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Representation Learning

4,925 papers

Papers per year

7
51
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487
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Papers

Comparing Supervised Models and Learned Speech Representations for Classifying Intelligibility of Disordered Speech on Selected Phrases INTERSPEECH 2021
V-statistics and Variance Estimation JMLR 2021
Nuc2Vec: Learning Representations of Nuclei in Histopathology Images with Contrastive Loss MIDL 2021
DeepBlueAI at TextGraphs 2021 Shared Task: Treating Multi-Hop Inference Explanation Regeneration as A Ranking Problem NAACL 2021
Style Transfer by Rigid Alignment in Neural Net Feature Space WACV 2021
Compositional Learning of Image-Text Query for Image Retrieval WACV 2021
Structured Visual Search via Composition-Aware Learning WACV 2021
Learning to Generate Dense Point Clouds With Textures on Multiple Categories WACV 2021
Few-Shot Font Style Transfer Between Different Languages WACV 2021
Deep reconstruction of strange attractors from time series NIPS 2020
Low Distortion Block-Resampling with Spatially Stochastic Networks NIPS 2020
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning NIPS 2020
Probabilistic Orientation Estimation with Matrix Fisher Distributions NIPS 2020
Feature Importance Ranking for Deep Learning NIPS 2020
Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning NIPS 2020
Language Through a Prism: A Spectral Approach for Multiscale Language Representations NIPS 2020
Latent World Models For Intrinsically Motivated Exploration NIPS 2020
Learning of Discrete Graphical Models with Neural Networks NIPS 2020
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views NIPS 2020
Sparse Symplectically Integrated Neural Networks NIPS 2020
Benchmarking Deep Learning Interpretability in Time Series Predictions NIPS 2020
Exchangeable Neural ODE for Set Modeling NIPS 2020
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection NIPS 2020
Training Linear Finite-State Machines NIPS 2020
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks NIPS 2020