Attribution Preservation in Network Compression for Reliable Network Interpretation

NeurIPS 2020 Attribution Preservation in Network Compression for Reliable Network Interpretation Geondo Park, June Yong Yang, Sung Ju Hwang, Eunho Yang  Neural networks embedded in safety-sensitive applications such as self-driving cars and wearable health monitors rely...

Adversarial Self-Supervised Contrastive Learning

NeurIPS 2020 Adversarial Self-Supervised Contrastive Learning Minseon Kim, Jihoon Tack, Sung Ju Hwang  Existing adversarial learning approaches mostly use class labels to generate adversarial samples that lead to incorrect predictions, which are then used to augment the training ...

Towards Deep Amortized Clustering

NeurIPS 2019 Workshop on Sets & Partitions Towards Deep Amortized ClusteringJuho Lee, Yoonho Lee, Yee Whye Teh We tackle amortized clustering, the problem of learning a neural network that can cluster a new dataset with only a few forward passes. We propose a novel learning framework f...

Graph Embedding VAE: A Permutation Invariant Model of Graph Structure

NeurIPS 2019 Workshop on Graph Representation Learning Graph Embedding VAE: A Permutation Invariant Model of Graph StructureTony Duan and Juho Lee Generative models of graph structure have applications in biology and social sciences. The state of the art is GraphRNN, which decomposes ...

Deep Gaussian Processes for Weakly Supervised Learning: Tumor Mutation Burden (TMB) Prediction

NeurIPS 2019 Workshop on Bayesian Deep Learning  Deep Gaussian Processes for Weakly Supervised Learning: Tumor Mutation Burden (TMB) Prediction  Sunho Park, Hongming Xu, Tae Hyun Hwang, Saehoon Kim  Tumor mutation burden (TMB) is a quantitative measurement of ...

Uncertainty-Aware Attention for Reliable Interpretation and Prediction

NeurIPS 2018 Uncertainty-Aware Attention for ReliableInterpretation and PredictionJay Heo, Haebeom Lee, Saehoon Kim, Juho Lee, Kwangjun Kim, Eunho Yang, Sung Ju Hwang Attention mechanism is effective in both focusing the deep learning models on relevant features and interpreting them. Howe...

TAEML: Task-Adaptive Ensemble of Meta-Learners

NeurIPS 2018 Workshop on Metalearning TAEML: Task-Adaptive Ensemble of Meta-LearnersMinseop Park, Saehoon Kim, Jungtaek Kim, Yanbin Liu, Seungjin Choi Most of meta-learning methods assume that a set of tasks in the meta-training phase is sampled from a single dataset. Thus when a new task ...

Stochastic Chebyshev Gradient Descent for Spectral Optimization

NeurIPS 2018 Stochastic Chebyshev Gradient Descent for Spectral OptimizationInsu Han, Haim Avron, Jinwoo Shin A large class of machine learning techniques requires the solution of optimization problems involving spectral functions of parametric matrices, e.g. log-determinant and nucle...

Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning

NeurIPS 2018 Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot LearningIYunlong Yu, Zhong Ji, Yanwei Fu, Jichang Guo, Yanwei Pang, Zhongfei Zhang Zero-Shot Learning (ZSL) is achieved via aligning the semantic relationships between the global image feature vector ...

Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding

NeurIPS 2018 Joint Active Feature Acquisition and Classification with Variable-Size Set EncodingHajin Shim, Sung Ju Hwang, Eunho Yang We consider the problem of active feature acquisition, where we sequentially select the subset of features in order to achieve the maximum predict...