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07/04/2023
Unsupervised Feature Learning with Emergent Data-Driven Prototypicality
Given an image set without any labels, our goal is to train a model that...
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06/23/2023
Variance-Covariance Regularization Improves Representation Learning
Transfer learning has emerged as a key approach in the machine learning ...
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04/08/2023
EMP-SSL: Towards Self-Supervised Learning in One Training Epoch
Recently, self-supervised learning (SSL) has achieved tremendous success...
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10/30/2022
Unsupervised Learning of Structured Representations via Closed-Loop Transcription
This paper proposes an unsupervised method for learning a unified repres...
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10/18/2022
Simple Emergent Action Representations from Multi-Task Policy Training
Low-level sensory and motor signals in the high-dimensional spaces (e.g....
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09/30/2022
Minimalistic Unsupervised Learning with the Sparse Manifold Transform
We describe a minimalistic and interpretable method for unsupervised lea...
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09/29/2022
Joint Embedding Self-Supervised Learning in the Kernel Regime
The fundamental goal of self-supervised learning (SSL) is to produce use...
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06/17/2022
Intra-Instance VICReg: Bag of Self-Supervised Image Patch Embedding
Recently, self-supervised learning (SSL) has achieved tremendous empiric...
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06/03/2022
On the duality between contrastive and non-contrastive self-supervised learning
Recent approaches in self-supervised learning of image representations c...
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01/24/2022
Neural Manifold Clustering and Embedding
Given a union of non-linear manifolds, non-linear subspace clustering or...
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10/13/2021
Decoupled Contrastive Learning
Contrastive learning (CL) is one of the most successful paradigms for se...
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07/23/2021
Free Hyperbolic Neural Networks with Limited Radii
Non-Euclidean geometry with constant negative curvature, i.e., hyperboli...
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07/15/2021
Recurrent Parameter Generators
We present a generic method for recurrently using the same parameters fo...
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03/29/2021
Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors
Transformer networks have revolutionized NLP representation learning sin...
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10/07/2020
A Neural Network MCMC sampler that maximizes Proposal Entropy
Markov Chain Monte Carlo (MCMC) methods sample from unnormalized probabi...
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09/30/2020
RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
Flow-based generative models have become an important class of unsupervi...
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06/17/2020
3D Shape Reconstruction from Free-Hand Sketches
Sketches are the most abstract 2D representations of real-world objects....
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11/27/2019
Orthogonal Convolutional Neural Networks
The instability and feature redundancy in CNNs hinders further performan...
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10/17/2019
Annealed Denoising Score Matching: Learning Energy-Based Models in High-Dimensional Spaces
Energy-Based Models (EBMs) outputs unmormalized log-probability values g...
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10/09/2019
Word Embedding Visualization Via Dictionary Learning
Co-occurrence statistics based word embedding techniques have proved to ...
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02/14/2019
Superposition of many models into one
We present a method for storing multiple models within a single set of p...
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06/23/2018