Search Results for author:
Found 27 papers, 14 papers with code
BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models
We propose a novel architecture (BootPIG) that allows a user to provide reference images of an object in order to guide the appearance of a concept in the generated images.
Diffusion Model Alignment Using Direct Preference Optimization
Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users' preferences.
End-to-End Diffusion Latent Optimization Improves Classifier Guidance
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 over image generation and editing.
EDICT: Exact Diffusion Inversion via Coupled Transformations
EDICT enables mathematically exact inversion of real and model-generated images by maintaining two coupled noise vectors which are used to invert each other in an alternating fashion.
ProGen2: Exploring the Boundaries of Protein Language Models
Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein design.
Can domain adaptation make object recognition work for everyone?
Despite the rapid progress in deep visual recognition, modern computer vision datasets significantly overrepresent the developed world and models trained on such datasets underperform on images from unseen geographies.
CLIP-Lite: Information Efficient Visual Representation Learning with Language Supervision
We propose CLIP-Lite, an information efficient method for visual representation learning by feature alignment with textual annotations.
PreViTS: Contrastive Pretraining with Video Tracking Supervision
In this work, we propose PreViTS, an SSL framework that utilizes an unsupervised tracking signal for selecting clips containing the same object, which helps better utilize temporal transformations of objects.
Field Extraction from Forms with Unlabeled Data
We propose a novel framework to conduct field extraction from forms with unlabeled data.
Don’t throw away that linear head: Few-shot protein fitness prediction with generative models
Predicting the fitness, i. e. functional value, of a protein sequence is an important and challenging task in biology, particularly due to the scarcity of assay-labeled data.
Deep Extrapolation for Attribute-Enhanced Generation
Attribute extrapolation in sample generation is challenging for deep neural networks operating beyond the training distribution.
CASTing Your Model: Learning to Localize Improves Self-Supervised Representations
Recent advances in self-supervised learning (SSL) have largely closed the gap with supervised ImageNet pretraining.
Designing Interpretable Approximations to Deep Reinforcement Learning
In an ever expanding set of research and application areas, deep neural networks (DNNs) set the bar for algorithm performance.
The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
In experiments conducted on MTurk, an AI tax policy provides an equality-productivity trade-off that is similar to that provided by the Saez framework along with higher inverse-income weighted social welfare.
Improving out-of-distribution generalization via multi-task self-supervised pretraining
Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness.
Ranked #116 on
Domain Generalization
on PACS
ProGen: Language Modeling for Protein Generation
Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science.
DIME: AN INFORMATION-THEORETIC DIFFICULTY MEASURE FOR AI DATASETS
Empirically, we estimate this lower bound using a neural network to compute DIME.
Maximum-Entropy Fine Grained Classification
Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data.
Ranked #19 on
Fine-Grained Image Classification
on NABirds
(using extra training data)
Maximum-Entropy Fine-Grained Classification
Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data.
Accelerating Neural Architecture Search using Performance Prediction
Methods for neural network hyperparameter optimization and meta-modeling are computationally expensive due to the need to train a large number of model configurations.
Pairwise Confusion for Fine-Grained Visual Classification
Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity.
Ranked #16 on
Fine-Grained Image Classification
on Stanford Dogs
Designing Neural Network Architectures using Reinforcement Learning
We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task.
Deep Learning the City : Quantifying Urban Perception At A Global Scale
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 behavior and health of its residents.
Are Safer Looking Neighborhoods More Lively? A Multimodal Investigation into Urban Life
But in a world where the preference for safe looking neighborhoods is small, the connection between the perception of safety and liveliness will be either weak or nonexistent.
Computers and Society Social and Information Networks Physics and Society
Coreset-Based Adaptive Tracking
We propose a method for learning from streaming visual data using a compact, constant size representation of all the data that was seen until a given moment.
A Light Transport Model for Mitigating Multipath Interference in Time-of-Flight Sensors
Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics.
A Light Transport Model for Mitigating Multipath Interference in TOF Sensors
Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics.
