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07/31/2023
Structural Transfer Learning in NL-to-Bash Semantic Parsers
Large-scale pre-training has made progress in many fields of natural lan...
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06/20/2023
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization
With the increase in the scale of Deep Learning (DL) training workloads ...
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11/22/2022
Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions
Despite the widespread success of Transformers on NLP tasks, recent work...
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03/14/2022
Revisiting the Compositional Generalization Abilities of Neural Sequence Models
Compositional generalization is a fundamental trait in humans, allowing ...
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03/12/2021
Are NLP Models really able to Solve Simple Math Word Problems?
The problem of designing NLP solvers for math word problems (MWP) has se...
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11/08/2020
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages
While recurrent models have been effective in NLP tasks, their performan...
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09/23/2020
On the Ability of Self-Attention Networks to Recognize Counter Languages
Transformers have supplanted recurrent models in a large number of NLP t...
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06/16/2020
On the Computational Power of Transformers and Its Implications in Sequence Modeling
Transformers are being used extensively across several sequence modeling...
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12/07/2019
