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20022021
most citedThe power of quantum systems on a line

220 citations

Showing cs.LGShow all

6 papers · 1 filter

cs.LG20216 cited

Which transformer architecture fits my data? A vocabulary bottleneck in self-attention

Noam Wies, Yoav Levine, Daniel Jannai +1

After their successful debut in natural language processing, Transformer architectures are now becoming the de-facto standard in many domains. An obstacle for their deployment over…

cs.LG20159 cited

Strongly Adaptive Online Learning

Amit Daniely, Alon Gonen, Shai Shalev-Shwartz

Strongly adaptive algorithms are algorithms whose performance on every time interval is close to optimal. We present a reduction that can transform standard low-regret algorithms t…

cs.LG201475 cited

On the Computational Efficiency of Training Neural Networks

Roi Livni, Shai Shalev-Shwartz, Ohad Shamir

It is well-known that neural networks are computationally hard to train. On the other hand, in practice, modern day neural networks are trained efficiently using SGD and a variety…

cs.LG2012

"Ideal Parent" Structure Learning for Continuous Variable Networks

Iftach Nachman, Gal Elidan, Nir Friedman

In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive procedu…

cs.LG20124 cited

Bounded Planning in Passive POMDPs

Roy Fox, Naftali Tishby

In Passive POMDPs actions do not affect the world state, but still incur costs. When the agent is bounded by information-processing constraints, it can only keep an approximation o…

cs.LG20124 cited

Learning the Experts for Online Sequence Prediction

Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz +1

Online sequence prediction is the problem of predicting the next element of a sequence given previous elements. This problem has been extensively studied in the context of individu…