124 citations · 504 across the 13 of their papers we have counts for
19 papers
Thinking Like Transformers
Gail Weiss, Yoav Goldberg, Eran Yahav
What is the computational model behind a Transformer? Where recurrent neural networks have direct parallels in finite state machines, allowing clear discussion and thought around a…
How Attentive are Graph Attention Networks?
Shaked Brody, Uri Alon, Eran Yahav
Graph Attention Networks (GATs) are one of the most popular GNN architectures and are considered as the state-of-the-art architecture for representation learning with graphs. In GA…
On the Bottleneck of Graph Neural Networks and its Practical Implications
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 i…
A Structural Model for Contextual Code Changes
Shaked Brody, Uri Alon, Eran Yahav
We address the problem of predicting edit completions based on a learned model that was trained on past edits. Given a code snippet that is partially edited, our goal is to predict…
A Formal Hierarchy of RNN Architectures
William Merrill, Gail Weiss, Yoav Goldberg +3
We develop a formal hierarchy of the expressive capacity of RNN architectures. The hierarchy is based on two formal properties: space complexity, which measures the RNN's memory, a…
Learning Deterministic Weighted Automata with Queries and Counterexamples
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). The…
Adversarial Examples for Models of Code
Noam Yefet, Uri Alon, Eran Yahav
Neural models of code have shown impressive results when performing tasks such as predicting method names and identifying certain kinds of bugs. We show that these models are vulne…
Structural Language Models of Code
Uri Alon, Roy Sadaka, Omer Levy +1
We address the problem of any-code completion - generating a missing piece of source code in a given program without any restriction on the vocabulary or structure. We introduce a…
Towards Neural Decompilation
Omer Katz, Yuval Olshaker, Yoav Goldberg +1
We address the problem of automatic decompilation, converting a program in low-level representation back to a higher-level human-readable programming language. The problem of decom…
Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs
Yaniv David, Uri Alon, Eran Yahav
We address the problem of reverse engineering of stripped executables, which contain no debug information. This is a challenging problem because of the low amount of syntactic info…
code2seq: Generating Sequences from Structured Representations of Code
Uri Alon, Shaked Brody, Omer Levy +1
The ability to generate natural language sequences from source code snippets has a variety of applications such as code summarization, documentation, and retrieval. Sequence-to-seq…
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…
A General Path-Based Representation for Predicting Program Properties
Uri Alon, Meital Zilberstein, Omer Levy +1
Predicting program properties such as names or expression types has a wide range of applications. It can ease the task of programming and increase programmer productivity. A major…
code2vec: Learning Distributed Representations of Code
Uri Alon, Meital Zilberstein, Omer Levy +1
We present a neural model for representing snippets of code as continuous distributed vectors ("code embeddings"). The main idea is to represent a code snippet as a single fixed-le…
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…
Programming Not Only by Example
Hila Peleg, Sharon Shoham, Eran Yahav
In recent years, there has been tremendous progress in automated synthesis techniques that are able to automatically generate code based on some intent expressed by the programmer.…
Learning Disjunctions of Predicates
Nader H. Bshouty, Dana Drachsler-Cohen, Martin Vechev +1
Let be a set of boolean functions. We present an algorithm for learning from membership queries. Our algorithm asks at most $…
Optimal Learning of Specifications from Examples
Dana Drachsler-Cohen, Martin Vechev, Eran Yahav
A fundamental challenge in synthesis from examples is designing a learning algorithm that poses the minimal number of questions to an end user while guaranteeing that the target hy…
Exploiting Social Navigation
Meital Ben Sinai, Nimrod Partush, Shir Yadid +1
We present an effective Sybil attack against social location based services. Our attack is based on creating a large number of reputed "bot drivers", and controlling their reported…