11 citations · 22 across the 11 of their papers we have counts for
13 papers
Base Models Can Reason By Taking a Cue From Training Data
Sophie L. Wang, Amil Dravid, Rulin Shao +3
In this paper, we study how training data creates associations between the tokens at the start of a base model's response and the reasoning behavior that follows. First, we demonst…
Neuron Populations Exhibit Divergent Selectivity with Scale
Amil Dravid, Yasaman Bahri, Alexei A. Efros +1
We investigate whether neuron populations within neural networks evolve predictably with scale, extending scaling laws beyond macroscopic observables such as loss. To probe this qu…
Caption-Driven Explainability: Probing CNNs for Bias via CLIP
Patrick Koller, Amil V. Dravid, Guido M. Schuster +1
Robustness has become one of the most critical problems in machine learning (ML). The science of interpreting ML models to understand their behavior and improve their robustness is…
Vision Transformers Don't Need Trained Registers
Nick Jiang, Amil Dravid, Alexei Efros +1
We investigate the mechanism underlying a previously identified phenomenon in Vision Transformers - the emergence of high-norm tokens that lead to noisy attention maps (Darcet et a…
Interpreting the Weight Space of Customized Diffusion Models
Amil Dravid, Yossi Gandelsman, Kuan-Chieh Wang +4
We investigate the space of weights spanned by a large collection of customized diffusion models. We populate this space by creating a dataset of over 60,000 models, each of which…
Idempotent Generative Network
Assaf Shocher, Amil Dravid, Yossi Gandelsman +3
We propose a new approach for generative modeling based on training a neural network to be idempotent. An idempotent operator is one that can be applied sequentially without changi…
Rosetta Neurons: Mining the Common Units in a Model Zoo
Amil Dravid, Yossi Gandelsman, Alexei A. Efros +1
Do different neural networks, trained for various vision tasks, share some common representations? In this paper, we demonstrate the existence of common features we call "Rosetta N…
DeepCOVID-Fuse: A Multi-modality Deep Learning Model Fusing Chest X-Radiographs and Clinical Variables to Predict COVID-19 Risk Levels
Yunan Wu, Amil Dravid, Ramsey Michael Wehbe +1
Propose: To present DeepCOVID-Fuse, a deep learning fusion model to predict risk levels in patients with confirmed coronavirus disease 2019 (COVID-19) and to evaluate the performan…
BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos
Jennifer J. Sun, Lili Karashchuk, Amil Dravid +9
Quantifying motion in 3D is important for studying the behavior of humans and other animals, but manual pose annotations are expensive and time-consuming to obtain. Self-supervised…
medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space
Amil Dravid, Florian Schiffers, Boqing Gong +1
Despite the surge of deep learning in the past decade, some users are skeptical to deploy these models in practice due to their black-box nature. Specifically, in the medical space…
Investigating the Potential of Auxiliary-Classifier GANs for Image Classification in Low Data Regimes
Amil Dravid, Florian Schiffers, Yunan Wu +2
Generative Adversarial Networks (GANs) have shown promise in augmenting datasets and boosting convolutional neural networks' (CNN) performance on image classification tasks. But th…
Visual Explanations for Convolutional Neural Networks via Latent Traversal of Generative Adversarial Networks
Amil Dravid, Aggelos K. Katsaggelos
Lack of explainability in artificial intelligence, specifically deep neural networks, remains a bottleneck for implementing models in practice. Popular techniques such as Gradient-…
Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes
Emanuel A. Azcona, Pierre Besson, Yunan Wu +6
We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learn…