50 citations · 171 across the 14 of their papers we have counts for
14 papers
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…
EDICT: Exact Diffusion Inversion via Coupled Transformations
Bram Wallace, Akash Gokul, Nikhil Naik
Finding an initial noise vector that produces an input image when fed into the diffusion process (known as inversion) is an important problem in denoising diffusion models (DDMs),…
Control Barrier Function Contracts for Vehicular Mission Planning Under Signal Temporal Logic Specifications
Muhammad Waqas, Nikhil Vijay Naik, Petros Ioannou +1
We present a compositional control synthesis method based on assume-guarantee contracts with application to correct-by-construction design of vehicular mission plans. In our approa…
ProGen2: Exploring the Boundaries of Protein Language Models
Erik Nijkamp, Jeffrey Ruffolo, Eli N. Weinstein +2
Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein…
Can domain adaptation make object recognition work for everyone?
Viraj Prabhu, Ramprasaath R. Selvaraju, Judy Hoffman +1
Despite the rapid progress in deep visual recognition, modern computer vision datasets significantly overrepresent the developed world and models trained on such datasets underperf…
CLIP-Lite: Information Efficient Visual Representation Learning with Language Supervision
Aman Shrivastava, Ramprasaath R. Selvaraju, Nikhil Naik +1
We propose CLIP-Lite, an information efficient method for visual representation learning by feature alignment with textual annotations. Compared to the previously proposed CLIP mod…
PreViTS: Contrastive Pretraining with Video Tracking Supervision
Brian Chen, Ramprasaath R. Selvaraju, Shih-Fu Chang +2
Videos are a rich source for self-supervised learning (SSL) of visual representations due to the presence of natural temporal transformations of objects. However, current methods t…
Field Extraction from Forms with Unlabeled Data
Mingfei Gao, Zeyuan Chen, Nikhil Naik +3
We propose a novel framework to conduct field extraction from forms with unlabeled data. To bootstrap the training process, we develop a rule-based method for mining noisy pseudo-l…
Deep Extrapolation for Attribute-Enhanced Generation
Alvin Chan, Ali Madani, Ben Krause +1
Attribute extrapolation in sample generation is challenging for deep neural networks operating beyond the training distribution. We formulate a new task for extrapolation in sequen…
CASTing Your Model: Learning to Localize Improves Self-Supervised Representations
Ramprasaath R. Selvaraju, Karan Desai, Justin Johnson +1
Recent advances in self-supervised learning (SSL) have largely closed the gap with supervised ImageNet pretraining. Despite their success these methods have been primarily applied…
Designing Interpretable Approximations to Deep Reinforcement Learning
Nathan Dahlin, Krishna Chaitanya Kalagarla, Nikhil Naik +2
In an ever expanding set of research and application areas, deep neural networks (DNNs) set the bar for algorithm performance. However, depending upon additional constraints such a…
The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
Stephan Zheng, Alexander Trott, Sunil Srinivasa +4
Tackling real-world socio-economic challenges requires designing and testing economic policies. However, this is hard in practice, due to a lack of appropriate (micro-level) econom…
Improving out-of-distribution generalization via multi-task self-supervised pretraining
Isabela Albuquerque, Nikhil Naik, Junnan Li +2
Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness. We show that features obtained us…
ProGen: Language Modeling for Protein Generation
Ali Madani, Bryan McCann, Nikhil Naik +5
Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. We pose protein engineering as an unsupervi…