261 citations · 890 across the 15 of their papers we have counts for
16 papers
Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs
Or Sharir, Anima Anandkumar
Deep learning often faces the challenge of efficiently processing dynamic inputs, such as sensor data or user inputs. For example, an AI writing assistant is required to update its…
Towards Neural Variational Monte Carlo That Scales Linearly with System Size
Or Sharir, Garnet Kin-Lic Chan, Anima Anandkumar
Quantum many-body problems are some of the most challenging problems in science and are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductor…
Neural tensor contractions and the expressive power of deep neural quantum states
Or Sharir, Amnon Shashua, Giuseppe Carleo
We establish a direct connection between general tensor networks and deep feed-forward artificial neural networks. The core of our results is the construction of neural-network lay…
Technical Report: Auxiliary Tuning and its Application to Conditional Text Generation
Yoel Zeldes, Dan Padnos, Or Sharir +1
We introduce a simple and efficient method, called Auxiliary Tuning, for adapting a pre-trained Language Model to a novel task; we demonstrate this approach on the task of conditio…
The Depth-to-Width Interplay in Self-Attention
Yoav Levine, Noam Wies, Or Sharir +2
Self-attention architectures, which are rapidly pushing the frontier in natural language processing, demonstrate a surprising depth-inefficient behavior: previous works indicate th…
The Cost of Training NLP Models: A Concise Overview
Or Sharir, Barak Peleg, Yoav Shoham
We review the cost of training large-scale language models, and the drivers of these costs. The intended audience includes engineers and scientists budgeting their model-training e…
SenseBERT: Driving Some Sense into BERT
Yoav Levine, Barak Lenz, Or Dagan +6
The ability to learn from large unlabeled corpora has allowed neural language models to advance the frontier in natural language understanding. However, existing self-supervision t…
Deep autoregressive models for the efficient variational simulation of many-body quantum systems
Or Sharir, Yoav Levine, Noam Wies +2
Artificial Neural Networks were recently shown to be an efficient representation of highly-entangled many-body quantum states. In practical applications, neural-network states inhe…
Quantum Entanglement in Deep Learning Architectures
Yoav Levine, Or Sharir, Nadav Cohen +1
Modern deep learning has enabled unprecedented achievements in various domains. Nonetheless, employment of machine learning for wave function representations is focused on more tra…
On the Long-Term Memory of Deep Recurrent Networks
Yoav Levine, Or Sharir, Alon Ziv +1
A key attribute that drives the unprecedented success of modern Recurrent Neural Networks (RNNs) on learning tasks which involve sequential data, is their ability to model intricat…
Sum-Product-Quotient Networks
Or Sharir, Amnon Shashua
We present a novel tractable generative model that extends Sum-Product Networks (SPNs) and significantly boosts their power. We call it Sum-Product-Quotient Networks (SPQNs), whose…
Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions
Nadav Cohen, Or Sharir, Yoav Levine +3
The driving force behind convolutional networks - the most successful deep learning architecture to date, is their expressive power. Despite its wide acceptance and vast empirical…
On the Expressive Power of Overlapping Architectures of Deep Learning
Or Sharir, Amnon Shashua
Expressive efficiency refers to the relation between two architectures A and B, whereby any function realized by B could be replicated by A, but there exists functions realized by…
Tensorial Mixture Models
Or Sharir, Ronen Tamari, Nadav Cohen +1
Casting neural networks in generative frameworks is a highly sought-after endeavor these days. Contemporary methods, such as Generative Adversarial Networks, capture some of the ge…
On the Expressive Power of Deep Learning: A Tensor Analysis
Nadav Cohen, Or Sharir, Amnon Shashua
It has long been conjectured that hypotheses spaces suitable for data that is compositional in nature, such as text or images, may be more efficiently represented with deep hierarc…
Deep SimNets
Nadav Cohen, Or Sharir, Amnon Shashua
We present a deep layered architecture that generalizes convolutional neural networks (ConvNets). The architecture, called SimNets, is driven by two operators: (i) a similarity fun…