42 citations · 45 across the 4 of their papers we have counts for
7 papers
SimRec: Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item Similarity
Shaked Brody, Shoval Lagziel
Sequential recommendation systems often struggle to make predictions or take action when dealing with cold-start items that have limited amount of interactions. In this work, we pr…
On the Expressivity Role of LayerNorm in Transformers' Attention
Shaked Brody, Uri Alon, Eran Yahav
Layer Normalization (LayerNorm) is an inherent component in all Transformer-based models. In this paper, we show that LayerNorm is crucial to the expressivity of the multi-head att…
FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions
Noam Rotstein, David Bensaid, Shaked Brody +2
The advent of vision-language pre-training techniques enhanced substantial progress in the development of models for image captioning. However, these models frequently produce gene…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
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…
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…
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…