12 citations · 39 across the 18 of their papers we have counts for
37 papers
Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks
Mehdi Seyfi, Amin Banitalebi-Dehkordi, Zirui Zhou +1
Combinatorial optimization finds an optimal solution within a discrete set of variables and constraints. The field has seen tremendous progress both in research and industry. With…
ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages
Mohammad Akbari, Saeed Ranjbar Alvar, Behnam Kamranian +2
Building multi-modal language models has been a trend in the recent years, where additional modalities such as image, video, speech, etc. are jointly learned along with natural lan…
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
Rindranirina Ramamonjison, Timothy T. Yu, Raymond Li +8
The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text…
Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions
Rindranirina Ramamonjison, Haley Li, Timothy T. Yu +5
We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formula…
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding
Morgan Heisler, Amin Banitalebi-Dehkordi, Yong Zhang
Data augmentation is an essential technique in improving the generalization of deep neural networks. The majority of existing image-domain augmentations either rely on geometric an…
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch
Tianyu Zhang, Amin Banitalebi-Dehkordi, Yong Zhang
Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-the-art handcra…