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03/07/2023
Predicted Embedding Power Regression for Large-Scale Out-of-Distribution Detection
Out-of-distribution (OOD) inputs can compromise the performance and safe...
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06/03/2022
A Robust Backpropagation-Free Framework for Images
While current deep learning algorithms have been successful for a wide v...
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06/24/2021
Continual Competitive Memory: A Neural System for Online Task-Free Lifelong Learning
In this article, we propose a novel form of unsupervised learning, conti...
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06/20/2021
Improving Label Quality by Jointly Modeling Items and Annotators
We propose a fully Bayesian framework for learning ground truth labels f...
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11/20/2019
Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication
For lossy image compression, we develop a neural-based system which lear...
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09/26/2019
The Ant Swarm Neuro-Evolution Procedure for Optimizing Recurrent Networks
Hand-crafting effective and efficient structures for recurrent neural ne...
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09/20/2019
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution
Neuro-evolution and neural architecture search algorithms have gained in...
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09/09/2018
A Neural Temporal Model for Human Motion Prediction
We propose novel neural temporal models for short-term motion prediction...
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05/29/2018
Visually Grounded, Situated Learning in Neural Models
The theory of situated cognition postulates that language is inseparable...
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05/26/2018
Biologically Motivated Algorithms for Propagating Local Target Representations
Finding biologically plausible alternatives to back-propagation of error...
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03/15/2018
Learned Iterative Decoding for Lossy Image Compression Systems
For lossy image compression systems, we develop an algorithm called iter...
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03/05/2018