2 citations · 3 across the 5 of their papers we have counts for
5 papers
Distributionally Robust Classification on a Data Budget
Benjamin Feuer, Ameya Joshi, Minh Pham +1
Real world uses of deep learning require predictable model behavior under distribution shifts. Models such as CLIP show emergent natural distributional robustness comparable to hum…
Circumventing Concept Erasure Methods For Text-to-Image Generative Models
Minh Pham, Kelly O. Marshall, Niv Cohen +2
Text-to-image generative models can produce photo-realistic images for an extremely broad range of concepts, and their usage has proliferated widely among the general public. On th…
ZeroForge: Feedforward Text-to-Shape Without 3D Supervision
Kelly O. Marshall, Minh Pham, Ameya Joshi +4
Current state-of-the-art methods for text-to-shape generation either require supervised training using a labeled dataset of pre-defined 3D shapes, or perform expensive inference-ti…
Transformer with Fourier Integral Attentions
Tan Nguyen, Minh Pham, Tam Nguyen +3
Multi-head attention empowers the recent success of transformers, the state-of-the-art models that have achieved remarkable success in sequence modeling and beyond. These attention…
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness
Ameya Joshi, Minh Pham, Minsu Cho +4
Randomized smoothing (RS) has been shown to be a fast, scalable technique for certifying the robustness of deep neural network classifiers. However, methods based on RS require aug…