1.3k citations · 3.2k across the 44 of their papers we have counts for
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Transfer Learning Between Related Tasks Using Expected Label Proportions
Matan Ben Noach, Yoav Goldberg
Deep learning systems thrive on abundance of labeled training data but such data is not always available, calling for alternative methods of supervision. One such method is expecta…
A Little Is Enough: Circumventing Defenses For Distributed Learning
Moran Baruch, Gilad Baruch, Yoav Goldberg
Distributed learning is central for large-scale training of deep-learning models. However, they are exposed to a security threat in which Byzantine participants can interrupt or co…
On the Practical Computational Power of Finite Precision RNNs for Language Recognition
Gail Weiss, Yoav Goldberg, Eran Yahav
While Recurrent Neural Networks (RNNs) are famously known to be Turing complete, this relies on infinite precision in the states and unbounded computation time. We consider the cas…
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
Gail Weiss, Yoav Goldberg, Eran Yahav
We present a novel algorithm that uses exact learning and abstraction to extract a deterministic finite automaton describing the state dynamics of a given trained RNN. We do this u…
On-the-fly Operation Batching in Dynamic Computation Graphs
Graham Neubig, Yoav Goldberg, Chris Dyer
Dynamic neural network toolkits such as PyTorch, DyNet, and Chainer offer more flexibility for implementing models that cope with data of varying dimensions and structure, relative…