no code implementations • 9 Jan 2024 • Yatong Bai, Utsav Garg, Apaar Shanker, Haoming Zhang, Samyak Parajuli, Erhan Bas, Isidora Filipovic, Amelia N. Chu, Eugenia D Fomitcheva, Elliot Branson, Aerin Kim, Somayeh Sojoudi, Kyunghyun Cho
Vision and vision-language applications of neural networks, such as image classification and captioning, rely on large-scale annotated datasets that require non-trivial data-collecting processes.
1 code implementation • ICML Workshop URL 2021 • Arnaud Fickinger, Natasha Jaques, Samyak Parajuli, Michael Chang, Nicholas Rhinehart, Glen Berseth, Stuart Russell, Sergey Levine
Unsupervised reinforcement learning (RL) studies how to leverage environment statistics to learn useful behaviors without the cost of reward engineering.
no code implementations • 1 Jan 2021 • Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer
Motivated by this, we introduce a new data augmentation method which advances the state-of-the-art and outperforms models pretrained with 1000x more labeled data.
no code implementations • 1 Nov 2020 • Hengyue Liu, Samyak Parajuli, Jesse Hostetler, Sek Chai, Bir Bhanu
Conditional computation for Deep Neural Networks (DNNs) reduce overall computational load and improve model accuracy by running a subset of the network.
1 code implementation • ICCV 2021 • Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer
We find that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work.
Ranked #29 on Domain Generalization on ImageNet-R
1 code implementation • 5 Dec 2019 • Abdul Rahman Kreidieh, Glen Berseth, Brandon Trabucco, Samyak Parajuli, Sergey Levine, Alexandre M. Bayen
This allows us to draw on connections between communication and cooperation in multi-agent RL, and demonstrate the benefits of increased cooperation between sub-policies on the training performance of the overall policy.
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • 12 Nov 2018 • Samyak Parajuli, Aswin Raghavan, Sek Chai
The use of deep neural networks in edge computing devices hinges on the balance between accuracy and complexity of computations.