7 citations · 18 across the 8 of their papers we have counts for
8 papers
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
Memory Networks: Towards Fully Biologically Plausible Learning
Jacobo Ruiz, Manas Gupta
The field of artificial intelligence faces significant challenges in achieving both biological plausibility and computational efficiency, particularly in visual learning tasks. Cur…
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks
Xue Geng, Zhe Wang, Chunyun Chen +10
Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI) tasks. However, deploying them brings significant challenges due to the huge cost of memory,…
Resource Efficient Neural Networks Using Hessian Based Pruning
Jack Chong, Manas Gupta, Lihui Chen
Neural network pruning is a practical way for reducing the size of trained models and the number of floating-point operations. One way of pruning is to use the relative Hessian tra…
Is Bio-Inspired Learning Better than Backprop? Benchmarking Bio Learning vs. Backprop
Manas Gupta, Sarthak Ketanbhai Modi, Hang Zhang +2
Bio-inspired learning has been gaining popularity recently given that Backpropagation (BP) is not considered biologically plausible. Many algorithms have been proposed in the liter…
Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning
Manas Gupta, Efe Camci, Vishandi Rudy Keneta +5
Pruning neural networks has become popular in the last decade when it was shown that a large number of weights can be safely removed from modern neural networks without compromisin…
PaRT: Parallel Learning Towards Robust and Transparent AI
Mahsa Paknezhad, Hamsawardhini Rengarajan, Chenghao Yuan +4
This paper takes a parallel learning approach for robust and transparent AI. A deep neural network is trained in parallel on multiple tasks, where each task is trained only on a su…
Learning to Prune Deep Neural Networks via Reinforcement Learning
Manas Gupta, Siddharth Aravindan, Aleksandra Kalisz +2
This paper proposes PuRL - a deep reinforcement learning (RL) based algorithm for pruning neural networks. Unlike current RL based model compression approaches where feedback is gi…