Search Results for author: Eran Yahav

Found 16 papers, 13 papers with code

Thinking Like Transformers

1 code implementation13 Jun 2021 Gail Weiss, Yoav Goldberg, Eran Yahav

In this paper we aim to change that, proposing a computational model for the transformer-encoder in the form of a programming language.

How Attentive are Graph Attention Networks?

3 code implementations30 May 2021 Shaked Brody, Uri Alon, Eran Yahav

However, in this paper we show that GATs can only compute a restricted kind of attention where the ranking of attended nodes is unconditioned on the query node.

Graph Attention Graph Property Prediction +3

On the Bottleneck of Graph Neural Networks and its Practical Implications

1 code implementation ICLR 2021 Uri Alon, Eran Yahav

Since the proposal of the graph neural network (GNN) by Gori et al. (2005) and Scarselli et al. (2008), one of the major problems in training GNNs was their struggle to propagate information between distant nodes in the graph.

A Structural Model for Contextual Code Changes

1 code implementation27 May 2020 Shaked Brody, Uri Alon, Eran Yahav

We conduct a thorough evaluation, comparing our approach to a variety of representation and modeling approaches that are driven by multiple strong models such as LSTMs, Transformers, and neural CRFs.


A Formal Hierarchy of RNN Architectures

no code implementations ACL 2020 William Merrill, Gail Weiss, Yoav Goldberg, Roy Schwartz, Noah A. Smith, Eran Yahav

While formally extending these findings to unsaturated RNNs is left to future work, we hypothesize that the practical learnable capacity of unsaturated RNNs obeys a similar hierarchy.

Learning Deterministic Weighted Automata with Queries and Counterexamples

1 code implementation NeurIPS 2019 Gail Weiss, Yoav Goldberg, Eran Yahav

We present an algorithm for extraction of a probabilistic deterministic finite automaton (PDFA) from a given black-box language model, such as a recurrent neural network (RNN).

Language Modelling

Adversarial Examples for Models of Code

3 code implementations15 Oct 2019 Noam Yefet, Uri Alon, Eran Yahav

Our evaluations demonstrate that DAMP has up to 89% success rate in changing a prediction to the adversary's choice (a targeted attack) and a success rate of up to 94% in changing a given prediction to any incorrect prediction (a non-targeted attack).

Structural Language Models of Code

1 code implementation ICML 2020 Uri Alon, Roy Sadaka, Omer Levy, Eran Yahav

We introduce a new approach to any-code completion that leverages the strict syntax of programming languages to model a code snippet as a tree - structural language modeling (SLM).

Code Completion Code Generation +1

Towards Neural Decompilation

no code implementations20 May 2019 Omer Katz, Yuval Olshaker, Yoav Goldberg, Eran Yahav

We address the problem of automatic decompilation, converting a program in low-level representation back to a higher-level human-readable programming language.

Machine Translation

Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs

1 code implementation25 Feb 2019 Yaniv David, Uri Alon, Eran Yahav

This is a challenging problem because of the low amount of syntactic information available in stripped executables, and the diverse assembly code patterns arising from compiler optimizations.

code2seq: Generating Sequences from Structured Representations of Code

5 code implementations ICLR 2019 Uri Alon, Shaked Brody, Omer Levy, Eran Yahav

The ability to generate natural language sequences from source code snippets has a variety of applications such as code summarization, documentation, and retrieval.

Code Summarization Source Code Summarization

On the Practical Computational Power of Finite Precision RNNs for Language Recognition

1 code implementation ACL 2018 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.

code2vec: Learning Distributed Representations of Code

9 code implementations26 Mar 2018 Uri Alon, Meital Zilberstein, Omer Levy, Eran Yahav

We demonstrate the effectiveness of our approach by using it to predict a method's name from the vector representation of its body.

A General Path-Based Representation for Predicting Program Properties

3 code implementations26 Mar 2018 Uri Alon, Meital Zilberstein, Omer Levy, Eran Yahav

A major challenge when learning from programs is $\textit{how to represent programs in a way that facilitates effective learning}$.

Learning Disjunctions of Predicates

no code implementations15 Jun 2017 Nader H. Bshouty, Dana Drachsler-Cohen, Martin Vechev, Eran Yahav

Our algorithm asks at most $|F| \cdot OPT(F_\vee)$ membership queries where $OPT(F_\vee)$ is the minimum worst case number of membership queries for learning $F_\vee$.

Program Synthesis

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