176 citations · 368 across the 18 of their papers we have counts for
18 papers
Training Video Foundation Models with NVIDIA NeMo
Zeeshan Patel, Ethan He, Parth Mannan +26
Video Foundation Models (VFMs) have recently been used to simulate the real world to train physical AI systems and develop creative visual experiences. However, there are significa…
Multi-Objective Neural Architecture Search by Learning Search Space Partitions
Yiyang Zhao, Linnan Wang, Tian Guo
Deploying deep learning models requires taking into consideration neural network metrics such as model size, inference latency, and #FLOPs, aside from inference accuracy. This resu…
GPUNet: Searching the Deployable Convolution Neural Networks for GPUs
Linnan Wang, Chenhan Yu, Satish Salian +3
Customizing Convolution Neural Networks (CNN) for production use has been a challenging task for DL practitioners. This paper intends to expedite the model customization with a mod…
Multi-objective Optimization by Learning Space Partitions
Yiyang Zhao, Linnan Wang, Kevin Yang +3
In contrast to single-objective optimization (SOO), multi-objective optimization (MOO) requires an optimizer to find the Pareto frontier, a subset of feasible solutions that are no…
Learning Space Partitions for Path Planning
Kevin Yang, Tianjun Zhang, Chris Cummins +6
Path planning, the problem of efficiently discovering high-reward trajectories, often requires optimizing a high-dimensional and multimodal reward function. Popular approaches like…
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search
Linnan Wang, Rodrigo Fonseca, Yuandong Tian
High dimensional black-box optimization has broad applications but remains a challenging problem to solve. Given a set of samples $\{\vx_i, y_i\}$, building a global model (like Ba…