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20172023
most citedCombining Label Propagation and Simple Models Out-performs Graph Neural Networks

114 citations · 475 across the 42 of their papers we have counts for

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Showing 2021Show all

9 papers · 1 filter

cs.CV2021★ 4 cited

Rethinking Nearest Neighbors for Visual Classification

Menglin Jia, Bor-Chun Chen, Zuxuan Wu +3

Neural network classifiers have become the de-facto choice for current "pre-train then fine-tune" paradigms of visual classification. In this paper, we investigate k-Nearest-Neighb…

cs.LG2021★ 59 cited

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Derek Lim, Felix Hohne, Xiuyu Li +4

Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other. Recently, new Graph Neural Networ…

cs.CV2021★ 2 cited

A Frequency Perspective of Adversarial Robustness

Shishira R Maiya, Max Ehrlich, Vatsal Agarwal +3

Adversarial examples pose a unique challenge for deep learning systems. Despite recent advances in both attacks and defenses, there is still a lack of clarity and consensus in the…

cs.CV2021★ 3 cited

NeRV: Neural Representations for Videos

Hao Chen, Bo He, Hanyu Wang +3

We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Unlike conventional representations that treat videos as frame sequences, we rep…

cs.CV2021★ 16 cited

MixNorm: Test-Time Adaptation Through Online Normalization Estimation

Xuefeng Hu, Gokhan Uzunbas, Sirius Chen +4

We present a simple and effective way to estimate the batch-norm statistics during test time, to fast adapt a source model to target test samples. Known as Test-Time Adaptation, mo…

cs.CV2021

Robustness and Generalization via Generative Adversarial Training

Omid Poursaeed, Tianxing Jiang, Harry Yang +2

While deep neural networks have achieved remarkable success in various computer vision tasks, they often fail to generalize to new domains and subtle variations of input images. Se…

stat.ML2021

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…

cs.SI2021★ 5 cited

Edge Proposal Sets for Link Prediction

Abhay Singh, Qian Huang, Sijia Linda Huang +4

Graphs are a common model for complex relational data such as social networks and protein interactions, and such data can evolve over time (e.g., new friendships) and be noisy (e.g…

cs.CV2021★ 9 cited

Deep Video Inpainting Detection

Peng Zhou, Ning Yu, Zuxuan Wu +3

This paper studies video inpainting detection, which localizes an inpainted region in a video both spatially and temporally. In particular, we introduce VIDNet, Video Inpainting De…