7 citations · 31 across the 12 of their papers we have counts for
12 papers
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
Mucong Ding, Tahseen Rabbani, Bang An +2
Graph Neural Networks (GNNs) are widely applied to graph learning problems such as node classification. When scaling up the underlying graphs of GNNs to a larger size, we are force…
Reviving Shift Equivariance in Vision Transformers
Peijian Ding, Davit Soselia, Thomas Armstrong +2
Shift equivariance is a fundamental principle that governs how we perceive the world - our recognition of an object remains invariant with respect to shifts. Transformers have gain…
Escaping From Saddle Points Using Asynchronous Coordinate Gradient Descent
Marco Bornstein, Jin-Peng Liu, Jingling Li +1
Large-scale non-convex optimization problems are expensive to solve due to computational and memory costs. To reduce the costs, first-order (computationally efficient) and asynchro…
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
Kaiwen Yang, Yanchao Sun, Jiahao Su +5
Data augmentation is a critical contributing factor to the success of deep learning but heavily relies on prior domain knowledge which is not always available. Recent works on auto…
An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication
Xiaofeng Xue, Haokun Mao, Qiong Li +1
Specializing Directed Acyclic Graph Federated Learning(SDAGFL) is a new federated learning framework which updates model from the devices with similar data distribution through Dir…
Datasets for Studying Generalization from Easy to Hard Examples
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta +5
We describe new datasets for studying generalization from easy to hard examples.
Guided Hyperparameter Tuning Through Visualization and Inference
Hyekang Joo, Calvin Bao, Ishan Sen +2
For deep learning practitioners, hyperparameter tuning for optimizing model performance can be a computationally expensive task. Though visualization can help practitioners relate…
Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets
Roozbeh Yousefzadeh, Furong Huang
Deep learning models extract, before a final classification layer, features or patterns which are key for their unprecedented advantageous performance. However, the process of comp…
Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers
Chen Zhu, Renkun Ni, Ping-yeh Chiang +3
Convex relaxations are effective for training and certifying neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical…
ARMA Nets: Expanding Receptive Field for Dense Prediction
Jiahao Su, Shiqi Wang, Furong Huang
Global information is essential for dense prediction problems, whose goal is to compute a discrete or continuous label for each pixel in the images. Traditional convolutional layer…
Non-negative Factorization of the Occurrence Tensor from Financial Contracts
Zheng Xu, Furong Huang, Louiqa Raschid +1
We propose an algorithm for the non-negative factorization of an occurrence tensor built from heterogeneous networks. We use l0 norm to model sparse errors over discrete values (oc…
Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model
Furong Huang, Animashree Anandkumar, Christian Borgs +6
Cataloging the neuronal cell types that comprise circuitry of individual brain regions is a major goal of modern neuroscience and the BRAIN initiative. Single-cell RNA sequencing c…