2 citations · 4 across the 6 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…