17 citations · 60 across the 34 of their papers we have counts for
7 papers · 1 filter
ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Data
Amin Banitalebi-Dehkordi, Yong Zhang
The 2021 NeurIPS Machine Learning for Combinatorial Optimization (ML4CO) competition was designed with the goal of improving state-of-the-art combinatorial optimization solvers by…
Model Composition: Can Multiple Neural Networks Be Combined into a Single Network Using Only Unlabeled Data?
Amin Banitalebi-Dehkordi, Xinyu Kang, Yong Zhang
The diversity of deep learning applications, datasets, and neural network architectures necessitates a careful selection of the architecture and data that match best to a target ap…
Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training Examples
Amin Banitalebi-Dehkordi, Yong Zhang
Convolutional Neural Networks (CNNs) for visual tasks are believed to learn both the low-level textures and high-level object attributes, throughout the network depth. This paper f…
EBJR: Energy-Based Joint Reasoning for Adaptive Inference
Mohammad Akbari, Amin Banitalebi-Dehkordi, Yong Zhang
State-of-the-art deep learning models have achieved significant performance levels on various benchmarks. However, the excellent performance comes at a cost of inefficient computat…
Auto-Split: A General Framework of Collaborative Edge-Cloud AI
Amin Banitalebi-Dehkordi, Naveen Vedula, Jian Pei +3
In many industry scale applications, large and resource consuming machine learning models reside in powerful cloud servers. At the same time, large amounts of input data are collec…
SimROD: A Simple Adaptation Method for Robust Object Detection
Rindra Ramamonjison, Amin Banitalebi-Dehkordi, Xinyu Kang +2
This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD). To overcome the challenging issues of domain shift and pseudo-label…
Revisiting Knowledge Distillation for Object Detection
Amin Banitalebi-Dehkordi
The existing solutions for object detection distillation rely on the availability of both a teacher model and ground-truth labels. We propose a new perspective to relax this constr…