most citedDesigning Neural Network Architectures using Reinforcement Learning

424 citations · 504 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2024

BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models

Senthil Purushwalkam, Akash Gokul, Shafiq Joty +1

Recent text-to-image generation models have demonstrated incredible success in generating images that faithfully follow input prompts. However, the requirement of using words to de…

cs.CV20232 cited

ConRad: Image Constrained Radiance Fields for 3D Generation from a Single Image

Senthil Purushwalkam, Nikhil Naik

We present a novel method for reconstructing 3D objects from a single RGB image. Our method leverages the latest image generation models to infer the hidden 3D structure while rema…

cs.CV2023

End-to-End Diffusion Latent Optimization Improves Classifier Guidance

Bram Wallace, Akash Gokul, Stefano Ermon +1

Classifier guidance -- using the gradients of an image classifier to steer the generations of a diffusion model -- has the potential to dramatically expand the creative control ove…

cs.LG2016424 cited

Designing Neural Network Architectures using Reinforcement Learning

Bowen Baker, Otkrist Gupta, Nikhil Naik +1

At present, designing convolutional neural network (CNN) architectures requires both human expertise and labor. New architectures are handcrafted by careful experimentation or modi…

cs.CV201652 cited

Deep Learning the City : Quantifying Urban Perception At A Global Scale

Abhimanyu Dubey, Nikhil Naik, Devi Parikh +2

Computer vision methods that quantify the perception of urban environment are increasingly being used to study the relationship between a city's physical appearance and the behavio…

cs.CY201618 cited

Are Safer Looking Neighborhoods More Lively? A Multimodal Investigation into Urban Life

Marco De Nadai, Radu L. Vieriu, Gloria Zen +6

Policy makers, urban planners, architects, sociologists, and economists are interested in creating urban areas that are both lively and safe. But are the safety and liveliness of n…