7 citations · 18 across the 9 of their papers we have counts for
11 papers
To Copy or Not to Copy: Controlling Speculative Decoding via Intrinsic Model Signals
Roy Eisenstadt, Ido Cohen, Edo Cohen-Karlik +2
Speculative Decoding (SD) has significantly accelerated Large Language Model (LLM) inference, yet existing approaches face a fundamental tradeoff between two drafting strategies: n…
Provable Benefits of Complex Parameterizations for Structured State Space Models
Yuval Ran-Milo, Eden Lumbroso, Edo Cohen-Karlik +3
Structured state space models (SSMs), the core engine behind prominent neural networks such as S4 and Mamba, are linear dynamical systems adhering to a specified structure, most no…
Overcoming Order in Autoregressive Graph Generation
Edo Cohen-Karlik, Eyal Rozenberg, Daniel Freedman
Graph generation is a fundamental problem in various domains, including chemistry and social networks. Recent work has shown that molecular graph generation using recurrent neural…
Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States
Noam Razin, Yotam Alexander, Edo Cohen-Karlik +3
In modern machine learning, models can often fit training data in numerous ways, some of which perform well on unseen (test) data, while others do not. Remarkably, in such cases gr…
Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets
Edo Cohen-Karlik, Itamar Menuhin-Gruman, Raja Giryes +2
Overparameterization in deep learning typically refers to settings where a trained neural network (NN) has representational capacity to fit the training data in many ways, some of…
On the Implicit Bias of Gradient Descent for Temporal Extrapolation
Edo Cohen-Karlik, Avichai Ben David, Nadav Cohen +1
When using recurrent neural networks (RNNs) it is common practice to apply trained models to sequences longer than those seen in training. This "extrapolating" usage deviates from…
Regularizing Towards Permutation Invariance in Recurrent Models
Edo Cohen-Karlik, Avichai Ben David, Amir Globerson
In many machine learning problems the output should not depend on the order of the input. Such "permutation invariant" functions have been studied extensively recently. Here we arg…
The workweek is the best time to start a family -- A Study of GPT-2 Based Claim Generation
Shai Gretz, Yonatan Bilu, Edo Cohen-Karlik +1
Argument generation is a challenging task whose research is timely considering its potential impact on social media and the dissemination of information. Here we suggest a pipeline…
A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis
Shai Gretz, Roni Friedman, Edo Cohen-Karlik +4
Identifying the quality of free-text arguments has become an important task in the rapidly expanding field of computational argumentation. In this work, we explore the challenging…
Automatic Argument Quality Assessment -- New Datasets and Methods
Assaf Toledo, Shai Gretz, Edo Cohen-Karlik +6
We explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data.…
Learning to combine Grammatical Error Corrections
Yoav Kantor, Yoav Katz, Leshem Choshen +5
The field of Grammatical Error Correction (GEC) has produced various systems to deal with focused phenomena or general text editing. We propose an automatic way to combine black-bo…