196 citations · 543 across the 22 of their papers we have counts for
8 papers · 1 filter
Graph Autoencoders with Deconvolutional Networks
Jia Li, Tomas Yu, Da-Cheng Juan +3
Recent studies have indicated that Graph Convolutional Networks (GCNs) act as a \emph{low pass} filter in spectral domain and encode smoothed node representations. In this paper, w…
Adversarial Robustness Across Representation Spaces
Pranjal Awasthi, George Yu, Chun-Sung Ferng +2
Adversarial robustness corresponds to the susceptibility of deep neural networks to imperceptible perturbations made at test time. In the context of image tasks, many algorithms ha…
HyperGrid: Efficient Multi-Task Transformers with Grid-wise Decomposable Hyper Projections
Yi Tay, Zhe Zhao, Dara Bahri +2
Achieving state-of-the-art performance on natural language understanding tasks typically relies on fine-tuning a fresh model for every task. Consequently, this approach leads to a…
Remix: Rebalanced Mixup
Hsin-Ping Chou, Shih-Chieh Chang, Jia-Yu Pan +2
Deep image classifiers often perform poorly when training data are heavily class-imbalanced. In this work, we propose a new regularization technique, Remix, that relaxes Mixup's fo…
Robust Processing-In-Memory Neural Networks via Noise-Aware Normalization
Li-Huang Tsai, Shih-Chieh Chang, Yu-Ting Chen +3
Analog computing hardwares, such as Processing-in-memory (PIM) accelerators, have gradually received more attention for accelerating the neural network computations. However, PIM a…
Synthesizer: Rethinking Self-Attention in Transformer Models
Yi Tay, Dara Bahri, Donald Metzler +3
The dot product self-attention is known to be central and indispensable to state-of-the-art Transformer models. But is it really required? This paper investigates the true importan…
Low-Dimensional Hyperbolic Knowledge Graph Embeddings
Ines Chami, Adva Wolf, Da-Cheng Juan +3
Knowledge graph (KG) embeddings learn low-dimensional representations of entities and relations to predict missing facts. KGs often exhibit hierarchical and logical patterns which…
Sparse Sinkhorn Attention
Yi Tay, Dara Bahri, Liu Yang +2
We propose Sparse Sinkhorn Attention, a new efficient and sparse method for learning to attend. Our method is based on differentiable sorting of internal representations. Concretel…