From the 1 of 140 linked papers with an AI index.
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A Spectral Regularizer for Unsupervised Disentanglement
Aditya Ramesh, Youngduck Choi, Yann LeCun
A generative model with a disentangled representation allows for independent control over different aspects of the output. Learning disentangled representations has been a recent t…
Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation
Xiang Zhang, Yann LeCun
This article proposes Adversarially-Trained Normalized Noisy-Feature Auto-Encoder (ATNNFAE) for byte-level text generation. An ATNNFAE consists of an auto-encoder where the interna…
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
Zhilin Yang, Jake Zhao, Bhuwan Dhingra +4
Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in lang…
Backpropagation for Implicit Spectral Densities
Aditya Ramesh, Yann LeCun
Most successful machine intelligence systems rely on gradient-based learning, which is made possible by backpropagation. Some systems are designed to aid us in interpreting data wh…
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli +2
Despite existing work on ensuring generalization of neural networks in terms of scale sensitive complexity measures, such as norms, margin and sharpness, these complexity measures…
DeSIGN: Design Inspiration from Generative Networks
Othman Sbai, Mohamed Elhoseiny, Antoine Bordes +2
Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant? To help answer this question, we design and investigate different image gene…