙

∙ 10/20/2016

Reasoning with Memory Augmented Neural Networks for Language Comprehension

Hypothesis testing is an important cognitive process that supports human...

0 Tsendsuren Munkhdalai, et al. ∙

∙ 07/15/2016

Neural Tree Indexers for Text Understanding

Recurrent neural networks (RNNs) process input text sequentially and mod...

0 Tsendsuren Munkhdalai, et al. ∙

∙ 07/14/2016

Neural Semantic Encoders

We present a memory augmented neural network for natural language unders...

0 Tsendsuren Munkhdalai, et al. ∙

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