Search Results for author: Samyak Parajuli

Found 7 papers, 3 papers with code

Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding

no code implementations9 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.

Image Captioning Image Classification +3

A Rigorous Evaluation of Real-World Distribution Shifts

no code implementations1 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.

Data Augmentation

Dynamically Throttleable Neural Networks (TNN)

no code implementations1 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.

Inter-Level Cooperation in Hierarchical Reinforcement Learning

1 code implementation5 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

Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks

no code implementations12 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.

Edge-computing General Classification +1

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