29 citations · 100 across the 11 of their papers we have counts for
11 papers
HyperCLIP: Adapting Vision-Language models with Hypernetworks
Victor Akinwande, Mohammad Sadegh Norouzzadeh, Devin Willmott +3
Self-supervised vision-language models trained with contrastive objectives form the basis of current state-of-the-art methods in AI vision tasks. The success of these models is a d…
Finetuning CLIP to Reason about Pairwise Differences
Dylan Sam, Devin Willmott, Joao D. Semedo +1
Vision-language models (VLMs) such as CLIP are trained via contrastive learning between text and image pairs, resulting in aligned image and text embeddings that are useful for man…
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning
Xidong Wu, Wan-Yi Lin, Devin Willmott +4
Federated Learning (FL) is a distributed training paradigm that enables clients scattered across the world to cooperatively learn a global model without divulging confidential data…
Understanding the Covariance Structure of Convolutional Filters
Asher Trockman, Devin Willmott, J. Zico Kolter
Neural network weights are typically initialized at random from univariate distributions, controlling just the variance of individual weights even in highly-structured operations l…
Orbital Mixer: Using Atomic Orbital Features for Basis Dependent Prediction of Molecular Wavefunctions
Kirill Shmilovich, Devin Willmott, Ivan Batalov +3
Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm o…
You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries
Devin Willmott, Anit Kumar Sahu, Fatemeh Sheikholeslami +2
Researchers have repeatedly shown that it is possible to craft adversarial attacks on deep classifiers (small perturbations that significantly change the class label), even in the…
A community-powered search of machine learning strategy space to find NMR property prediction models
Lars A. Bratholm, Will Gerrard, Brandon Anderson +26
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML st…
Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes
Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott +1
We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples for deep learning models solely based on information limited to output l…
Black-box Adversarial Attacks with Bayesian Optimization
Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott +1
We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output p…
Improving RNA secondary structure prediction via state inference with deep recurrent neural networks
Devin Willmott, David Murrugarra, Qiang Ye
The problem of determining which nucleotides of an RNA sequence are paired or unpaired in the secondary structure of an RNA, which we call RNA state inference, can be studied by di…
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Kyle Helfrich, Devin Willmott, Qiang Ye
Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients. Recent work on Unitary Recurrent Neural Networks (uRNNs) h…