18 citations · 76 across the 12 of their papers we have counts for
12 papers
PriViT: Vision Transformers for Fast Private Inference
Naren Dhyani, Jianqiao Mo, Minsu Cho +4
The Vision Transformer (ViT) architecture has emerged as the backbone of choice for state-of-the-art deep models for computer vision applications. However, ViTs are ill-suited for…
Generalizable Implicit Neural Representations via Instance Pattern Composers
Chiheon Kim, Doyup Lee, Saehoon Kim +2
Despite recent advances in implicit neural representations (INRs), it remains challenging for a coordinate-based multi-layer perceptron (MLP) of INRs to learn a common representati…
Revisiting Self-Distillation
Minh Pham, Minsu Cho, Ameya Joshi +1
Knowledge distillation is the procedure of transferring "knowledge" from a large model (the teacher) to a more compact one (the student), often being used in the context of model c…
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…
Autoregressive Image Generation using Residual Quantization
Doyup Lee, Chiheon Kim, Saehoon Kim +2
For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ) represents an image as a sequence of discrete codes. A short sequence length is important for a…
Selective Network Linearization for Efficient Private Inference
Minsu Cho, Ameya Joshi, Siddharth Garg +2
Private inference (PI) enables inference directly on cryptographically secure data.While promising to address many privacy issues, it has seen limited use due to extreme runtimes.…