Or Sharir

Orcid: 0000-0003-4957-8957

According to our database1, Or Sharir authored at least 19 papers between 2016 and 2024.

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Bibliography

2024
ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs.
CoRR, 2024

2023
Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs.
CoRR, 2023

2022
Towards Neural Variational Monte Carlo That Scales Linearly with System Size.
CoRR, 2022

2021
Neural tensor contractions and the expressive power of deep neural quantum states.
CoRR, 2021

2020
Technical Report: Auxiliary Tuning and its Application to Conditional Text Generation.
CoRR, 2020

The Cost of Training NLP Models: A Concise Overview.
CoRR, 2020

Limits to Depth Efficiencies of Self-Attention.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

SenseBERT: Driving Some Sense into BERT.
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019
SenseBERT: Driving Some Sense into BERT.
CoRR, 2019

Deep autoregressive models for the efficient variational simulation of many-body quantum systems.
CoRR, 2019

2018
Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks.
CoRR, 2018

On the Expressive Power of Overlapping Architectures of Deep Learning.
Proceedings of the 6th International Conference on Learning Representations, 2018

Benefits of Depth for Long-Term Memory of Recurrent Networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

Sum-Product-Quotient Networks.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
On the Expressive Power of Overlapping Operations of Deep Networks.
CoRR, 2017

Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions.
CoRR, 2017

2016
Tensorial Mixture Models.
CoRR, 2016

Deep SimNets.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

On the Expressive Power of Deep Learning: A Tensor Analysis.
Proceedings of the 29th Conference on Learning Theory, 2016


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