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ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, Stefan Lee
Neural Information Processing Systems http://nips.cc/
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, Jianmin Wang
Neural Information Processing Systems http://nips.cc/
Testing for Families of Distributions via the Fourier Transform
Alistair Stewart, Ilias Diakonikolas, Clement Canonne
Neural Information Processing Systems http://nips.cc/
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems
Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi
Such learning problems are formulated as latent or generative model learning assuming that observations were emerged from the low-dimensional latent states, which includes an intractable posterior inference of latent states for given input data.
Transfer Learning via Minimizing the Performance Gap Between Domains
Boyu Wang, Jorge Mendez, Mingbo Cai, Eric Eaton
To address this issue, we present the first analysis for instance weighting transfer learning that considers the presence of labeled target examples. The contribution of our work is two-fold.1. We address the question ofhow to measure the divergence between two domains given label informationforthetargetdomain.
Learning Mask-aware CLIP Representations for Zero-Shot Segmentation Siyu Jiao 1,2,3, Y unchao Wei 1,2,3, Y aowei Wang
Recently, pre-trained vision-language models have been increasingly used to tackle the challenging zero-shot segmentation task.
Input Similarity from the Neural Network Perspective
Guillaume Charpiat, Nicolas Girard, Loris Felardos, Yuliya Tarabalka
Neural Information Processing Systems http://nips.cc/
Frequency-Domain Dynamic Pruning for Convolutional Neural Networks
Zhenhua Liu, Jizheng Xu, Xiulian Peng, Ruiqin Xiong
Neural Information Processing Systems http://nips.cc/