Search Results for author:
Found 15 papers, 7 papers with code
URLOST: Unsupervised Representation Learning without Stationarity or Topology
Unsupervised representation learning has seen tremendous progress but is constrained by its reliance on data modality-specific stationarity and topology, a limitation not found in biological intelligence systems.
Learning Internal Representations of 3D Transformations from 2D Projected Inputs
When interacting in a three dimensional world, humans must estimate 3D structure from visual inputs projected down to two dimensional retinal images.
PIM: Video Coding using Perceptual Importance Maps
The contributions of this work are threefold: (1) we introduce a web-tool which allows scalable collection of fine-grained perceptual importance, by having users interactively paint spatio-temporal maps over encoded videos; (2) we use this tool to collect a dataset with 178 videos with a total of 14443 frames of human annotated spatio-temporal importance maps over the videos; and (3) we use our curated dataset to train a lightweight machine learning model which can predict these spatio-temporal importance regions.
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Neuroscience has long been an essential driver of progress in artificial intelligence (AI).
Minimalistic Unsupervised Learning with the Sparse Manifold Transform
Though there remains a small performance gap between our simple constructive model and SOTA methods, the evidence points to this as a promising direction for achieving a principled and white-box approach to unsupervised learning.
Ranked #1 on
Unsupervised MNIST
on MNIST
Self-Supervised Learning
Sparse Representation-based Classification
+3
Bispectral Neural Networks
We present a neural network architecture, Bispectral Neural Networks (BNNs) for learning representations that are invariant to the actions of compact commutative groups on the space over which a signal is defined.
An Interactive Annotation Tool for Perceptual Video Compression
We use this tool to collect data in-the-wild (10 videos, 17 users) and utilize the obtained importance maps in the context of x264 coding to demonstrate that the tool can indeed be used to generate videos which, at the same bitrate, look perceptually better through a subjective study - and are 1. 9 times more likely to be preferred by viewers.
Disentangling images with Lie group transformations and sparse coding
Discrete spatial patterns and their continuous transformations are two important regularities contained in natural signals.
RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG-Flow, which can separate information at different scales of images and extract disentangled representations at each scale.
Tent: Fully Test-time Adaptation by Entropy Minimization
A model must adapt itself to generalize to new and different data during testing.
Dynamic Scale Inference by Entropy Minimization
Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field.
Auditory Separation of a Conversation from Background via Attentional Gating
Our Attentional Gating Network (AGN) uses a variable attentional context to specify which speakers in the mixture are of interest.
Superposition of many models into one
We present a method for storing multiple models within a single set of parameters.
Generalization Challenges for Neural Architectures in Audio Source Separation
Recent work has shown that recurrent neural networks can be trained to separate individual speakers in a sound mixture with high fidelity.
Emergence of foveal image sampling from learning to attend in visual scenes
We describe a neural attention model with a learnable retinal sampling lattice.
