37 citations · 68 across the 11 of their papers we have counts for
13 papers
Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators
Zhengyang Su, Isay Katsman, Yueqi Wang +10
Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation. However, industrial recommender systems often benefit from restricting the output space to a c…
Revisiting the Necessity of Graph Learning and Common Graph Benchmarks
Isay Katsman, Ethan Lou, Anna Gilbert
Graph machine learning has enjoyed a meteoric rise in popularity since the introduction of deep learning in graph contexts. This is no surprise due to the ubiquity of graph data in…
Shedding Light on Problems with Hyperbolic Graph Learning
Isay Katsman, Anna Gilbert
Recent papers in the graph machine learning literature have introduced a number of approaches for hyperbolic representation learning. The asserted benefits are improved performance…
Riemannian Residual Neural Networks
Isay Katsman, Eric Ming Chen, Sidhanth Holalkere +4
Recent methods in geometric deep learning have introduced various neural networks to operate over data that lie on Riemannian manifolds. Such networks are often necessary to learn…
Equivariant Manifold Flows
Isay Katsman, Aaron Lou, Derek Lim +3
Tractably modelling distributions over manifolds has long been an important goal in the natural sciences. Recent work has focused on developing general machine learning models to l…
Neural Manifold Ordinary Differential Equations
Aaron Lou, Derek Lim, Isay Katsman +4
To better conform to data geometry, recent deep generative modelling techniques adapt Euclidean constructions to non-Euclidean spaces. In this paper, we study normalizing flows on…
Differentiating through the Fréchet Mean
Aaron Lou, Isay Katsman, Qingxuan Jiang +3
Recent advances in deep representation learning on Riemannian manifolds extend classical deep learning operations to better capture the geometry of the manifold. One possible exten…
Enhancing Adversarial Example Transferability with an Intermediate Level Attack
Qian Huang, Isay Katsman, Horace He +3
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models. Adversarial examples often exhibit black-box transfer, meaning that adversa…
Fashion++: Minimal Edits for Outfit Improvement
Wei-Lin Hsiao, Isay Katsman, Chao-Yuan Wu +2
Given an outfit, what small changes would most improve its fashionability? This question presents an intriguing new vision challenge. We introduce Fashion++, an approach that propo…
Adversarial Example Decomposition
Horace He, Aaron Lou, Qingxuan Jiang +3
Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations. Moreover, these adversarial perturbations often transfer acr…
Intermediate Level Adversarial Attack for Enhanced Transferability
Qian Huang, Zeqi Gu, Isay Katsman +5
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models. Adversarial examples often exhibit black-box transfer, meaning that adversa…
Semantic Segmentation with Scarce Data
Isay Katsman, Rohun Tripathi, Andreas Veit +1
Semantic segmentation is a challenging vision problem that usually necessitates the collection of large amounts of finely annotated data, which is often quite expensive to obtain.…
Generative Adversarial Perturbations
Omid Poursaeed, Isay Katsman, Bicheng Gao +1
In this paper, we propose novel generative models for creating adversarial examples, slightly perturbed images resembling natural images but maliciously crafted to fool pre-trained…